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+- translations/zh/3-Web-App/README.md | 2 +- translations/zh/4-Classification/README.md | 2 +- translations/zh/5-Clustering/README.md | 2 +- translations/zh/6-NLP/README.md | 2 +- translations/zh/7-TimeSeries/README.md | 2 +- translations/zh/8-Reinforcement/README.md | 2 +- translations/zh/9-Real-World/README.md | 2 +- translations/zh/README.md | 4 +- 391 files changed, 2166 insertions(+), 3051 deletions(-) delete mode 100644 translated_images/es/ml-for-beginners-video-banner.279f2a268d213075.webp delete mode 100644 translated_images/es/ml-for-beginners.7b65fdd1f4f41598.webp delete mode 100644 translated_images/fr/ml-for-beginners-video-banner.279f2a268d213075.webp delete mode 100644 translated_images/fr/ml-for-beginners.7b65fdd1f4f41598.webp create mode 100644 translations/en/.co-op-translator.json create mode 100644 translations/es/.co-op-translator.json create mode 100644 translations/fr/.co-op-translator.json diff --git a/README.md b/README.md index 990b60ffb..cf324953b 100644 --- a/README.md +++ b/README.md @@ -13,7 +13,7 @@ #### Supported via GitHub Action (Automated & Always Up-to-Date) -[Arabic](./translations/ar/README.md) | [Bengali](./translations/bn/README.md) | [Bulgarian](./translations/bg/README.md) | [Burmese (Myanmar)](./translations/my/README.md) | [Chinese (Simplified)](./translations/zh/README.md) | [Chinese (Traditional, Hong Kong)](./translations/hk/README.md) | [Chinese (Traditional, Macau)](./translations/mo/README.md) | [Chinese (Traditional, Taiwan)](./translations/tw/README.md) | [Croatian](./translations/hr/README.md) | [Czech](./translations/cs/README.md) | [Danish](./translations/da/README.md) | [Dutch](./translations/nl/README.md) | [Estonian](./translations/et/README.md) | [Finnish](./translations/fi/README.md) | [French](./translations/fr/README.md) | [German](./translations/de/README.md) | [Greek](./translations/el/README.md) | [Hebrew](./translations/he/README.md) | [Hindi](./translations/hi/README.md) | [Hungarian](./translations/hu/README.md) | [Indonesian](./translations/id/README.md) | [Italian](./translations/it/README.md) | [Japanese](./translations/ja/README.md) | [Kannada](./translations/kn/README.md) | [Korean](./translations/ko/README.md) | [Lithuanian](./translations/lt/README.md) | [Malay](./translations/ms/README.md) | [Malayalam](./translations/ml/README.md) | [Marathi](./translations/mr/README.md) | [Nepali](./translations/ne/README.md) | [Nigerian Pidgin](./translations/pcm/README.md) | [Norwegian](./translations/no/README.md) | [Persian (Farsi)](./translations/fa/README.md) | [Polish](./translations/pl/README.md) | [Portuguese (Brazil)](./translations/br/README.md) | [Portuguese (Portugal)](./translations/pt/README.md) | [Punjabi (Gurmukhi)](./translations/pa/README.md) | [Romanian](./translations/ro/README.md) | [Russian](./translations/ru/README.md) | [Serbian (Cyrillic)](./translations/sr/README.md) | [Slovak](./translations/sk/README.md) | [Slovenian](./translations/sl/README.md) | [Spanish](./translations/es/README.md) | [Swahili](./translations/sw/README.md) | [Swedish](./translations/sv/README.md) | [Tagalog (Filipino)](./translations/tl/README.md) | [Tamil](./translations/ta/README.md) | [Telugu](./translations/te/README.md) | [Thai](./translations/th/README.md) | [Turkish](./translations/tr/README.md) | [Ukrainian](./translations/uk/README.md) | [Urdu](./translations/ur/README.md) | [Vietnamese](./translations/vi/README.md) +[Arabic](./translations/ar/README.md) | [Bengali](./translations/bn/README.md) | [Bulgarian](./translations/bg/README.md) | [Burmese (Myanmar)](./translations/my/README.md) | [Chinese (Simplified)](./translations/zh-CN/README.md) | [Chinese (Traditional, Hong Kong)](./translations/zh-HK/README.md) | [Chinese (Traditional, Macau)](./translations/zh-MO/README.md) | [Chinese (Traditional, Taiwan)](./translations/zh-TW/README.md) | [Croatian](./translations/hr/README.md) | [Czech](./translations/cs/README.md) | [Danish](./translations/da/README.md) | [Dutch](./translations/nl/README.md) | [Estonian](./translations/et/README.md) | [Finnish](./translations/fi/README.md) | [French](./translations/fr/README.md) | [German](./translations/de/README.md) | [Greek](./translations/el/README.md) | [Hebrew](./translations/he/README.md) | [Hindi](./translations/hi/README.md) | [Hungarian](./translations/hu/README.md) | [Indonesian](./translations/id/README.md) | [Italian](./translations/it/README.md) | [Japanese](./translations/ja/README.md) | [Kannada](./translations/kn/README.md) | [Korean](./translations/ko/README.md) | [Lithuanian](./translations/lt/README.md) | [Malay](./translations/ms/README.md) | [Malayalam](./translations/ml/README.md) | [Marathi](./translations/mr/README.md) | [Nepali](./translations/ne/README.md) | [Nigerian Pidgin](./translations/pcm/README.md) | [Norwegian](./translations/no/README.md) | [Persian (Farsi)](./translations/fa/README.md) | [Polish](./translations/pl/README.md) | [Portuguese (Brazil)](./translations/pt-BR/README.md) | [Portuguese (Portugal)](./translations/pt-PT/README.md) | [Punjabi (Gurmukhi)](./translations/pa/README.md) | [Romanian](./translations/ro/README.md) | [Russian](./translations/ru/README.md) | [Serbian (Cyrillic)](./translations/sr/README.md) | [Slovak](./translations/sk/README.md) | [Slovenian](./translations/sl/README.md) | [Spanish](./translations/es/README.md) | [Swahili](./translations/sw/README.md) | [Swedish](./translations/sv/README.md) | [Tagalog (Filipino)](./translations/tl/README.md) | [Tamil](./translations/ta/README.md) | [Telugu](./translations/te/README.md) | [Thai](./translations/th/README.md) | [Turkish](./translations/tr/README.md) | [Ukrainian](./translations/uk/README.md) | [Urdu](./translations/ur/README.md) | [Vietnamese](./translations/vi/README.md) > **Prefer to Clone Locally?** diff --git a/translated_images/es/ml-for-beginners-video-banner.279f2a268d213075.webp 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a/translations/br/1-Introduction/README.md +++ b/translations/br/1-Introduction/README.md @@ -11,7 +11,7 @@ CO_OP_TRANSLATOR_METADATA: Nesta seção do currículo, você será apresentado aos conceitos básicos que fundamentam o campo do aprendizado de máquina, o que ele é, e aprenderá sobre sua história e as técnicas que os pesquisadores utilizam para trabalhar com ele. Vamos explorar juntos este novo mundo do aprendizado de máquina! -![globo](../../../translated_images/br/globe.59f26379ceb40428.webp) +![globo](../../../translated_images/pt-BR/globe.59f26379ceb40428.webp) > Foto por Bill Oxford no Unsplash ### Aulas diff --git a/translations/br/2-Regression/1-Tools/solution/R/lesson_1-R.ipynb b/translations/br/2-Regression/1-Tools/solution/R/lesson_1-R.ipynb index 3f7fa9859..b26aecbdf 100644 --- a/translations/br/2-Regression/1-Tools/solution/R/lesson_1-R.ipynb +++ b/translations/br/2-Regression/1-Tools/solution/R/lesson_1-R.ipynb @@ -48,7 +48,7 @@ " width=\"630\"/>\n", "
Arte por @allison_horst
\n", "\n", - "\n" + "\n" ], "metadata": { "id": "LWNNzfqd6feZ" diff --git a/translations/br/2-Regression/2-Data/solution/R/lesson_2-R.ipynb b/translations/br/2-Regression/2-Data/solution/R/lesson_2-R.ipynb index cf706199e..a91027f55 100644 --- a/translations/br/2-Regression/2-Data/solution/R/lesson_2-R.ipynb +++ b/translations/br/2-Regression/2-Data/solution/R/lesson_2-R.ipynb @@ -227,7 +227,7 @@ "
Arte por @allison_horst
\n", "\n", "\n", - "\n" + "\n" ], "metadata": { "id": "o4jLY5-VZO2C" @@ -531,7 +531,7 @@ "
Infográfico por Dasani Madipalli
\n", "\n", "\n", - "\n", + "\n", "\n", "Existe um *sábio* ditado que diz o seguinte:\n", "\n", diff --git a/translations/br/2-Regression/3-Linear/solution/R/lesson_3-R.ipynb b/translations/br/2-Regression/3-Linear/solution/R/lesson_3-R.ipynb index 0c5b3261c..01d25ab1a 100644 --- a/translations/br/2-Regression/3-Linear/solution/R/lesson_3-R.ipynb +++ b/translations/br/2-Regression/3-Linear/solution/R/lesson_3-R.ipynb @@ -130,7 +130,7 @@ ">\n", "> Em outras palavras, e referindo-se à pergunta original dos dados das abóboras: \"prever o preço de uma abóbora por alqueire por mês\", `X` se referiria ao preço e `Y` ao mês de venda.\n", ">\n", - "> ![](../../../../../../translated_images/br/calculation.989aa7822020d9d0ba9fc781f1ab5192f3421be86ebb88026528aef33c37b0d8.png)\n", + "> ![](../../../../../../translated_images/pt-BR/calculation.989aa7822020d9d0ba9fc781f1ab5192f3421be86ebb88026528aef33c37b0d8.png)\n", " Infográfico por Jen Looper\n", "> \n", "> Calcule o valor de Y. Se você está pagando cerca de \\$4, deve ser abril!\n", @@ -162,7 +162,7 @@ "
Arte por @allison_horst
\n", "\n", "\n", - "\n" + "\n" ], "metadata": { "id": "WdUKXk7Bs8-V" @@ -567,7 +567,7 @@ "
Infográfico por Dasani Madipalli
\n", "\n", "\n", - "\n" + "\n" ], "metadata": { "id": "YqXjLuWavNxW" @@ -808,7 +808,7 @@ "
Infográfico por Dasani Madipalli
\n", "\n", "\n", - "\n" + "\n" ], "metadata": { "id": "HOCqJXLTwtWI" diff --git a/translations/br/2-Regression/4-Logistic/solution/R/lesson_4-R.ipynb b/translations/br/2-Regression/4-Logistic/solution/R/lesson_4-R.ipynb index bfcb4e787..3604e2c91 100644 --- a/translations/br/2-Regression/4-Logistic/solution/R/lesson_4-R.ipynb +++ b/translations/br/2-Regression/4-Logistic/solution/R/lesson_4-R.ipynb @@ -6,7 +6,7 @@ "source": [ "## Construir um modelo de regressão logística - Aula 4\n", "\n", - "![Infográfico de regressão logística vs. regressão linear](../../../../../../translated_images/br/linear-vs-logistic.ba180bf95e7ee667.webp)\n", + "![Infográfico de regressão logística vs. regressão linear](../../../../../../translated_images/pt-BR/linear-vs-logistic.ba180bf95e7ee667.webp)\n", "\n", "#### **[Questionário pré-aula](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/15/)**\n", "\n", @@ -78,7 +78,7 @@ "\n", "A regressão logística não oferece os mesmos recursos que a regressão linear. A primeira fornece uma previsão sobre uma `categoria binária` (\"laranja ou não laranja\"), enquanto a segunda é capaz de prever `valores contínuos`, por exemplo, dado a origem de uma abóbora e o momento da colheita, *quanto o preço dela vai aumentar*.\n", "\n", - "![Infográfico por Dasani Madipalli](../../../../../../translated_images/br/pumpkin-classifier.562771f104ad5436.webp)\n", + "![Infográfico por Dasani Madipalli](../../../../../../translated_images/pt-BR/pumpkin-classifier.562771f104ad5436.webp)\n", "\n", "### Outras classificações\n", "\n", @@ -88,7 +88,7 @@ "\n", "- **Ordinal**, que envolve categorias ordenadas, útil se quisermos organizar nossos resultados de forma lógica, como nossas abóboras ordenadas por um número finito de tamanhos (mini,pequeno,médio,grande,xl,xxl).\n", "\n", - "![Regressão multinomial vs ordinal](../../../../../../translated_images/br/multinomial-vs-ordinal.36701b4850e37d86.webp)\n", + "![Regressão multinomial vs ordinal](../../../../../../translated_images/pt-BR/multinomial-vs-ordinal.36701b4850e37d86.webp)\n", "\n", "#### **As variáveis NÃO precisam ser correlacionadas**\n", "\n", diff --git a/translations/br/2-Regression/README.md b/translations/br/2-Regression/README.md index 5bccd587e..f783e746d 100644 --- a/translations/br/2-Regression/README.md +++ b/translations/br/2-Regression/README.md @@ -12,7 +12,7 @@ CO_OP_TRANSLATOR_METADATA: Na América do Norte, as abóboras são frequentemente esculpidas em rostos assustadores para o Halloween. Vamos descobrir mais sobre esses vegetais fascinantes! -![jack-o-lanterns](../../../translated_images/br/jack-o-lanterns.181c661a9212457d.webp) +![jack-o-lanterns](../../../translated_images/pt-BR/jack-o-lanterns.181c661a9212457d.webp) > Foto de Beth Teutschmann no Unsplash ## O que você vai aprender diff --git a/translations/br/3-Web-App/README.md b/translations/br/3-Web-App/README.md index 1757b539c..6e69e4b2d 100644 --- a/translations/br/3-Web-App/README.md +++ b/translations/br/3-Web-App/README.md @@ -11,7 +11,7 @@ CO_OP_TRANSLATOR_METADATA: Nesta seção do currículo, você será introduzido a um tópico aplicado de aprendizado de máquina: como salvar seu modelo Scikit-learn como um arquivo que pode ser usado para fazer previsões dentro de um aplicativo web. Depois que o modelo estiver salvo, você aprenderá como utilizá-lo em um aplicativo web construído com Flask. Primeiro, você criará um modelo usando alguns dados relacionados a avistamentos de OVNIs! Em seguida, você construirá um aplicativo web que permitirá inserir um número de segundos junto com valores de latitude e longitude para prever qual país relatou ter visto um OVNI. -![Estacionamento de OVNIs](../../../translated_images/br/ufo.9e787f5161da9d4d.webp) +![Estacionamento de OVNIs](../../../translated_images/pt-BR/ufo.9e787f5161da9d4d.webp) Foto por Michael Herren no Unsplash diff --git a/translations/br/4-Classification/1-Introduction/solution/R/lesson_10-R.ipynb b/translations/br/4-Classification/1-Introduction/solution/R/lesson_10-R.ipynb index abf4891c8..0b8b71f9f 100644 --- a/translations/br/4-Classification/1-Introduction/solution/R/lesson_10-R.ipynb +++ b/translations/br/4-Classification/1-Introduction/solution/R/lesson_10-R.ipynb @@ -44,7 +44,7 @@ "
Comemore as culinárias pan-asiáticas nestas lições! Imagem de Jen Looper
\n", "\n", "\n", - "\n", + "\n", "\n", "Classificação é uma forma de [aprendizado supervisionado](https://wikipedia.org/wiki/Supervised_learning) que tem muito em comum com técnicas de regressão. Na classificação, você treina um modelo para prever a qual `categoria` um item pertence. Se o aprendizado de máquina é sobre prever valores ou nomes para coisas usando conjuntos de dados, então a classificação geralmente se divide em dois grupos: *classificação binária* e *classificação multiclasses*.\n", "\n", diff --git a/translations/br/4-Classification/README.md b/translations/br/4-Classification/README.md index fa008deb4..a5e6bf6ce 100644 --- a/translations/br/4-Classification/README.md +++ b/translations/br/4-Classification/README.md @@ -13,7 +13,7 @@ CO_OP_TRANSLATOR_METADATA: Na Ásia e na Índia, as tradições culinárias são extremamente diversas e muito deliciosas! Vamos analisar dados sobre culinárias regionais para tentar entender seus ingredientes. -![Vendedor de comida tailandesa](../../../translated_images/br/thai-food.c47a7a7f9f05c218.webp) +![Vendedor de comida tailandesa](../../../translated_images/pt-BR/thai-food.c47a7a7f9f05c218.webp) > Foto por Lisheng Chang no Unsplash ## O que você vai aprender diff --git a/translations/br/5-Clustering/README.md b/translations/br/5-Clustering/README.md index 3b2012f1a..91f4e08c6 100644 --- a/translations/br/5-Clustering/README.md +++ b/translations/br/5-Clustering/README.md @@ -15,7 +15,7 @@ Clustering é uma tarefa de aprendizado de máquina que busca encontrar objetos O público diversificado da Nigéria tem gostos musicais igualmente variados. Usando dados extraídos do Spotify (inspirado por [este artigo](https://towardsdatascience.com/country-wise-visual-analysis-of-music-taste-using-spotify-api-seaborn-in-python-77f5b749b421)), vamos analisar algumas músicas populares na Nigéria. Este conjunto de dados inclui informações sobre a pontuação de 'dançabilidade', 'acousticness', volume, 'speechiness', popularidade e energia de várias músicas. Será interessante descobrir padrões nesses dados! -![Um toca-discos](../../../translated_images/br/turntable.f2b86b13c53302dc.webp) +![Um toca-discos](../../../translated_images/pt-BR/turntable.f2b86b13c53302dc.webp) > Foto por Marcela Laskoski no Unsplash diff --git a/translations/br/6-NLP/README.md b/translations/br/6-NLP/README.md index f7729dc48..361ade8ae 100644 --- a/translations/br/6-NLP/README.md +++ b/translations/br/6-NLP/README.md @@ -17,7 +17,7 @@ Nesta seção do currículo, você será introduzido a um dos usos mais difundid Nestas lições, aprenderemos os fundamentos do PLN construindo pequenos bots conversacionais para entender como o aprendizado de máquina ajuda a tornar essas conversas cada vez mais 'inteligentes'. Você viajará no tempo, conversando com Elizabeth Bennett e Mr. Darcy do clássico romance de Jane Austen, **Orgulho e Preconceito**, publicado em 1813. Depois, você aprofundará seu conhecimento aprendendo sobre análise de sentimentos por meio de avaliações de hotéis na Europa. -![Livro Orgulho e Preconceito e chá](../../../translated_images/br/p&p.279f1c49ecd88941.webp) +![Livro Orgulho e Preconceito e chá](../../../translated_images/pt-BR/p&p.279f1c49ecd88941.webp) > Foto por Elaine Howlin no Unsplash ## Lições diff --git a/translations/br/7-TimeSeries/README.md b/translations/br/7-TimeSeries/README.md index 57deae96b..f7171862c 100644 --- a/translations/br/7-TimeSeries/README.md +++ b/translations/br/7-TimeSeries/README.md @@ -17,7 +17,7 @@ Nestes dois módulos, você será introduzido à previsão de séries temporais, Nosso foco regional é o uso de eletricidade no mundo, um conjunto de dados interessante para aprender a prever o consumo futuro de energia com base em padrões de carga anteriores. Você verá como esse tipo de previsão pode ser extremamente útil em um ambiente empresarial. -![rede elétrica](../../../translated_images/br/electric-grid.0c21d5214db09ffa.webp) +![rede elétrica](../../../translated_images/pt-BR/electric-grid.0c21d5214db09ffa.webp) Foto de [Peddi Sai hrithik](https://unsplash.com/@shutter_log?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText) de torres elétricas em uma estrada em Rajasthan no [Unsplash](https://unsplash.com/s/photos/electric-india?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText) diff --git a/translations/br/8-Reinforcement/README.md b/translations/br/8-Reinforcement/README.md index de8c90a4b..3916ca5f1 100644 --- a/translations/br/8-Reinforcement/README.md +++ b/translations/br/8-Reinforcement/README.md @@ -13,7 +13,7 @@ O aprendizado por reforço, RL, é considerado um dos paradigmas básicos de apr Imagine que você tem um ambiente simulado, como o mercado de ações. O que acontece se você impuser uma determinada regulamentação? Isso terá um efeito positivo ou negativo? Se algo negativo acontecer, você precisa aceitar esse _reforço negativo_, aprender com ele e mudar de direção. Se o resultado for positivo, você precisa construir sobre esse _reforço positivo_. -![Pedro e o lobo](../../../translated_images/br/peter.779730f9ba3a8a8d.webp) +![Pedro e o lobo](../../../translated_images/pt-BR/peter.779730f9ba3a8a8d.webp) > Pedro e seus amigos precisam escapar do lobo faminto! Imagem por [Jen Looper](https://twitter.com/jenlooper) diff --git a/translations/br/9-Real-World/README.md b/translations/br/9-Real-World/README.md index cc155d186..e4c19ba9f 100644 --- a/translations/br/9-Real-World/README.md +++ b/translations/br/9-Real-World/README.md @@ -11,7 +11,7 @@ CO_OP_TRANSLATOR_METADATA: Nesta seção do currículo, você será apresentado a algumas aplicações reais do aprendizado de máquina clássico. Pesquisamos na internet para encontrar artigos e publicações sobre aplicações que utilizam essas estratégias, evitando ao máximo redes neurais, aprendizado profundo e IA. Descubra como o aprendizado de máquina é usado em sistemas empresariais, aplicações ecológicas, finanças, artes e cultura, entre outros. -![chess](../../../translated_images/br/chess.e704a268781bdad8.webp) +![chess](../../../translated_images/pt-BR/chess.e704a268781bdad8.webp) > Foto por Alexis Fauvet no Unsplash diff --git a/translations/br/README.md b/translations/br/README.md index d788c4dba..0640d7881 100644 --- a/translations/br/README.md +++ b/translations/br/README.md @@ -41,7 +41,7 @@ CO_OP_TRANSLATOR_METADATA: Estamos realizando uma série de aprendizado com IA no Discord, saiba mais e junte-se a nós em [Learn with AI Series](https://aka.ms/learnwithai/discord) de 18 a 30 de setembro de 2025. Você receberá dicas e truques para usar o GitHub Copilot para Ciência de Dados. -![Learn with AI series](../../../../translated_images/br/3.9b58fd8d6c373c20.webp) +![Learn with AI series](../../../../translated_images/pt-BR/3.9b58fd8d6c373c20.webp) # Machine Learning para Iniciantes - Um Currículo @@ -90,7 +90,7 @@ Siga estes passos: Algumas das lições estão disponíveis em vídeos curtos. Você pode encontrar todos eles incorporados nas lições, ou na [playlist ML for Beginners no canal Microsoft Developer do YouTube](https://aka.ms/ml-beginners-videos) clicando na imagem abaixo. -[![ML for beginners banner](../../../../translated_images/br/ml-for-beginners-video-banner.63f694a100034bc6.webp)](https://aka.ms/ml-beginners-videos) +[![ML for beginners banner](../../../../translated_images/pt-BR/ml-for-beginners-video-banner.63f694a100034bc6.webp)](https://aka.ms/ml-beginners-videos) --- diff --git a/translations/en/.co-op-translator.json b/translations/en/.co-op-translator.json new file mode 100644 index 000000000..3f9f6d5a4 --- /dev/null +++ b/translations/en/.co-op-translator.json @@ -0,0 +1,596 @@ +{ + "1-Introduction/1-intro-to-ML/README.md": { + "original_hash": "69389392fa6346e0dfa30f664b7b6fec", + "translation_date": "2025-09-06T10:54:05+00:00", + "source_file": "1-Introduction/1-intro-to-ML/README.md", + "language_code": "en" + }, + 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{ + "original_hash": "fba3b94d88bfb9b81369b869a1e9a20f", + "translation_date": "2025-09-06T10:58:18+00:00", + "source_file": "sketchnotes/LICENSE.md", + "language_code": "en" + }, + "sketchnotes/README.md": { + "original_hash": "a88d5918c1b9da69a40d917a0840c497", + "translation_date": "2025-09-06T10:57:50+00:00", + "source_file": "sketchnotes/README.md", + "language_code": "en" + } +} \ No newline at end of file diff --git a/translations/en/1-Introduction/1-intro-to-ML/README.md b/translations/en/1-Introduction/1-intro-to-ML/README.md index 16432c74d..a2b0662c3 100644 --- a/translations/en/1-Introduction/1-intro-to-ML/README.md +++ b/translations/en/1-Introduction/1-intro-to-ML/README.md @@ -1,12 +1,3 @@ - # Introduction to Machine Learning ## [Pre-lecture quiz](https://ff-quizzes.netlify.app/en/ml/) diff --git a/translations/en/1-Introduction/1-intro-to-ML/assignment.md b/translations/en/1-Introduction/1-intro-to-ML/assignment.md index b3b59ec86..c297ea751 100644 --- a/translations/en/1-Introduction/1-intro-to-ML/assignment.md +++ b/translations/en/1-Introduction/1-intro-to-ML/assignment.md @@ -1,12 +1,3 @@ - # Get Up and Running ## Instructions diff --git a/translations/en/1-Introduction/2-history-of-ML/README.md b/translations/en/1-Introduction/2-history-of-ML/README.md index 711c0a684..3b1d4581d 100644 --- a/translations/en/1-Introduction/2-history-of-ML/README.md +++ b/translations/en/1-Introduction/2-history-of-ML/README.md @@ -1,12 +1,3 @@ - # History of Machine Learning ![Summary of History of Machine Learning in a sketchnote](../../../../sketchnotes/ml-history.png) diff --git a/translations/en/1-Introduction/2-history-of-ML/assignment.md b/translations/en/1-Introduction/2-history-of-ML/assignment.md index 1246adf0b..1de5069f3 100644 --- a/translations/en/1-Introduction/2-history-of-ML/assignment.md +++ b/translations/en/1-Introduction/2-history-of-ML/assignment.md @@ -1,12 +1,3 @@ - # Create a timeline ## Instructions diff --git a/translations/en/1-Introduction/3-fairness/README.md b/translations/en/1-Introduction/3-fairness/README.md index 4dd489c65..e666e0950 100644 --- a/translations/en/1-Introduction/3-fairness/README.md +++ b/translations/en/1-Introduction/3-fairness/README.md @@ -1,12 +1,3 @@ - # Building Machine Learning solutions with responsible AI ![Summary of responsible AI in Machine Learning in a sketchnote](../../../../sketchnotes/ml-fairness.png) diff --git a/translations/en/1-Introduction/3-fairness/assignment.md b/translations/en/1-Introduction/3-fairness/assignment.md index 4b2541616..6e1ff7baa 100644 --- a/translations/en/1-Introduction/3-fairness/assignment.md +++ b/translations/en/1-Introduction/3-fairness/assignment.md @@ -1,12 +1,3 @@ - # Explore the Responsible AI Toolbox ## Instructions diff --git a/translations/en/1-Introduction/4-techniques-of-ML/README.md b/translations/en/1-Introduction/4-techniques-of-ML/README.md index deecaffce..c040b3f37 100644 --- a/translations/en/1-Introduction/4-techniques-of-ML/README.md +++ b/translations/en/1-Introduction/4-techniques-of-ML/README.md @@ -1,12 +1,3 @@ - # Techniques of Machine Learning The process of creating, using, and maintaining machine learning models and the data they rely on is quite different from many other development workflows. In this lesson, we will break down the process and outline the key techniques you need to understand. You will: diff --git a/translations/en/1-Introduction/4-techniques-of-ML/assignment.md b/translations/en/1-Introduction/4-techniques-of-ML/assignment.md index 47c03f245..cd997fab5 100644 --- a/translations/en/1-Introduction/4-techniques-of-ML/assignment.md +++ b/translations/en/1-Introduction/4-techniques-of-ML/assignment.md @@ -1,12 +1,3 @@ - # Interview a data scientist ## Instructions diff --git a/translations/en/1-Introduction/README.md b/translations/en/1-Introduction/README.md index 7d79d293d..2971940d6 100644 --- a/translations/en/1-Introduction/README.md +++ b/translations/en/1-Introduction/README.md @@ -1,12 +1,3 @@ - # Introduction to machine learning In this section of the curriculum, you will be introduced to the fundamental concepts behind the field of machine learning, what it entails, and learn about its history and the techniques researchers use to work with it. Let's dive into this exciting world of ML together! diff --git a/translations/en/2-Regression/1-Tools/README.md b/translations/en/2-Regression/1-Tools/README.md index 75c816e1b..cc38068fc 100644 --- a/translations/en/2-Regression/1-Tools/README.md +++ b/translations/en/2-Regression/1-Tools/README.md @@ -1,12 +1,3 @@ - # Get started with Python and Scikit-learn for regression models ![Summary of regressions in a sketchnote](../../../../sketchnotes/ml-regression.png) diff --git a/translations/en/2-Regression/1-Tools/assignment.md b/translations/en/2-Regression/1-Tools/assignment.md index 053b9f06d..46df8611d 100644 --- a/translations/en/2-Regression/1-Tools/assignment.md +++ b/translations/en/2-Regression/1-Tools/assignment.md @@ -1,12 +1,3 @@ - # Regression with Scikit-learn ## Instructions diff --git a/translations/en/2-Regression/1-Tools/solution/Julia/README.md b/translations/en/2-Regression/1-Tools/solution/Julia/README.md index 464d00b28..6bd6d687d 100644 --- a/translations/en/2-Regression/1-Tools/solution/Julia/README.md +++ b/translations/en/2-Regression/1-Tools/solution/Julia/README.md @@ -1,12 +1,3 @@ - --- diff --git a/translations/en/2-Regression/2-Data/README.md b/translations/en/2-Regression/2-Data/README.md index 5da05137d..1f2062d04 100644 --- a/translations/en/2-Regression/2-Data/README.md +++ b/translations/en/2-Regression/2-Data/README.md @@ -1,12 +1,3 @@ - # Build a regression model using Scikit-learn: prepare and visualize data ![Data visualization infographic](../../../../2-Regression/2-Data/images/data-visualization.png) diff --git a/translations/en/2-Regression/2-Data/assignment.md b/translations/en/2-Regression/2-Data/assignment.md index 8ac6352d0..ea07c38d9 100644 --- a/translations/en/2-Regression/2-Data/assignment.md +++ b/translations/en/2-Regression/2-Data/assignment.md @@ -1,12 +1,3 @@ - # Exploring Visualizations There are several different libraries available for data visualization. Use the Pumpkin data from this lesson to create some visualizations with matplotlib and seaborn in a sample notebook. Which libraries are easier to use? diff --git a/translations/en/2-Regression/2-Data/solution/Julia/README.md b/translations/en/2-Regression/2-Data/solution/Julia/README.md index 47db2a297..6bd6d687d 100644 --- a/translations/en/2-Regression/2-Data/solution/Julia/README.md +++ b/translations/en/2-Regression/2-Data/solution/Julia/README.md @@ -1,12 +1,3 @@ - --- diff --git a/translations/en/2-Regression/3-Linear/README.md b/translations/en/2-Regression/3-Linear/README.md index 1dd3ba824..3223bbadc 100644 --- a/translations/en/2-Regression/3-Linear/README.md +++ b/translations/en/2-Regression/3-Linear/README.md @@ -1,12 +1,3 @@ - # Build a regression model using Scikit-learn: regression four ways ![Linear vs polynomial regression infographic](../../../../2-Regression/3-Linear/images/linear-polynomial.png) @@ -115,11 +106,11 @@ Now that you understand the math behind linear regression, let's create a regres From the previous lesson, you've likely observed that the average price for different months looks like this: -Average price by month +Average price by month This suggests there might be some correlation, and we can attempt to train a linear regression model to predict the relationship between `Month` and `Price`, or between `DayOfYear` and `Price`. Here's the scatterplot showing the latter relationship: -Scatter plot of Price vs. Day of Year +Scatter plot of Price vs. Day of Year Let's check for correlation using the `corr` function: @@ -138,7 +129,7 @@ for i,var in enumerate(new_pumpkins['Variety'].unique()): ax = df.plot.scatter('DayOfYear','Price',ax=ax,c=colors[i],label=var) ``` -Scatter plot of Price vs. Day of Year +Scatter plot of Price vs. Day of Year Our investigation suggests that variety has a greater impact on price than the actual selling date. This can be visualized with a bar graph: @@ -146,7 +137,7 @@ Our investigation suggests that variety has a greater impact on price than the a new_pumpkins.groupby('Variety')['Price'].mean().plot(kind='bar') ``` -Bar graph of price vs variety +Bar graph of price vs variety Let's focus on one pumpkin variety, the 'pie type,' and examine the effect of the date on price: @@ -154,7 +145,7 @@ Let's focus on one pumpkin variety, the 'pie type,' and examine the effect of th pie_pumpkins = new_pumpkins[new_pumpkins['Variety']=='PIE TYPE'] pie_pumpkins.plot.scatter('DayOfYear','Price') ``` -Scatter plot of Price vs. Day of Year +Scatter plot of Price vs. Day of Year If we calculate the correlation between `Price` and `DayOfYear` using the `corr` function, we get approximately `-0.27`, indicating that training a predictive model is worthwhile. @@ -228,7 +219,7 @@ plt.scatter(X_test,y_test) plt.plot(X_test,pred) ``` -Linear regression +Linear regression ## Polynomial Regression @@ -257,7 +248,7 @@ Using `PolynomialFeatures(2)` means we will include all second-degree polynomial Pipelines can be used in the same way as the original `LinearRegression` object, meaning we can `fit` the pipeline and then use `predict` to get prediction results. Below is the graph showing test data and the approximation curve: -Polynomial regression +Polynomial regression Using Polynomial Regression, we can achieve slightly lower MSE and higher determination, but not significantly. We need to consider other features! @@ -275,7 +266,7 @@ Ideally, we want to predict prices for different pumpkin varieties using the sam Here’s how the average price depends on variety: -Average price by variety +Average price by variety To include variety in our model, we first need to convert it to numeric form, or **encode** it. There are several ways to do this: diff --git a/translations/en/2-Regression/3-Linear/assignment.md b/translations/en/2-Regression/3-Linear/assignment.md index 368d68790..5ad41fa86 100644 --- a/translations/en/2-Regression/3-Linear/assignment.md +++ b/translations/en/2-Regression/3-Linear/assignment.md @@ -1,12 +1,3 @@ - # Create a Regression Model ## Instructions diff --git a/translations/en/2-Regression/3-Linear/solution/Julia/README.md b/translations/en/2-Regression/3-Linear/solution/Julia/README.md index 99b0055e4..b030aaf79 100644 --- a/translations/en/2-Regression/3-Linear/solution/Julia/README.md +++ b/translations/en/2-Regression/3-Linear/solution/Julia/README.md @@ -1,12 +1,3 @@ - --- diff --git a/translations/en/2-Regression/4-Logistic/README.md b/translations/en/2-Regression/4-Logistic/README.md index 6806f2e14..ff7e84494 100644 --- a/translations/en/2-Regression/4-Logistic/README.md +++ b/translations/en/2-Regression/4-Logistic/README.md @@ -1,12 +1,3 @@ - # Logistic regression to predict categories ![Logistic vs. linear regression infographic](../../../../2-Regression/4-Logistic/images/linear-vs-logistic.png) diff --git a/translations/en/2-Regression/4-Logistic/assignment.md b/translations/en/2-Regression/4-Logistic/assignment.md index 58ad282a8..c3c0432d6 100644 --- a/translations/en/2-Regression/4-Logistic/assignment.md +++ b/translations/en/2-Regression/4-Logistic/assignment.md @@ -1,12 +1,3 @@ - # Retrying some Regression ## Instructions diff --git a/translations/en/2-Regression/4-Logistic/solution/Julia/README.md b/translations/en/2-Regression/4-Logistic/solution/Julia/README.md index cb7e38135..6bd6d687d 100644 --- a/translations/en/2-Regression/4-Logistic/solution/Julia/README.md +++ b/translations/en/2-Regression/4-Logistic/solution/Julia/README.md @@ -1,12 +1,3 @@ - --- diff --git a/translations/en/2-Regression/README.md b/translations/en/2-Regression/README.md index cf9d3d7c7..8f74d8fd4 100644 --- a/translations/en/2-Regression/README.md +++ b/translations/en/2-Regression/README.md @@ -1,12 +1,3 @@ - # Regression models for machine learning ## Regional topic: Regression models for pumpkin prices in North America 🎃 diff --git a/translations/en/3-Web-App/1-Web-App/README.md b/translations/en/3-Web-App/1-Web-App/README.md index 2708b4d8e..f8e6c839f 100644 --- a/translations/en/3-Web-App/1-Web-App/README.md +++ b/translations/en/3-Web-App/1-Web-App/README.md @@ -1,12 +1,3 @@ - # Build a Web App to use a ML Model In this lesson, you will train a machine learning model using a fascinating dataset: _UFO sightings over the past century_, sourced from NUFORC's database. diff --git a/translations/en/3-Web-App/1-Web-App/assignment.md b/translations/en/3-Web-App/1-Web-App/assignment.md index 8d7e57856..e8067a590 100644 --- a/translations/en/3-Web-App/1-Web-App/assignment.md +++ b/translations/en/3-Web-App/1-Web-App/assignment.md @@ -1,12 +1,3 @@ - # Try a different model ## Instructions diff --git a/translations/en/3-Web-App/README.md b/translations/en/3-Web-App/README.md index 398199147..3db3c98e5 100644 --- a/translations/en/3-Web-App/README.md +++ b/translations/en/3-Web-App/README.md @@ -1,12 +1,3 @@ - # Build a web app to use your ML model In this part of the curriculum, you'll explore a practical application of machine learning: how to save your Scikit-learn model as a file that can be used to make predictions in a web application. Once the model is saved, you'll learn how to integrate it into a web app built with Flask. You'll start by creating a model using data about UFO sightings! Then, you'll develop a web app that allows users to input a number of seconds along with latitude and longitude values to predict which country reported the UFO sighting. diff --git a/translations/en/4-Classification/1-Introduction/README.md b/translations/en/4-Classification/1-Introduction/README.md index 5b938ac6e..a934bd292 100644 --- a/translations/en/4-Classification/1-Introduction/README.md +++ b/translations/en/4-Classification/1-Introduction/README.md @@ -1,12 +1,3 @@ - # Introduction to classification In these four lessons, you will dive into one of the core areas of traditional machine learning: _classification_. We'll explore various classification algorithms using a dataset about the diverse and delicious cuisines of Asia and India. Get ready to whet your appetite! diff --git a/translations/en/4-Classification/1-Introduction/assignment.md b/translations/en/4-Classification/1-Introduction/assignment.md index c8be8bdeb..c6cfe6102 100644 --- a/translations/en/4-Classification/1-Introduction/assignment.md +++ b/translations/en/4-Classification/1-Introduction/assignment.md @@ -1,12 +1,3 @@ - # Explore classification methods ## Instructions diff --git a/translations/en/4-Classification/1-Introduction/solution/Julia/README.md b/translations/en/4-Classification/1-Introduction/solution/Julia/README.md index 6d378d787..6bd6d687d 100644 --- a/translations/en/4-Classification/1-Introduction/solution/Julia/README.md +++ b/translations/en/4-Classification/1-Introduction/solution/Julia/README.md @@ -1,12 +1,3 @@ - --- diff --git a/translations/en/4-Classification/2-Classifiers-1/README.md b/translations/en/4-Classification/2-Classifiers-1/README.md index ab06a9ef0..d514f9642 100644 --- a/translations/en/4-Classification/2-Classifiers-1/README.md +++ b/translations/en/4-Classification/2-Classifiers-1/README.md @@ -1,12 +1,3 @@ - # Cuisine classifiers 1 In this lesson, you will use the dataset you saved from the previous lesson, which contains balanced and clean data about cuisines. diff --git a/translations/en/4-Classification/2-Classifiers-1/assignment.md b/translations/en/4-Classification/2-Classifiers-1/assignment.md index 538e1697c..0abf103a1 100644 --- a/translations/en/4-Classification/2-Classifiers-1/assignment.md +++ b/translations/en/4-Classification/2-Classifiers-1/assignment.md @@ -1,12 +1,3 @@ - # Study the solvers ## Instructions diff --git a/translations/en/4-Classification/2-Classifiers-1/solution/Julia/README.md b/translations/en/4-Classification/2-Classifiers-1/solution/Julia/README.md index 98bc224f7..6bd6d687d 100644 --- a/translations/en/4-Classification/2-Classifiers-1/solution/Julia/README.md +++ b/translations/en/4-Classification/2-Classifiers-1/solution/Julia/README.md @@ -1,12 +1,3 @@ - --- diff --git a/translations/en/4-Classification/3-Classifiers-2/README.md b/translations/en/4-Classification/3-Classifiers-2/README.md index e75cef579..307afbafa 100644 --- a/translations/en/4-Classification/3-Classifiers-2/README.md +++ b/translations/en/4-Classification/3-Classifiers-2/README.md @@ -1,12 +1,3 @@ - # Cuisine classifiers 2 In this second classification lesson, you will explore additional methods for classifying numeric data. You will also learn about the implications of choosing one classifier over another. diff --git a/translations/en/4-Classification/3-Classifiers-2/assignment.md b/translations/en/4-Classification/3-Classifiers-2/assignment.md index fa2d01870..061985391 100644 --- a/translations/en/4-Classification/3-Classifiers-2/assignment.md +++ b/translations/en/4-Classification/3-Classifiers-2/assignment.md @@ -1,12 +1,3 @@ - # Parameter Play ## Instructions diff --git a/translations/en/4-Classification/3-Classifiers-2/solution/Julia/README.md b/translations/en/4-Classification/3-Classifiers-2/solution/Julia/README.md index 0ecd06fec..6bd6d687d 100644 --- a/translations/en/4-Classification/3-Classifiers-2/solution/Julia/README.md +++ b/translations/en/4-Classification/3-Classifiers-2/solution/Julia/README.md @@ -1,12 +1,3 @@ - --- diff --git a/translations/en/4-Classification/4-Applied/README.md b/translations/en/4-Classification/4-Applied/README.md index bad266a2b..739a05e26 100644 --- a/translations/en/4-Classification/4-Applied/README.md +++ b/translations/en/4-Classification/4-Applied/README.md @@ -1,12 +1,3 @@ - # Build a Cuisine Recommender Web App In this lesson, you will create a classification model using techniques learned in previous lessons and the delicious cuisine dataset used throughout this series. Additionally, you will develop a small web app to utilize a saved model, leveraging Onnx's web runtime. diff --git a/translations/en/4-Classification/4-Applied/assignment.md b/translations/en/4-Classification/4-Applied/assignment.md index eea0a3150..a4e76bb59 100644 --- a/translations/en/4-Classification/4-Applied/assignment.md +++ b/translations/en/4-Classification/4-Applied/assignment.md @@ -1,12 +1,3 @@ - # Build a recommender ## Instructions diff --git a/translations/en/4-Classification/README.md b/translations/en/4-Classification/README.md index 04f5f57c5..50a466199 100644 --- a/translations/en/4-Classification/README.md +++ b/translations/en/4-Classification/README.md @@ -1,12 +1,3 @@ - # Getting started with classification ## Regional topic: Delicious Asian and Indian Cuisines 🍜 diff --git a/translations/en/5-Clustering/1-Visualize/README.md b/translations/en/5-Clustering/1-Visualize/README.md index 7c9338513..bc63d978a 100644 --- a/translations/en/5-Clustering/1-Visualize/README.md +++ b/translations/en/5-Clustering/1-Visualize/README.md @@ -1,12 +1,3 @@ - # Introduction to clustering Clustering is a type of [Unsupervised Learning](https://wikipedia.org/wiki/Unsupervised_learning) that assumes a dataset is unlabelled or that its inputs are not paired with predefined outputs. It uses various algorithms to analyze unlabeled data and group it based on patterns identified within the data. diff --git a/translations/en/5-Clustering/1-Visualize/assignment.md b/translations/en/5-Clustering/1-Visualize/assignment.md index 5aad29cd3..cf30ea1d2 100644 --- a/translations/en/5-Clustering/1-Visualize/assignment.md +++ b/translations/en/5-Clustering/1-Visualize/assignment.md @@ -1,12 +1,3 @@ - # Research other visualizations for clustering ## Instructions diff --git a/translations/en/5-Clustering/1-Visualize/solution/Julia/README.md b/translations/en/5-Clustering/1-Visualize/solution/Julia/README.md index 0f879cee3..6bd6d687d 100644 --- a/translations/en/5-Clustering/1-Visualize/solution/Julia/README.md +++ b/translations/en/5-Clustering/1-Visualize/solution/Julia/README.md @@ -1,12 +1,3 @@ - --- diff --git a/translations/en/5-Clustering/2-K-Means/README.md b/translations/en/5-Clustering/2-K-Means/README.md index 6c949c44c..9417e8f1c 100644 --- a/translations/en/5-Clustering/2-K-Means/README.md +++ b/translations/en/5-Clustering/2-K-Means/README.md @@ -1,12 +1,3 @@ - # K-Means Clustering ## [Pre-lecture quiz](https://ff-quizzes.netlify.app/en/ml/) diff --git a/translations/en/5-Clustering/2-K-Means/assignment.md b/translations/en/5-Clustering/2-K-Means/assignment.md index bf84e62c5..1bd62d23b 100644 --- a/translations/en/5-Clustering/2-K-Means/assignment.md +++ b/translations/en/5-Clustering/2-K-Means/assignment.md @@ -1,12 +1,3 @@ - # Try different clustering methods ## Instructions diff --git a/translations/en/5-Clustering/2-K-Means/solution/Julia/README.md b/translations/en/5-Clustering/2-K-Means/solution/Julia/README.md index 81ffd75a8..6bd6d687d 100644 --- a/translations/en/5-Clustering/2-K-Means/solution/Julia/README.md +++ b/translations/en/5-Clustering/2-K-Means/solution/Julia/README.md @@ -1,12 +1,3 @@ - --- diff --git a/translations/en/5-Clustering/README.md b/translations/en/5-Clustering/README.md index f8aa229db..aa11d87ab 100644 --- a/translations/en/5-Clustering/README.md +++ b/translations/en/5-Clustering/README.md @@ -1,12 +1,3 @@ - # Clustering models for machine learning Clustering is a machine learning task that aims to identify objects that are similar to each other and group them into clusters. What sets clustering apart from other machine learning approaches is that it happens automatically. In fact, it’s fair to say it’s the opposite of supervised learning. diff --git a/translations/en/6-NLP/1-Introduction-to-NLP/README.md b/translations/en/6-NLP/1-Introduction-to-NLP/README.md index a236bdeaf..26ddaba88 100644 --- a/translations/en/6-NLP/1-Introduction-to-NLP/README.md +++ b/translations/en/6-NLP/1-Introduction-to-NLP/README.md @@ -1,12 +1,3 @@ - # Introduction to natural language processing This lesson provides a brief history and key concepts of *natural language processing*, a subfield of *computational linguistics*. diff --git a/translations/en/6-NLP/1-Introduction-to-NLP/assignment.md b/translations/en/6-NLP/1-Introduction-to-NLP/assignment.md index 4a4cc3c29..ad29af538 100644 --- a/translations/en/6-NLP/1-Introduction-to-NLP/assignment.md +++ b/translations/en/6-NLP/1-Introduction-to-NLP/assignment.md @@ -1,12 +1,3 @@ - # Search for a bot ## Instructions diff --git a/translations/en/6-NLP/2-Tasks/README.md b/translations/en/6-NLP/2-Tasks/README.md index a911e79ff..f7650b7a0 100644 --- a/translations/en/6-NLP/2-Tasks/README.md +++ b/translations/en/6-NLP/2-Tasks/README.md @@ -1,12 +1,3 @@ - # Common natural language processing tasks and techniques For most *natural language processing* tasks, the text to be processed must be broken down, analyzed, and the results stored or cross-referenced with rules and datasets. These tasks allow the programmer to derive the _meaning_, _intent_, or simply the _frequency_ of terms and words in a text. diff --git a/translations/en/6-NLP/2-Tasks/assignment.md b/translations/en/6-NLP/2-Tasks/assignment.md index fc57e30b1..a2c0e1421 100644 --- a/translations/en/6-NLP/2-Tasks/assignment.md +++ b/translations/en/6-NLP/2-Tasks/assignment.md @@ -1,12 +1,3 @@ - # Make a Bot talk back ## Instructions diff --git a/translations/en/6-NLP/3-Translation-Sentiment/README.md b/translations/en/6-NLP/3-Translation-Sentiment/README.md index e1bfc9c71..f962d087a 100644 --- a/translations/en/6-NLP/3-Translation-Sentiment/README.md +++ b/translations/en/6-NLP/3-Translation-Sentiment/README.md @@ -1,12 +1,3 @@ - # Translation and sentiment analysis with ML In the previous lessons, you learned how to create a basic bot using `TextBlob`, a library that incorporates machine learning behind the scenes to perform basic NLP tasks like extracting noun phrases. Another significant challenge in computational linguistics is accurately _translating_ a sentence from one spoken or written language to another. diff --git a/translations/en/6-NLP/3-Translation-Sentiment/assignment.md b/translations/en/6-NLP/3-Translation-Sentiment/assignment.md index 0585a122a..8cd2f156c 100644 --- a/translations/en/6-NLP/3-Translation-Sentiment/assignment.md +++ b/translations/en/6-NLP/3-Translation-Sentiment/assignment.md @@ -1,12 +1,3 @@ - # Poetic license ## Instructions diff --git a/translations/en/6-NLP/3-Translation-Sentiment/solution/Julia/README.md b/translations/en/6-NLP/3-Translation-Sentiment/solution/Julia/README.md index 51e9de850..6bd6d687d 100644 --- a/translations/en/6-NLP/3-Translation-Sentiment/solution/Julia/README.md +++ b/translations/en/6-NLP/3-Translation-Sentiment/solution/Julia/README.md @@ -1,12 +1,3 @@ - --- diff --git a/translations/en/6-NLP/3-Translation-Sentiment/solution/R/README.md b/translations/en/6-NLP/3-Translation-Sentiment/solution/R/README.md index 7851e0de5..a6fbf0d3d 100644 --- a/translations/en/6-NLP/3-Translation-Sentiment/solution/R/README.md +++ b/translations/en/6-NLP/3-Translation-Sentiment/solution/R/README.md @@ -1,12 +1,3 @@ - this is a temporary placeholder --- diff --git a/translations/en/6-NLP/4-Hotel-Reviews-1/README.md b/translations/en/6-NLP/4-Hotel-Reviews-1/README.md index cdb8f46a4..b22aad8c6 100644 --- a/translations/en/6-NLP/4-Hotel-Reviews-1/README.md +++ b/translations/en/6-NLP/4-Hotel-Reviews-1/README.md @@ -1,12 +1,3 @@ - # Sentiment analysis with hotel reviews - processing the data In this section, you'll apply techniques from previous lessons to perform exploratory data analysis on a large dataset. Once you understand the relevance of the various columns, you'll learn: diff --git a/translations/en/6-NLP/4-Hotel-Reviews-1/assignment.md b/translations/en/6-NLP/4-Hotel-Reviews-1/assignment.md index f9066b71f..7c053fa21 100644 --- a/translations/en/6-NLP/4-Hotel-Reviews-1/assignment.md +++ b/translations/en/6-NLP/4-Hotel-Reviews-1/assignment.md @@ -1,12 +1,3 @@ - # NLTK ## Instructions diff --git a/translations/en/6-NLP/4-Hotel-Reviews-1/solution/Julia/README.md b/translations/en/6-NLP/4-Hotel-Reviews-1/solution/Julia/README.md index 7c3b4b1c0..6bd6d687d 100644 --- a/translations/en/6-NLP/4-Hotel-Reviews-1/solution/Julia/README.md +++ b/translations/en/6-NLP/4-Hotel-Reviews-1/solution/Julia/README.md @@ -1,12 +1,3 @@ - --- diff --git a/translations/en/6-NLP/4-Hotel-Reviews-1/solution/R/README.md b/translations/en/6-NLP/4-Hotel-Reviews-1/solution/R/README.md index e298c4f11..f6c288b1f 100644 --- a/translations/en/6-NLP/4-Hotel-Reviews-1/solution/R/README.md +++ b/translations/en/6-NLP/4-Hotel-Reviews-1/solution/R/README.md @@ -1,12 +1,3 @@ - this is a temporary placeholder --- diff --git a/translations/en/6-NLP/5-Hotel-Reviews-2/README.md b/translations/en/6-NLP/5-Hotel-Reviews-2/README.md index 0881617be..20eadd18a 100644 --- a/translations/en/6-NLP/5-Hotel-Reviews-2/README.md +++ b/translations/en/6-NLP/5-Hotel-Reviews-2/README.md @@ -1,12 +1,3 @@ - # Sentiment analysis with hotel reviews Now that you've explored the dataset in detail, it's time to filter the columns and apply NLP techniques to gain new insights about the hotels. diff --git a/translations/en/6-NLP/5-Hotel-Reviews-2/assignment.md b/translations/en/6-NLP/5-Hotel-Reviews-2/assignment.md index bd4746258..12cbfc177 100644 --- a/translations/en/6-NLP/5-Hotel-Reviews-2/assignment.md +++ b/translations/en/6-NLP/5-Hotel-Reviews-2/assignment.md @@ -1,12 +1,3 @@ - # Try a different dataset ## Instructions diff --git a/translations/en/6-NLP/5-Hotel-Reviews-2/solution/Julia/README.md b/translations/en/6-NLP/5-Hotel-Reviews-2/solution/Julia/README.md index e856f04a2..6bd6d687d 100644 --- a/translations/en/6-NLP/5-Hotel-Reviews-2/solution/Julia/README.md +++ b/translations/en/6-NLP/5-Hotel-Reviews-2/solution/Julia/README.md @@ -1,12 +1,3 @@ - --- diff --git a/translations/en/6-NLP/5-Hotel-Reviews-2/solution/R/README.md b/translations/en/6-NLP/5-Hotel-Reviews-2/solution/R/README.md index 18a6c5f71..a6fbf0d3d 100644 --- a/translations/en/6-NLP/5-Hotel-Reviews-2/solution/R/README.md +++ b/translations/en/6-NLP/5-Hotel-Reviews-2/solution/R/README.md @@ -1,12 +1,3 @@ - this is a temporary placeholder --- diff --git a/translations/en/6-NLP/README.md b/translations/en/6-NLP/README.md index 2dc8e841b..f0d733b56 100644 --- a/translations/en/6-NLP/README.md +++ b/translations/en/6-NLP/README.md @@ -1,12 +1,3 @@ - # Getting started with natural language processing Natural language processing (NLP) is the ability of a computer program to understand human language as it is spoken and written—commonly referred to as natural language. It is a branch of artificial intelligence (AI). NLP has been around for over 50 years and has its origins in the field of linguistics. The entire field focuses on enabling machines to comprehend and process human language. This capability can then be applied to tasks such as spell checking or machine translation. NLP has numerous practical applications across various domains, including medical research, search engines, and business intelligence. diff --git a/translations/en/6-NLP/data/README.md b/translations/en/6-NLP/data/README.md index 73d506e75..03f9377b8 100644 --- a/translations/en/6-NLP/data/README.md +++ b/translations/en/6-NLP/data/README.md @@ -1,12 +1,3 @@ - Download the hotel review data to this folder. --- diff --git a/translations/en/7-TimeSeries/1-Introduction/README.md b/translations/en/7-TimeSeries/1-Introduction/README.md index 27465eadd..9c4784112 100644 --- a/translations/en/7-TimeSeries/1-Introduction/README.md +++ b/translations/en/7-TimeSeries/1-Introduction/README.md @@ -1,12 +1,3 @@ - # Introduction to time series forecasting ![Summary of time series in a sketchnote](../../../../sketchnotes/ml-timeseries.png) diff --git a/translations/en/7-TimeSeries/1-Introduction/assignment.md b/translations/en/7-TimeSeries/1-Introduction/assignment.md index 6a080df87..75cd458e5 100644 --- a/translations/en/7-TimeSeries/1-Introduction/assignment.md +++ b/translations/en/7-TimeSeries/1-Introduction/assignment.md @@ -1,12 +1,3 @@ - # Visualize some more Time Series ## Instructions diff --git a/translations/en/7-TimeSeries/1-Introduction/solution/Julia/README.md b/translations/en/7-TimeSeries/1-Introduction/solution/Julia/README.md index 5b32cb0f0..6bd6d687d 100644 --- a/translations/en/7-TimeSeries/1-Introduction/solution/Julia/README.md +++ b/translations/en/7-TimeSeries/1-Introduction/solution/Julia/README.md @@ -1,12 +1,3 @@ - --- diff --git a/translations/en/7-TimeSeries/1-Introduction/solution/R/README.md b/translations/en/7-TimeSeries/1-Introduction/solution/R/README.md index a7a6eac8d..a6fbf0d3d 100644 --- a/translations/en/7-TimeSeries/1-Introduction/solution/R/README.md +++ b/translations/en/7-TimeSeries/1-Introduction/solution/R/README.md @@ -1,12 +1,3 @@ - this is a temporary placeholder --- diff --git a/translations/en/7-TimeSeries/2-ARIMA/README.md b/translations/en/7-TimeSeries/2-ARIMA/README.md index 13acef560..0a0b8cf8e 100644 --- a/translations/en/7-TimeSeries/2-ARIMA/README.md +++ b/translations/en/7-TimeSeries/2-ARIMA/README.md @@ -1,12 +1,3 @@ - # Time series forecasting with ARIMA In the previous lesson, you explored time series forecasting and worked with a dataset showing variations in electrical load over time. diff --git a/translations/en/7-TimeSeries/2-ARIMA/assignment.md b/translations/en/7-TimeSeries/2-ARIMA/assignment.md index 85b78670c..ca02a97e5 100644 --- a/translations/en/7-TimeSeries/2-ARIMA/assignment.md +++ b/translations/en/7-TimeSeries/2-ARIMA/assignment.md @@ -1,12 +1,3 @@ - # A new ARIMA model ## Instructions diff --git a/translations/en/7-TimeSeries/2-ARIMA/solution/Julia/README.md b/translations/en/7-TimeSeries/2-ARIMA/solution/Julia/README.md index 557588a40..6bd6d687d 100644 --- a/translations/en/7-TimeSeries/2-ARIMA/solution/Julia/README.md +++ b/translations/en/7-TimeSeries/2-ARIMA/solution/Julia/README.md @@ -1,12 +1,3 @@ - --- diff --git a/translations/en/7-TimeSeries/2-ARIMA/solution/R/README.md b/translations/en/7-TimeSeries/2-ARIMA/solution/R/README.md index 80d1d3fba..a6fbf0d3d 100644 --- a/translations/en/7-TimeSeries/2-ARIMA/solution/R/README.md +++ b/translations/en/7-TimeSeries/2-ARIMA/solution/R/README.md @@ -1,12 +1,3 @@ - this is a temporary placeholder --- diff --git a/translations/en/7-TimeSeries/3-SVR/README.md b/translations/en/7-TimeSeries/3-SVR/README.md index 476ccf548..21fb8d096 100644 --- a/translations/en/7-TimeSeries/3-SVR/README.md +++ b/translations/en/7-TimeSeries/3-SVR/README.md @@ -1,12 +1,3 @@ - # Time Series Forecasting with Support Vector Regressor In the previous lesson, you learned how to use the ARIMA model for time series predictions. Now, you'll explore the Support Vector Regressor model, which is used to predict continuous data. diff --git a/translations/en/7-TimeSeries/3-SVR/assignment.md b/translations/en/7-TimeSeries/3-SVR/assignment.md index 3301a29d5..e8d90889e 100644 --- a/translations/en/7-TimeSeries/3-SVR/assignment.md +++ b/translations/en/7-TimeSeries/3-SVR/assignment.md @@ -1,12 +1,3 @@ - # A new SVR model ## Instructions [^1] diff --git a/translations/en/7-TimeSeries/README.md b/translations/en/7-TimeSeries/README.md index f10a77788..902c69106 100644 --- a/translations/en/7-TimeSeries/README.md +++ b/translations/en/7-TimeSeries/README.md @@ -1,12 +1,3 @@ - # Introduction to time series forecasting What is time series forecasting? It's the process of predicting future events by analyzing past trends. diff --git a/translations/en/8-Reinforcement/1-QLearning/README.md b/translations/en/8-Reinforcement/1-QLearning/README.md index d8e705042..349fe96f8 100644 --- a/translations/en/8-Reinforcement/1-QLearning/README.md +++ b/translations/en/8-Reinforcement/1-QLearning/README.md @@ -1,12 +1,3 @@ - ## Visualizing the Learned Policy After running the learning algorithm, we can visualize the Q-Table to see the learned policy. The arrows (or circles) in each cell will indicate the preferred direction of movement based on the Q-Table values. This visualization helps us understand how the agent has learned to navigate the environment. diff --git a/translations/en/8-Reinforcement/1-QLearning/assignment.md b/translations/en/8-Reinforcement/1-QLearning/assignment.md index 214a80359..bfb088c57 100644 --- a/translations/en/8-Reinforcement/1-QLearning/assignment.md +++ b/translations/en/8-Reinforcement/1-QLearning/assignment.md @@ -1,12 +1,3 @@ - # A More Realistic World In our scenario, Peter could move around almost endlessly without feeling tired or hungry. In a more realistic world, he would need to sit down and rest occasionally, as well as eat to sustain himself. Let's make our world more realistic by implementing the following rules: diff --git a/translations/en/8-Reinforcement/1-QLearning/solution/Julia/README.md b/translations/en/8-Reinforcement/1-QLearning/solution/Julia/README.md index 5e16c27f7..6bd6d687d 100644 --- a/translations/en/8-Reinforcement/1-QLearning/solution/Julia/README.md +++ b/translations/en/8-Reinforcement/1-QLearning/solution/Julia/README.md @@ -1,12 +1,3 @@ - --- diff --git a/translations/en/8-Reinforcement/1-QLearning/solution/R/README.md b/translations/en/8-Reinforcement/1-QLearning/solution/R/README.md index d99441b0d..a6fbf0d3d 100644 --- a/translations/en/8-Reinforcement/1-QLearning/solution/R/README.md +++ b/translations/en/8-Reinforcement/1-QLearning/solution/R/README.md @@ -1,12 +1,3 @@ - this is a temporary placeholder --- diff --git a/translations/en/8-Reinforcement/2-Gym/README.md b/translations/en/8-Reinforcement/2-Gym/README.md index 5e57522da..540d19807 100644 --- a/translations/en/8-Reinforcement/2-Gym/README.md +++ b/translations/en/8-Reinforcement/2-Gym/README.md @@ -1,12 +1,3 @@ - ## Prerequisites In this lesson, we will use a library called **OpenAI Gym** to simulate different **environments**. You can run the code for this lesson locally (e.g., using Visual Studio Code), in which case the simulation will open in a new window. If you're running the code online, you may need to make some adjustments, as described [here](https://towardsdatascience.com/rendering-openai-gym-envs-on-binder-and-google-colab-536f99391cc7). diff --git a/translations/en/8-Reinforcement/2-Gym/assignment.md b/translations/en/8-Reinforcement/2-Gym/assignment.md index b882ffc4f..4460c712e 100644 --- a/translations/en/8-Reinforcement/2-Gym/assignment.md +++ b/translations/en/8-Reinforcement/2-Gym/assignment.md @@ -1,12 +1,3 @@ - # Train Mountain Car [OpenAI Gym](http://gym.openai.com) is designed so that all environments share the same API—i.e., the same methods `reset`, `step`, and `render`, as well as the same abstractions for **action space** and **observation space**. This makes it possible to adapt the same reinforcement learning algorithms to different environments with minimal code changes. diff --git a/translations/en/8-Reinforcement/2-Gym/solution/Julia/README.md b/translations/en/8-Reinforcement/2-Gym/solution/Julia/README.md index c3feab50c..6bd6d687d 100644 --- a/translations/en/8-Reinforcement/2-Gym/solution/Julia/README.md +++ b/translations/en/8-Reinforcement/2-Gym/solution/Julia/README.md @@ -1,12 +1,3 @@ - --- diff --git a/translations/en/8-Reinforcement/2-Gym/solution/R/README.md b/translations/en/8-Reinforcement/2-Gym/solution/R/README.md index 2ddffb1b5..a6fbf0d3d 100644 --- a/translations/en/8-Reinforcement/2-Gym/solution/R/README.md +++ b/translations/en/8-Reinforcement/2-Gym/solution/R/README.md @@ -1,12 +1,3 @@ - this is a temporary placeholder --- diff --git a/translations/en/8-Reinforcement/README.md b/translations/en/8-Reinforcement/README.md index 6bef504ba..4800c3085 100644 --- a/translations/en/8-Reinforcement/README.md +++ b/translations/en/8-Reinforcement/README.md @@ -1,12 +1,3 @@ - # Introduction to reinforcement learning Reinforcement learning, or RL, is considered one of the fundamental paradigms of machine learning, alongside supervised learning and unsupervised learning. RL focuses on decision-making: making the right decisions or, at the very least, learning from them. diff --git a/translations/en/9-Real-World/1-Applications/README.md b/translations/en/9-Real-World/1-Applications/README.md index 427f3107e..98e21f8c4 100644 --- a/translations/en/9-Real-World/1-Applications/README.md +++ b/translations/en/9-Real-World/1-Applications/README.md @@ -1,12 +1,3 @@ - # Postscript: Machine Learning in the Real World ![Summary of machine learning in the real world in a sketchnote](../../../../sketchnotes/ml-realworld.png) diff --git a/translations/en/9-Real-World/1-Applications/assignment.md b/translations/en/9-Real-World/1-Applications/assignment.md index 6b229fac8..43661100f 100644 --- a/translations/en/9-Real-World/1-Applications/assignment.md +++ b/translations/en/9-Real-World/1-Applications/assignment.md @@ -1,12 +1,3 @@ - # A ML Scavenger Hunt ## Instructions diff --git a/translations/en/9-Real-World/2-Debugging-ML-Models/README.md b/translations/en/9-Real-World/2-Debugging-ML-Models/README.md index 445e0936f..5f3712e8a 100644 --- a/translations/en/9-Real-World/2-Debugging-ML-Models/README.md +++ b/translations/en/9-Real-World/2-Debugging-ML-Models/README.md @@ -1,12 +1,3 @@ - # Postscript: Model Debugging in Machine Learning using Responsible AI dashboard components ## [Pre-lecture quiz](https://ff-quizzes.netlify.app/en/ml/) diff --git a/translations/en/9-Real-World/2-Debugging-ML-Models/assignment.md b/translations/en/9-Real-World/2-Debugging-ML-Models/assignment.md index 7177d4699..7c2f45678 100644 --- a/translations/en/9-Real-World/2-Debugging-ML-Models/assignment.md +++ b/translations/en/9-Real-World/2-Debugging-ML-Models/assignment.md @@ -1,12 +1,3 @@ - # Explore Responsible AI (RAI) dashboard ## Instructions diff --git a/translations/en/9-Real-World/README.md b/translations/en/9-Real-World/README.md index ede44aa39..6980a235c 100644 --- a/translations/en/9-Real-World/README.md +++ b/translations/en/9-Real-World/README.md @@ -1,12 +1,3 @@ - # Postscript: Real-world applications of classical machine learning In this section of the curriculum, you'll explore some practical applications of classical machine learning. We've searched extensively to find whitepapers and articles showcasing how these techniques are applied, while deliberately minimizing the focus on neural networks, deep learning, and AI. Discover how machine learning is utilized in business systems, environmental projects, finance, arts and culture, and more. diff --git a/translations/en/AGENTS.md b/translations/en/AGENTS.md index d9efbd04e..510dc4949 100644 --- a/translations/en/AGENTS.md +++ b/translations/en/AGENTS.md @@ -1,12 +1,3 @@ - # AGENTS.md ## Project Overview diff --git a/translations/en/CODE_OF_CONDUCT.md b/translations/en/CODE_OF_CONDUCT.md index bb028b2ec..45dfbe834 100644 --- a/translations/en/CODE_OF_CONDUCT.md +++ b/translations/en/CODE_OF_CONDUCT.md @@ -1,12 +1,3 @@ - # Microsoft Open Source Code of Conduct This project follows the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/). diff --git a/translations/en/CONTRIBUTING.md b/translations/en/CONTRIBUTING.md index 0703cc98e..7d5cc7081 100644 --- a/translations/en/CONTRIBUTING.md +++ b/translations/en/CONTRIBUTING.md @@ -1,12 +1,3 @@ - # Contributing This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA), which confirms that you have the rights to, and indeed do, grant us permission to use your contribution. For more details, visit https://cla.microsoft.com. diff --git a/translations/en/README.md b/translations/en/README.md index b0aea7c83..9f62fea07 100644 --- a/translations/en/README.md +++ b/translations/en/README.md @@ -1,12 +1,3 @@ - [![GitHub license](https://img.shields.io/github/license/microsoft/ML-For-Beginners.svg)](https://github.com/microsoft/ML-For-Beginners/blob/master/LICENSE) [![GitHub contributors](https://img.shields.io/github/contributors/microsoft/ML-For-Beginners.svg)](https://GitHub.com/microsoft/ML-For-Beginners/graphs/contributors/) [![GitHub issues](https://img.shields.io/github/issues/microsoft/ML-For-Beginners.svg)](https://GitHub.com/microsoft/ML-For-Beginners/issues/) @@ -22,7 +13,7 @@ CO_OP_TRANSLATOR_METADATA: #### Supported via GitHub Action (Automated & Always Up-to-Date) -[Arabic](../ar/README.md) | [Bengali](../bn/README.md) | [Bulgarian](../bg/README.md) | [Burmese (Myanmar)](../my/README.md) | [Chinese (Simplified)](../zh/README.md) | [Chinese (Traditional, Hong Kong)](../hk/README.md) | [Chinese (Traditional, Macau)](../mo/README.md) | [Chinese (Traditional, Taiwan)](../tw/README.md) | [Croatian](../hr/README.md) | [Czech](../cs/README.md) | [Danish](../da/README.md) | [Dutch](../nl/README.md) | [Estonian](../et/README.md) | [Finnish](../fi/README.md) | [French](../fr/README.md) | [German](../de/README.md) | [Greek](../el/README.md) | [Hebrew](../he/README.md) | [Hindi](../hi/README.md) | [Hungarian](../hu/README.md) | [Indonesian](../id/README.md) | [Italian](../it/README.md) | [Japanese](../ja/README.md) | [Kannada](../kn/README.md) | [Korean](../ko/README.md) | [Lithuanian](../lt/README.md) | [Malay](../ms/README.md) | [Malayalam](../ml/README.md) | [Marathi](../mr/README.md) | [Nepali](../ne/README.md) | [Nigerian Pidgin](../pcm/README.md) | [Norwegian](../no/README.md) | [Persian (Farsi)](../fa/README.md) | [Polish](../pl/README.md) | [Portuguese (Brazil)](../br/README.md) | [Portuguese (Portugal)](../pt/README.md) | [Punjabi (Gurmukhi)](../pa/README.md) | [Romanian](../ro/README.md) | [Russian](../ru/README.md) | [Serbian (Cyrillic)](../sr/README.md) | [Slovak](../sk/README.md) | [Slovenian](../sl/README.md) | [Spanish](../es/README.md) | [Swahili](../sw/README.md) | [Swedish](../sv/README.md) | [Tagalog (Filipino)](../tl/README.md) | [Tamil](../ta/README.md) | [Telugu](../te/README.md) | [Thai](../th/README.md) | [Turkish](../tr/README.md) | [Ukrainian](../uk/README.md) | [Urdu](../ur/README.md) | [Vietnamese](../vi/README.md) +[Arabic](../ar/README.md) | [Bengali](../bn/README.md) | [Bulgarian](../bg/README.md) | [Burmese (Myanmar)](../my/README.md) | [Chinese (Simplified)](../zh-CN/README.md) | [Chinese (Traditional, Hong Kong)](../zh-HK/README.md) | [Chinese (Traditional, Macau)](../zh-MO/README.md) | [Chinese (Traditional, Taiwan)](../zh-TW/README.md) | [Croatian](../hr/README.md) | [Czech](../cs/README.md) | [Danish](../da/README.md) | [Dutch](../nl/README.md) | [Estonian](../et/README.md) | [Finnish](../fi/README.md) | [French](../fr/README.md) | [German](../de/README.md) | [Greek](../el/README.md) | [Hebrew](../he/README.md) | [Hindi](../hi/README.md) | [Hungarian](../hu/README.md) | [Indonesian](../id/README.md) | [Italian](../it/README.md) | [Japanese](../ja/README.md) | [Kannada](../kn/README.md) | [Korean](../ko/README.md) | [Lithuanian](../lt/README.md) | [Malay](../ms/README.md) | [Malayalam](../ml/README.md) | [Marathi](../mr/README.md) | [Nepali](../ne/README.md) | [Nigerian Pidgin](../pcm/README.md) | [Norwegian](../no/README.md) | [Persian (Farsi)](../fa/README.md) | [Polish](../pl/README.md) | [Portuguese (Brazil)](../pt-BR/README.md) | [Portuguese (Portugal)](../pt-PT/README.md) | [Punjabi (Gurmukhi)](../pa/README.md) | [Romanian](../ro/README.md) | [Russian](../ru/README.md) | [Serbian (Cyrillic)](../sr/README.md) | [Slovak](../sk/README.md) | [Slovenian](../sl/README.md) | [Spanish](../es/README.md) | [Swahili](../sw/README.md) | [Swedish](../sv/README.md) | [Tagalog (Filipino)](../tl/README.md) | [Tamil](../ta/README.md) | [Telugu](../te/README.md) | [Thai](../th/README.md) | [Turkish](../tr/README.md) | [Ukrainian](../uk/README.md) | [Urdu](../ur/README.md) | [Vietnamese](../vi/README.md) > **Prefer to Clone Locally?** @@ -41,7 +32,7 @@ CO_OP_TRANSLATOR_METADATA: We have a Discord learn with AI series ongoing, learn more and join us at [Learn with AI Series](https://aka.ms/learnwithai/discord) from 18 - 30 September, 2025. You will get tips and tricks of using GitHub Copilot for Data Science. -![Learn with AI series](../../../../translated_images/en/3.9b58fd8d6c373c20.webp) +![Learn with AI series](../../translated_images/en/3.9b58fd8d6c373c20.webp) # Machine Learning for Beginners - A Curriculum @@ -90,7 +81,7 @@ Follow these steps: Some of the lessons are available as short form video. You can find all these in-line in the lessons, or on the [ML for Beginners playlist on the Microsoft Developer YouTube channel](https://aka.ms/ml-beginners-videos) by clicking the image below. -[![ML for beginners banner](../../../../translated_images/en/ml-for-beginners-video-banner.63f694a100034bc6.webp)](https://aka.ms/ml-beginners-videos) +[![ML for beginners banner](../../translated_images/en/ml-for-beginners-video-banner.63f694a100034bc6.webp)](https://aka.ms/ml-beginners-videos) --- @@ -127,7 +118,7 @@ By ensuring that the content aligns with projects, the process is made more enga - [post-lecture quiz](https://ff-quizzes.netlify.app/en/ml/) > **A note about languages**: These lessons are primarily written in Python, but many are also available in R. To complete an R lesson, go to the `/solution` folder and look for R lessons. They include an .rmd extension that represents an **R Markdown** file which can be simply defined as an embedding of `code chunks` (of R or other languages) and a `YAML header` (that guides how to format outputs such as PDF) in a `Markdown document`. As such, it serves as an exemplary authoring framework for data science since it allows you to combine your code, its output, and your thoughts by allowing you to write them down in Markdown. Moreover, R Markdown documents can be rendered to output formats such as PDF, HTML, or Word. -> **A note about quizzes**: All quizzes are contained in the [Quiz App folder](../../quiz-app), for 52 total quizzes of three questions each. They are linked from within the lessons but the quiz app can be run locally; follow the instruction in the `quiz-app` folder to locally host or deploy to Azure. +> **A note about quizzes**: All quizzes are contained in [Quiz App folder](../../quiz-app), for 52 total quizzes of three questions each. They are linked from within the lessons but the quiz app can be run locally; follow the instruction in the `quiz-app` folder to locally host or deploy to Azure. | Lesson Number | Topic | Lesson Grouping | Learning Objectives | Linked Lesson | Author | | :-----------: | :------------------------------------------------------------: | :-------------------------------------------------: | ------------------------------------------------------------------------------------------------------------------------------- | :--------------------------------------------------------------------------------------------------------------------------------------: | :--------------------------------------------------: | diff --git a/translations/en/SECURITY.md b/translations/en/SECURITY.md index 2c05c37f8..b20cf5cfe 100644 --- a/translations/en/SECURITY.md +++ b/translations/en/SECURITY.md @@ -1,12 +1,3 @@ - ## Security Microsoft prioritizes the security of its software products and services, including all source code repositories managed through our GitHub organizations, such as [Microsoft](https://github.com/Microsoft), [Azure](https://github.com/Azure), [DotNet](https://github.com/dotnet), [AspNet](https://github.com/aspnet), [Xamarin](https://github.com/xamarin), and [our GitHub organizations](https://opensource.microsoft.com/). diff --git a/translations/en/SUPPORT.md b/translations/en/SUPPORT.md index 437bee5b6..e141ce111 100644 --- a/translations/en/SUPPORT.md +++ b/translations/en/SUPPORT.md @@ -1,12 +1,3 @@ - # Support ## How to file issues and get help diff --git a/translations/en/TROUBLESHOOTING.md b/translations/en/TROUBLESHOOTING.md index 6d3c46cbd..012b0f235 100644 --- a/translations/en/TROUBLESHOOTING.md +++ b/translations/en/TROUBLESHOOTING.md @@ -1,12 +1,3 @@ - # Troubleshooting Guide This guide helps you resolve common issues when working with the Machine Learning for Beginners curriculum. If you don't find a solution here, please visit our [Discord Discussions](https://aka.ms/foundry/discord) or [open an issue](https://github.com/microsoft/ML-For-Beginners/issues). diff --git a/translations/en/docs/_sidebar.md b/translations/en/docs/_sidebar.md index 379b92440..2088adf8b 100644 --- a/translations/en/docs/_sidebar.md +++ b/translations/en/docs/_sidebar.md @@ -1,12 +1,3 @@ - - Introduction - [Introduction to Machine Learning](../1-Introduction/1-intro-to-ML/README.md) - [History of Machine Learning](../1-Introduction/2-history-of-ML/README.md) diff --git a/translations/en/for-teachers.md b/translations/en/for-teachers.md index a2dd3ac27..dae314009 100644 --- a/translations/en/for-teachers.md +++ b/translations/en/for-teachers.md @@ -1,12 +1,3 @@ - ## For Educators Would you like to use this curriculum in your classroom? Feel free to do so! diff --git a/translations/en/quiz-app/README.md b/translations/en/quiz-app/README.md index 2d1aab9b5..a79f2efa7 100644 --- a/translations/en/quiz-app/README.md +++ b/translations/en/quiz-app/README.md @@ -1,12 +1,3 @@ - # Quizzes These quizzes are the pre- and post-lecture quizzes for the ML curriculum at https://aka.ms/ml-beginners diff --git a/translations/en/sketchnotes/LICENSE.md b/translations/en/sketchnotes/LICENSE.md index 4ec8ccf13..135f9fbbe 100644 --- a/translations/en/sketchnotes/LICENSE.md +++ b/translations/en/sketchnotes/LICENSE.md @@ -1,12 +1,3 @@ - Rights, then the database in which You have Sui Generis Database Rights (but not its individual contents) is Adapted Material; and c. You must comply with the conditions in Section 3(a) if You Share all or a substantial portion of the contents of the database. diff --git a/translations/en/sketchnotes/README.md b/translations/en/sketchnotes/README.md index 12fefa2b4..2728f9a04 100644 --- a/translations/en/sketchnotes/README.md +++ b/translations/en/sketchnotes/README.md @@ -1,12 +1,3 @@ - All the sketchnotes for the curriculum can be downloaded here. 🖨 For high-resolution printing, TIFF versions are available at [this repo](https://github.com/girliemac/a-picture-is-worth-a-1000-words/tree/main/ml/tiff). diff --git a/translations/es/.co-op-translator.json b/translations/es/.co-op-translator.json new file mode 100644 index 000000000..b330bfb6d --- /dev/null +++ b/translations/es/.co-op-translator.json @@ -0,0 +1,596 @@ +{ + "1-Introduction/1-intro-to-ML/README.md": { + "original_hash": "69389392fa6346e0dfa30f664b7b6fec", + "translation_date": "2025-09-04T22:21:40+00:00", + "source_file": 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{ + "original_hash": "fba3b94d88bfb9b81369b869a1e9a20f", + "translation_date": "2025-09-04T00:10:39+00:00", + "source_file": "sketchnotes/LICENSE.md", + "language_code": "es" + }, + "sketchnotes/README.md": { + "original_hash": "a88d5918c1b9da69a40d917a0840c497", + "translation_date": "2025-09-04T00:05:03+00:00", + "source_file": "sketchnotes/README.md", + "language_code": "es" + } +} \ No newline at end of file diff --git a/translations/es/1-Introduction/1-intro-to-ML/README.md b/translations/es/1-Introduction/1-intro-to-ML/README.md index d9abed8ea..71f3ff689 100644 --- a/translations/es/1-Introduction/1-intro-to-ML/README.md +++ b/translations/es/1-Introduction/1-intro-to-ML/README.md @@ -1,12 +1,3 @@ - # Introducción al aprendizaje automático ## [Cuestionario previo a la clase](https://ff-quizzes.netlify.app/en/ml/) diff --git a/translations/es/1-Introduction/1-intro-to-ML/assignment.md b/translations/es/1-Introduction/1-intro-to-ML/assignment.md index 84c41429f..15466c259 100644 --- a/translations/es/1-Introduction/1-intro-to-ML/assignment.md +++ b/translations/es/1-Introduction/1-intro-to-ML/assignment.md @@ -1,12 +1,3 @@ - # Ponte en marcha ## Instrucciones diff --git a/translations/es/1-Introduction/2-history-of-ML/README.md b/translations/es/1-Introduction/2-history-of-ML/README.md index 3f4e91222..374881230 100644 --- a/translations/es/1-Introduction/2-history-of-ML/README.md +++ b/translations/es/1-Introduction/2-history-of-ML/README.md @@ -1,12 +1,3 @@ - # Historia del aprendizaje automático ![Resumen de la historia del aprendizaje automático en un sketchnote](../../../../sketchnotes/ml-history.png) diff --git a/translations/es/1-Introduction/2-history-of-ML/assignment.md b/translations/es/1-Introduction/2-history-of-ML/assignment.md index d5fb84722..a4a3b9837 100644 --- a/translations/es/1-Introduction/2-history-of-ML/assignment.md +++ b/translations/es/1-Introduction/2-history-of-ML/assignment.md @@ -1,12 +1,3 @@ - # Crear una línea de tiempo ## Instrucciones diff --git a/translations/es/1-Introduction/3-fairness/README.md b/translations/es/1-Introduction/3-fairness/README.md index a9c3a3f89..320d03b97 100644 --- a/translations/es/1-Introduction/3-fairness/README.md +++ b/translations/es/1-Introduction/3-fairness/README.md @@ -1,12 +1,3 @@ - # Construyendo soluciones de aprendizaje automático con IA responsable ![Resumen de IA responsable en aprendizaje automático en un sketchnote](../../../../sketchnotes/ml-fairness.png) diff --git a/translations/es/1-Introduction/3-fairness/assignment.md b/translations/es/1-Introduction/3-fairness/assignment.md index 18ddbede4..a167610e8 100644 --- a/translations/es/1-Introduction/3-fairness/assignment.md +++ b/translations/es/1-Introduction/3-fairness/assignment.md @@ -1,12 +1,3 @@ - # Explora el Toolbox de IA Responsable ## Instrucciones diff --git a/translations/es/1-Introduction/4-techniques-of-ML/README.md b/translations/es/1-Introduction/4-techniques-of-ML/README.md index 86216de1a..cd3b96e73 100644 --- a/translations/es/1-Introduction/4-techniques-of-ML/README.md +++ b/translations/es/1-Introduction/4-techniques-of-ML/README.md @@ -1,12 +1,3 @@ - # Técnicas de Aprendizaje Automático El proceso de construir, usar y mantener modelos de aprendizaje automático y los datos que utilizan es muy diferente de muchos otros flujos de trabajo de desarrollo. En esta lección, desmitificaremos el proceso y describiremos las principales técnicas que necesitas conocer. Tú: diff --git a/translations/es/1-Introduction/4-techniques-of-ML/assignment.md b/translations/es/1-Introduction/4-techniques-of-ML/assignment.md index 42cfcb035..9a5c53e99 100644 --- a/translations/es/1-Introduction/4-techniques-of-ML/assignment.md +++ b/translations/es/1-Introduction/4-techniques-of-ML/assignment.md @@ -1,12 +1,3 @@ - # Entrevista a un científico de datos ## Instrucciones diff --git a/translations/es/1-Introduction/README.md b/translations/es/1-Introduction/README.md index d7d6796d4..2aecdfa9a 100644 --- a/translations/es/1-Introduction/README.md +++ b/translations/es/1-Introduction/README.md @@ -1,12 +1,3 @@ - # Introducción al aprendizaje automático En esta sección del plan de estudios, se te presentarán los conceptos básicos que sustentan el campo del aprendizaje automático, qué es, y aprenderás sobre su historia y las técnicas que los investigadores utilizan para trabajar con él. ¡Exploremos juntos este nuevo mundo del aprendizaje automático! diff --git a/translations/es/2-Regression/1-Tools/README.md b/translations/es/2-Regression/1-Tools/README.md index 077faad27..9da925065 100644 --- a/translations/es/2-Regression/1-Tools/README.md +++ b/translations/es/2-Regression/1-Tools/README.md @@ -1,12 +1,3 @@ - # Comienza con Python y Scikit-learn para modelos de regresión ![Resumen de regresiones en un sketchnote](../../../../sketchnotes/ml-regression.png) diff --git a/translations/es/2-Regression/1-Tools/assignment.md b/translations/es/2-Regression/1-Tools/assignment.md index 0f8278d17..28a9a6351 100644 --- a/translations/es/2-Regression/1-Tools/assignment.md +++ b/translations/es/2-Regression/1-Tools/assignment.md @@ -1,12 +1,3 @@ - # Regresión con Scikit-learn ## Instrucciones diff --git a/translations/es/2-Regression/1-Tools/solution/Julia/README.md b/translations/es/2-Regression/1-Tools/solution/Julia/README.md index d39a2c6f2..fa6cc3256 100644 --- a/translations/es/2-Regression/1-Tools/solution/Julia/README.md +++ b/translations/es/2-Regression/1-Tools/solution/Julia/README.md @@ -1,12 +1,3 @@ - --- diff --git a/translations/es/2-Regression/2-Data/README.md b/translations/es/2-Regression/2-Data/README.md index a54efd0ae..b923bd389 100644 --- a/translations/es/2-Regression/2-Data/README.md +++ b/translations/es/2-Regression/2-Data/README.md @@ -1,12 +1,3 @@ - # Construir un modelo de regresión usando Scikit-learn: preparar y visualizar datos ![Infografía de visualización de datos](../../../../2-Regression/2-Data/images/data-visualization.png) diff --git a/translations/es/2-Regression/2-Data/assignment.md b/translations/es/2-Regression/2-Data/assignment.md index 061b9f9d9..996ad003a 100644 --- a/translations/es/2-Regression/2-Data/assignment.md +++ b/translations/es/2-Regression/2-Data/assignment.md @@ -1,12 +1,3 @@ - # Explorando Visualizaciones Existen varias bibliotecas disponibles para la visualización de datos. Crea algunas visualizaciones utilizando los datos de Pumpkin en esta lección con matplotlib y seaborn en un cuaderno de ejemplo. ¿Qué bibliotecas son más fáciles de usar? diff --git a/translations/es/2-Regression/2-Data/solution/Julia/README.md b/translations/es/2-Regression/2-Data/solution/Julia/README.md index 3ded93351..3e1c9dd06 100644 --- a/translations/es/2-Regression/2-Data/solution/Julia/README.md +++ b/translations/es/2-Regression/2-Data/solution/Julia/README.md @@ -1,12 +1,3 @@ - --- diff --git a/translations/es/2-Regression/3-Linear/README.md b/translations/es/2-Regression/3-Linear/README.md index 433e45122..ae43d7eb3 100644 --- a/translations/es/2-Regression/3-Linear/README.md +++ b/translations/es/2-Regression/3-Linear/README.md @@ -1,12 +1,3 @@ - # Construir un modelo de regresión usando Scikit-learn: cuatro formas de regresión ![Infografía de regresión lineal vs polinómica](../../../../2-Regression/3-Linear/images/linear-polynomial.png) @@ -114,11 +105,11 @@ Ahora que tienes una comprensión de las matemáticas detrás de la regresión l De la lección anterior probablemente hayas visto que el precio promedio para diferentes meses se ve así: -Precio promedio por mes +Precio promedio por mes Esto sugiere que debería haber alguna correlación, y podemos intentar entrenar un modelo de regresión lineal para predecir la relación entre `Mes` y `Precio`, o entre `DíaDelAño` y `Precio`. Aquí está el gráfico de dispersión que muestra esta última relación: -Gráfico de dispersión de Precio vs. Día del Año +Gráfico de dispersión de Precio vs. Día del Año Veamos si hay una correlación usando la función `corr`: @@ -137,7 +128,7 @@ for i,var in enumerate(new_pumpkins['Variety'].unique()): ax = df.plot.scatter('DayOfYear','Price',ax=ax,c=colors[i],label=var) ``` -Gráfico de dispersión de Precio vs. Día del Año +Gráfico de dispersión de Precio vs. Día del Año Nuestra investigación sugiere que la variedad tiene más efecto en el precio general que la fecha de venta real. Podemos ver esto con un gráfico de barras: @@ -145,7 +136,7 @@ Nuestra investigación sugiere que la variedad tiene más efecto en el precio ge new_pumpkins.groupby('Variety')['Price'].mean().plot(kind='bar') ``` -Gráfico de barras de precio vs variedad +Gráfico de barras de precio vs variedad Centrémonos por el momento solo en una variedad de calabaza, el 'tipo pie', y veamos qué efecto tiene la fecha en el precio: @@ -153,7 +144,7 @@ Centrémonos por el momento solo en una variedad de calabaza, el 'tipo pie', y v pie_pumpkins = new_pumpkins[new_pumpkins['Variety']=='PIE TYPE'] pie_pumpkins.plot.scatter('DayOfYear','Price') ``` -Gráfico de dispersión de Precio vs. Día del Año +Gráfico de dispersión de Precio vs. Día del Año Si ahora calculamos la correlación entre `Precio` y `DíaDelAño` usando la función `corr`, obtendremos algo como `-0.27`, lo que significa que tiene sentido entrenar un modelo predictivo. @@ -227,7 +218,7 @@ plt.scatter(X_test,y_test) plt.plot(X_test,pred) ``` -Regresión lineal +Regresión lineal ## Regresión Polinómica @@ -256,7 +247,7 @@ Usar `PolynomialFeatures(2)` significa que incluiremos todos los polinomios de s Los pipelines pueden usarse de la misma manera que el objeto original `LinearRegression`, es decir, podemos usar `fit` en el pipeline y luego usar `predict` para obtener los resultados de predicción. Aquí está el gráfico que muestra los datos de prueba y la curva de aproximación: -Regresión polinómica +Regresión polinómica Usando Regresión Polinómica, podemos obtener un MSE ligeramente más bajo y un coeficiente de determinación más alto, pero no significativamente. ¡Necesitamos tomar en cuenta otras características! @@ -274,7 +265,7 @@ En un mundo ideal, queremos poder predecir precios para diferentes variedades de Aquí puedes ver cómo el precio promedio depende de la variedad: -Precio promedio por variedad +Precio promedio por variedad Para tomar en cuenta la variedad, primero necesitamos convertirla a forma numérica, o **codificarla**. Hay varias maneras de hacerlo: diff --git a/translations/es/2-Regression/3-Linear/assignment.md b/translations/es/2-Regression/3-Linear/assignment.md index 3ba989374..ef5200ef9 100644 --- a/translations/es/2-Regression/3-Linear/assignment.md +++ b/translations/es/2-Regression/3-Linear/assignment.md @@ -1,12 +1,3 @@ - # Crear un Modelo de Regresión ## Instrucciones diff --git a/translations/es/2-Regression/3-Linear/solution/Julia/README.md b/translations/es/2-Regression/3-Linear/solution/Julia/README.md index 2ce9b0d09..06cbcf487 100644 --- a/translations/es/2-Regression/3-Linear/solution/Julia/README.md +++ b/translations/es/2-Regression/3-Linear/solution/Julia/README.md @@ -1,12 +1,3 @@ - --- diff --git a/translations/es/2-Regression/4-Logistic/README.md b/translations/es/2-Regression/4-Logistic/README.md index dd68dda4d..868a84f6a 100644 --- a/translations/es/2-Regression/4-Logistic/README.md +++ b/translations/es/2-Regression/4-Logistic/README.md @@ -1,12 +1,3 @@ - # Regresión logística para predecir categorías ![Infografía de regresión logística vs. regresión lineal](../../../../2-Regression/4-Logistic/images/linear-vs-logistic.png) diff --git a/translations/es/2-Regression/4-Logistic/assignment.md b/translations/es/2-Regression/4-Logistic/assignment.md index f95b77be7..305d446a9 100644 --- a/translations/es/2-Regression/4-Logistic/assignment.md +++ b/translations/es/2-Regression/4-Logistic/assignment.md @@ -1,12 +1,3 @@ - # Reintentando algo de Regresión ## Instrucciones diff --git a/translations/es/2-Regression/4-Logistic/solution/Julia/README.md b/translations/es/2-Regression/4-Logistic/solution/Julia/README.md index 88b0db57c..7a0aaee20 100644 --- a/translations/es/2-Regression/4-Logistic/solution/Julia/README.md +++ b/translations/es/2-Regression/4-Logistic/solution/Julia/README.md @@ -1,12 +1,3 @@ - --- diff --git a/translations/es/2-Regression/README.md b/translations/es/2-Regression/README.md index fdc8ac1c2..a1588a250 100644 --- a/translations/es/2-Regression/README.md +++ b/translations/es/2-Regression/README.md @@ -1,12 +1,3 @@ - # Modelos de regresión para aprendizaje automático ## Tema regional: Modelos de regresión para precios de calabazas en América del Norte 🎃 diff --git a/translations/es/3-Web-App/1-Web-App/README.md b/translations/es/3-Web-App/1-Web-App/README.md index df9ee5952..aca876c05 100644 --- a/translations/es/3-Web-App/1-Web-App/README.md +++ b/translations/es/3-Web-App/1-Web-App/README.md @@ -1,12 +1,3 @@ - # Construir una aplicación web para usar un modelo de ML En esta lección, entrenarás un modelo de ML con un conjunto de datos que es de otro mundo: _avistamientos de OVNIs durante el último siglo_, obtenidos de la base de datos de NUFORC. diff --git a/translations/es/3-Web-App/1-Web-App/assignment.md b/translations/es/3-Web-App/1-Web-App/assignment.md index 44ecab056..8a9275b9b 100644 --- a/translations/es/3-Web-App/1-Web-App/assignment.md +++ b/translations/es/3-Web-App/1-Web-App/assignment.md @@ -1,12 +1,3 @@ - # Prueba con un modelo diferente ## Instrucciones diff --git a/translations/es/3-Web-App/README.md b/translations/es/3-Web-App/README.md index 378aaf8b6..ac643f7c2 100644 --- a/translations/es/3-Web-App/README.md +++ b/translations/es/3-Web-App/README.md @@ -1,12 +1,3 @@ - # Crea una aplicación web para usar tu modelo de ML En esta sección del curso, se te presentará un tema práctico de aprendizaje automático: cómo guardar tu modelo de Scikit-learn como un archivo que pueda ser utilizado para hacer predicciones dentro de una aplicación web. Una vez que el modelo esté guardado, aprenderás cómo usarlo en una aplicación web construida con Flask. Primero, crearás un modelo utilizando algunos datos relacionados con avistamientos de OVNIs. Luego, construirás una aplicación web que te permitirá ingresar un número de segundos junto con un valor de latitud y longitud para predecir qué país reportó haber visto un OVNI. diff --git a/translations/es/4-Classification/1-Introduction/README.md b/translations/es/4-Classification/1-Introduction/README.md index 773954f4d..19d4906d7 100644 --- a/translations/es/4-Classification/1-Introduction/README.md +++ b/translations/es/4-Classification/1-Introduction/README.md @@ -1,12 +1,3 @@ - # Introducción a la clasificación En estas cuatro lecciones, explorarás un enfoque fundamental del aprendizaje automático clásico: _la clasificación_. Utilizaremos varios algoritmos de clasificación con un conjunto de datos sobre las brillantes cocinas de Asia e India. ¡Espero que tengas hambre! diff --git a/translations/es/4-Classification/1-Introduction/assignment.md b/translations/es/4-Classification/1-Introduction/assignment.md index 64a90e383..6469d1363 100644 --- a/translations/es/4-Classification/1-Introduction/assignment.md +++ b/translations/es/4-Classification/1-Introduction/assignment.md @@ -1,12 +1,3 @@ - # Explora métodos de clasificación ## Instrucciones diff --git a/translations/es/4-Classification/1-Introduction/solution/Julia/README.md b/translations/es/4-Classification/1-Introduction/solution/Julia/README.md index d784cfc76..231af8f9e 100644 --- a/translations/es/4-Classification/1-Introduction/solution/Julia/README.md +++ b/translations/es/4-Classification/1-Introduction/solution/Julia/README.md @@ -1,12 +1,3 @@ - --- diff --git a/translations/es/4-Classification/2-Classifiers-1/README.md b/translations/es/4-Classification/2-Classifiers-1/README.md index 9e91c8ba7..4ca93e70f 100644 --- a/translations/es/4-Classification/2-Classifiers-1/README.md +++ b/translations/es/4-Classification/2-Classifiers-1/README.md @@ -1,12 +1,3 @@ - # Clasificadores de cocina 1 En esta lección, usarás el conjunto de datos que guardaste en la última lección, lleno de datos equilibrados y limpios sobre cocinas. diff --git a/translations/es/4-Classification/2-Classifiers-1/assignment.md b/translations/es/4-Classification/2-Classifiers-1/assignment.md index 01591b72c..ab9d4d623 100644 --- a/translations/es/4-Classification/2-Classifiers-1/assignment.md +++ b/translations/es/4-Classification/2-Classifiers-1/assignment.md @@ -1,12 +1,3 @@ - # Estudia los solucionadores ## Instrucciones diff --git a/translations/es/4-Classification/2-Classifiers-1/solution/Julia/README.md b/translations/es/4-Classification/2-Classifiers-1/solution/Julia/README.md index df9307ce5..fa6cc3256 100644 --- a/translations/es/4-Classification/2-Classifiers-1/solution/Julia/README.md +++ b/translations/es/4-Classification/2-Classifiers-1/solution/Julia/README.md @@ -1,12 +1,3 @@ - --- diff --git a/translations/es/4-Classification/3-Classifiers-2/README.md b/translations/es/4-Classification/3-Classifiers-2/README.md index e1ba25c26..202a8fc99 100644 --- a/translations/es/4-Classification/3-Classifiers-2/README.md +++ b/translations/es/4-Classification/3-Classifiers-2/README.md @@ -1,12 +1,3 @@ - # Clasificadores de cocina 2 En esta segunda lección de clasificación, explorarás más formas de clasificar datos numéricos. También aprenderás sobre las implicaciones de elegir un clasificador sobre otro. diff --git a/translations/es/4-Classification/3-Classifiers-2/assignment.md b/translations/es/4-Classification/3-Classifiers-2/assignment.md index af1b431d1..36c804058 100644 --- a/translations/es/4-Classification/3-Classifiers-2/assignment.md +++ b/translations/es/4-Classification/3-Classifiers-2/assignment.md @@ -1,12 +1,3 @@ - # Juego de Parámetros ## Instrucciones diff --git a/translations/es/4-Classification/3-Classifiers-2/solution/Julia/README.md b/translations/es/4-Classification/3-Classifiers-2/solution/Julia/README.md index 5e943c949..231af8f9e 100644 --- a/translations/es/4-Classification/3-Classifiers-2/solution/Julia/README.md +++ b/translations/es/4-Classification/3-Classifiers-2/solution/Julia/README.md @@ -1,12 +1,3 @@ - --- diff --git a/translations/es/4-Classification/4-Applied/README.md b/translations/es/4-Classification/4-Applied/README.md index e4be40c62..65d29736a 100644 --- a/translations/es/4-Classification/4-Applied/README.md +++ b/translations/es/4-Classification/4-Applied/README.md @@ -1,12 +1,3 @@ - # Construir una Aplicación Web de Recomendación de Cocina En esta lección, construirás un modelo de clasificación utilizando algunas de las técnicas que has aprendido en lecciones anteriores y con el delicioso conjunto de datos de cocina utilizado a lo largo de esta serie. Además, crearás una pequeña aplicación web para usar un modelo guardado, aprovechando el runtime web de Onnx. diff --git a/translations/es/4-Classification/4-Applied/assignment.md b/translations/es/4-Classification/4-Applied/assignment.md index 655b84ad7..f61470a30 100644 --- a/translations/es/4-Classification/4-Applied/assignment.md +++ b/translations/es/4-Classification/4-Applied/assignment.md @@ -1,12 +1,3 @@ - # Construir un recomendador ## Instrucciones diff --git a/translations/es/4-Classification/README.md b/translations/es/4-Classification/README.md index e7820f167..2e90c3f07 100644 --- a/translations/es/4-Classification/README.md +++ b/translations/es/4-Classification/README.md @@ -1,12 +1,3 @@ - # Comenzando con la clasificación ## Tema regional: Deliciosas cocinas asiáticas e indias 🍜 diff --git a/translations/es/5-Clustering/1-Visualize/README.md b/translations/es/5-Clustering/1-Visualize/README.md index c429fcebe..9e383584e 100644 --- a/translations/es/5-Clustering/1-Visualize/README.md +++ b/translations/es/5-Clustering/1-Visualize/README.md @@ -1,12 +1,3 @@ - # Introducción a la agrupación La agrupación es un tipo de [aprendizaje no supervisado](https://wikipedia.org/wiki/Unsupervised_learning) que asume que un conjunto de datos no está etiquetado o que sus entradas no están asociadas con salidas predefinidas. Utiliza varios algoritmos para clasificar datos no etiquetados y proporcionar agrupaciones según los patrones que detecta en los datos. diff --git a/translations/es/5-Clustering/1-Visualize/assignment.md b/translations/es/5-Clustering/1-Visualize/assignment.md index a7e9a6247..17fa5992f 100644 --- a/translations/es/5-Clustering/1-Visualize/assignment.md +++ b/translations/es/5-Clustering/1-Visualize/assignment.md @@ -1,12 +1,3 @@ - # Investigar otras visualizaciones para agrupamiento ## Instrucciones diff --git a/translations/es/5-Clustering/1-Visualize/solution/Julia/README.md b/translations/es/5-Clustering/1-Visualize/solution/Julia/README.md index e45aa1f0f..231af8f9e 100644 --- a/translations/es/5-Clustering/1-Visualize/solution/Julia/README.md +++ b/translations/es/5-Clustering/1-Visualize/solution/Julia/README.md @@ -1,12 +1,3 @@ - --- diff --git a/translations/es/5-Clustering/2-K-Means/README.md b/translations/es/5-Clustering/2-K-Means/README.md index c4e9a40d6..66775405b 100644 --- a/translations/es/5-Clustering/2-K-Means/README.md +++ b/translations/es/5-Clustering/2-K-Means/README.md @@ -1,12 +1,3 @@ - # Agrupamiento K-Means ## [Cuestionario previo a la lección](https://ff-quizzes.netlify.app/en/ml/) diff --git a/translations/es/5-Clustering/2-K-Means/assignment.md b/translations/es/5-Clustering/2-K-Means/assignment.md index 5bb8e93ed..462fa5347 100644 --- a/translations/es/5-Clustering/2-K-Means/assignment.md +++ b/translations/es/5-Clustering/2-K-Means/assignment.md @@ -1,12 +1,3 @@ - # Prueba diferentes métodos de agrupamiento ## Instrucciones diff --git a/translations/es/5-Clustering/2-K-Means/solution/Julia/README.md b/translations/es/5-Clustering/2-K-Means/solution/Julia/README.md index d92796796..231af8f9e 100644 --- a/translations/es/5-Clustering/2-K-Means/solution/Julia/README.md +++ b/translations/es/5-Clustering/2-K-Means/solution/Julia/README.md @@ -1,12 +1,3 @@ - --- diff --git a/translations/es/5-Clustering/README.md b/translations/es/5-Clustering/README.md index 560d5bbbe..6c435e55c 100644 --- a/translations/es/5-Clustering/README.md +++ b/translations/es/5-Clustering/README.md @@ -1,12 +1,3 @@ - # Modelos de agrupamiento para aprendizaje automático El agrupamiento es una tarea de aprendizaje automático que busca encontrar objetos que se asemejen entre sí y agruparlos en grupos llamados clústeres. Lo que diferencia el agrupamiento de otros enfoques en el aprendizaje automático es que todo sucede automáticamente; de hecho, es justo decir que es lo opuesto al aprendizaje supervisado. diff --git a/translations/es/6-NLP/1-Introduction-to-NLP/README.md b/translations/es/6-NLP/1-Introduction-to-NLP/README.md index 7b4e0d3ee..69cee413b 100644 --- a/translations/es/6-NLP/1-Introduction-to-NLP/README.md +++ b/translations/es/6-NLP/1-Introduction-to-NLP/README.md @@ -1,12 +1,3 @@ - # Introducción al procesamiento de lenguaje natural Esta lección cubre una breve historia y conceptos importantes del *procesamiento de lenguaje natural*, un subcampo de la *lingüística computacional*. diff --git a/translations/es/6-NLP/1-Introduction-to-NLP/assignment.md b/translations/es/6-NLP/1-Introduction-to-NLP/assignment.md index 2dbb10608..10501fb5d 100644 --- a/translations/es/6-NLP/1-Introduction-to-NLP/assignment.md +++ b/translations/es/6-NLP/1-Introduction-to-NLP/assignment.md @@ -1,12 +1,3 @@ - # Busca un bot ## Instrucciones diff --git a/translations/es/6-NLP/2-Tasks/README.md b/translations/es/6-NLP/2-Tasks/README.md index 173a678c8..2b0efd82c 100644 --- a/translations/es/6-NLP/2-Tasks/README.md +++ b/translations/es/6-NLP/2-Tasks/README.md @@ -1,12 +1,3 @@ - # Tareas y técnicas comunes de procesamiento de lenguaje natural Para la mayoría de las tareas de *procesamiento de lenguaje natural*, el texto que se va a procesar debe descomponerse, examinarse y los resultados almacenarse o cruzarse con reglas y conjuntos de datos. Estas tareas permiten al programador derivar el _significado_, la _intención_ o solo la _frecuencia_ de términos y palabras en un texto. diff --git a/translations/es/6-NLP/2-Tasks/assignment.md b/translations/es/6-NLP/2-Tasks/assignment.md index 45062e651..34c4577f1 100644 --- a/translations/es/6-NLP/2-Tasks/assignment.md +++ b/translations/es/6-NLP/2-Tasks/assignment.md @@ -1,12 +1,3 @@ - # Haz que un bot responda ## Instrucciones diff --git a/translations/es/6-NLP/3-Translation-Sentiment/README.md b/translations/es/6-NLP/3-Translation-Sentiment/README.md index 5f414f97d..f3973eec7 100644 --- a/translations/es/6-NLP/3-Translation-Sentiment/README.md +++ b/translations/es/6-NLP/3-Translation-Sentiment/README.md @@ -1,12 +1,3 @@ - # Traducción y análisis de sentimientos con ML En las lecciones anteriores aprendiste cómo construir un bot básico utilizando `TextBlob`, una biblioteca que incorpora ML detrás de escena para realizar tareas básicas de PLN como la extracción de frases nominales. Otro desafío importante en la lingüística computacional es la _traducción_ precisa de una oración de un idioma hablado o escrito a otro. diff --git a/translations/es/6-NLP/3-Translation-Sentiment/assignment.md b/translations/es/6-NLP/3-Translation-Sentiment/assignment.md index d96d8ff03..4afedea2c 100644 --- a/translations/es/6-NLP/3-Translation-Sentiment/assignment.md +++ b/translations/es/6-NLP/3-Translation-Sentiment/assignment.md @@ -1,12 +1,3 @@ - # Licencia poética ## Instrucciones diff --git a/translations/es/6-NLP/3-Translation-Sentiment/solution/Julia/README.md b/translations/es/6-NLP/3-Translation-Sentiment/solution/Julia/README.md index 19b11973d..fa6cc3256 100644 --- a/translations/es/6-NLP/3-Translation-Sentiment/solution/Julia/README.md +++ b/translations/es/6-NLP/3-Translation-Sentiment/solution/Julia/README.md @@ -1,12 +1,3 @@ - --- diff --git a/translations/es/6-NLP/3-Translation-Sentiment/solution/R/README.md b/translations/es/6-NLP/3-Translation-Sentiment/solution/R/README.md index 412650613..231af8f9e 100644 --- a/translations/es/6-NLP/3-Translation-Sentiment/solution/R/README.md +++ b/translations/es/6-NLP/3-Translation-Sentiment/solution/R/README.md @@ -1,12 +1,3 @@ - --- diff --git a/translations/es/6-NLP/4-Hotel-Reviews-1/README.md b/translations/es/6-NLP/4-Hotel-Reviews-1/README.md index ef13b317f..85f606446 100644 --- a/translations/es/6-NLP/4-Hotel-Reviews-1/README.md +++ b/translations/es/6-NLP/4-Hotel-Reviews-1/README.md @@ -1,12 +1,3 @@ - # Análisis de sentimientos con reseñas de hoteles - procesando los datos En esta sección, utilizarás las técnicas de las lecciones anteriores para realizar un análisis exploratorio de datos en un conjunto de datos grande. Una vez que tengas una buena comprensión de la utilidad de las diferentes columnas, aprenderás: diff --git a/translations/es/6-NLP/4-Hotel-Reviews-1/assignment.md b/translations/es/6-NLP/4-Hotel-Reviews-1/assignment.md index 657f19bcf..59fb5a741 100644 --- a/translations/es/6-NLP/4-Hotel-Reviews-1/assignment.md +++ b/translations/es/6-NLP/4-Hotel-Reviews-1/assignment.md @@ -1,12 +1,3 @@ - # NLTK ## Instrucciones diff --git a/translations/es/6-NLP/4-Hotel-Reviews-1/solution/Julia/README.md b/translations/es/6-NLP/4-Hotel-Reviews-1/solution/Julia/README.md index b75ef597e..231af8f9e 100644 --- a/translations/es/6-NLP/4-Hotel-Reviews-1/solution/Julia/README.md +++ b/translations/es/6-NLP/4-Hotel-Reviews-1/solution/Julia/README.md @@ -1,12 +1,3 @@ - --- diff --git a/translations/es/6-NLP/4-Hotel-Reviews-1/solution/R/README.md b/translations/es/6-NLP/4-Hotel-Reviews-1/solution/R/README.md index 30759bd6d..231af8f9e 100644 --- a/translations/es/6-NLP/4-Hotel-Reviews-1/solution/R/README.md +++ b/translations/es/6-NLP/4-Hotel-Reviews-1/solution/R/README.md @@ -1,12 +1,3 @@ - --- diff --git a/translations/es/6-NLP/5-Hotel-Reviews-2/README.md b/translations/es/6-NLP/5-Hotel-Reviews-2/README.md index 57709b178..983d2154e 100644 --- a/translations/es/6-NLP/5-Hotel-Reviews-2/README.md +++ b/translations/es/6-NLP/5-Hotel-Reviews-2/README.md @@ -1,12 +1,3 @@ - # Análisis de sentimientos con reseñas de hoteles Ahora que has explorado el conjunto de datos en detalle, es momento de filtrar las columnas y luego usar técnicas de procesamiento de lenguaje natural (NLP) en el conjunto de datos para obtener nuevas perspectivas sobre los hoteles. diff --git a/translations/es/6-NLP/5-Hotel-Reviews-2/assignment.md b/translations/es/6-NLP/5-Hotel-Reviews-2/assignment.md index 8dadbaa59..c48000ca2 100644 --- a/translations/es/6-NLP/5-Hotel-Reviews-2/assignment.md +++ b/translations/es/6-NLP/5-Hotel-Reviews-2/assignment.md @@ -1,12 +1,3 @@ - # Prueba con un conjunto de datos diferente ## Instrucciones diff --git a/translations/es/6-NLP/5-Hotel-Reviews-2/solution/Julia/README.md b/translations/es/6-NLP/5-Hotel-Reviews-2/solution/Julia/README.md index 7267c6dfe..231af8f9e 100644 --- a/translations/es/6-NLP/5-Hotel-Reviews-2/solution/Julia/README.md +++ b/translations/es/6-NLP/5-Hotel-Reviews-2/solution/Julia/README.md @@ -1,12 +1,3 @@ - --- diff --git a/translations/es/6-NLP/5-Hotel-Reviews-2/solution/R/README.md b/translations/es/6-NLP/5-Hotel-Reviews-2/solution/R/README.md index 10167492d..231af8f9e 100644 --- a/translations/es/6-NLP/5-Hotel-Reviews-2/solution/R/README.md +++ b/translations/es/6-NLP/5-Hotel-Reviews-2/solution/R/README.md @@ -1,12 +1,3 @@ - --- diff --git a/translations/es/6-NLP/README.md b/translations/es/6-NLP/README.md index 4991dd6cc..e09790289 100644 --- a/translations/es/6-NLP/README.md +++ b/translations/es/6-NLP/README.md @@ -1,12 +1,3 @@ - # Comenzando con el procesamiento de lenguaje natural El procesamiento de lenguaje natural (NLP, por sus siglas en inglés) es la capacidad de un programa de computadora para entender el lenguaje humano tal como se habla y se escribe, conocido como lenguaje natural. Es un componente de la inteligencia artificial (IA). El NLP ha existido por más de 50 años y tiene raíces en el campo de la lingüística. Todo el campo está dirigido a ayudar a las máquinas a entender y procesar el lenguaje humano. Esto puede ser utilizado para realizar tareas como la corrección ortográfica o la traducción automática. Tiene una variedad de aplicaciones en el mundo real en varios campos, incluyendo la investigación médica, los motores de búsqueda y la inteligencia empresarial. diff --git a/translations/es/6-NLP/data/README.md b/translations/es/6-NLP/data/README.md index 628ed0a7f..3098e46f8 100644 --- a/translations/es/6-NLP/data/README.md +++ b/translations/es/6-NLP/data/README.md @@ -1,12 +1,3 @@ - Descarga los datos de reseñas de hoteles en esta carpeta. --- diff --git a/translations/es/7-TimeSeries/1-Introduction/README.md b/translations/es/7-TimeSeries/1-Introduction/README.md index 54e857523..e48d68bf4 100644 --- a/translations/es/7-TimeSeries/1-Introduction/README.md +++ b/translations/es/7-TimeSeries/1-Introduction/README.md @@ -1,12 +1,3 @@ - # Introducción a la predicción de series temporales ![Resumen de series temporales en un sketchnote](../../../../sketchnotes/ml-timeseries.png) diff --git a/translations/es/7-TimeSeries/1-Introduction/assignment.md b/translations/es/7-TimeSeries/1-Introduction/assignment.md index 2f62a32c4..30a975cdf 100644 --- a/translations/es/7-TimeSeries/1-Introduction/assignment.md +++ b/translations/es/7-TimeSeries/1-Introduction/assignment.md @@ -1,12 +1,3 @@ - # Visualiza algunas series temporales más ## Instrucciones diff --git a/translations/es/7-TimeSeries/1-Introduction/solution/Julia/README.md b/translations/es/7-TimeSeries/1-Introduction/solution/Julia/README.md index ff6617cd1..3e1c9dd06 100644 --- a/translations/es/7-TimeSeries/1-Introduction/solution/Julia/README.md +++ b/translations/es/7-TimeSeries/1-Introduction/solution/Julia/README.md @@ -1,12 +1,3 @@ - --- diff --git a/translations/es/7-TimeSeries/1-Introduction/solution/R/README.md b/translations/es/7-TimeSeries/1-Introduction/solution/R/README.md index 1bf0a688c..587ece1fb 100644 --- a/translations/es/7-TimeSeries/1-Introduction/solution/R/README.md +++ b/translations/es/7-TimeSeries/1-Introduction/solution/R/README.md @@ -1,12 +1,3 @@ - --- diff --git a/translations/es/7-TimeSeries/2-ARIMA/README.md b/translations/es/7-TimeSeries/2-ARIMA/README.md index eeb8ad3fe..4dfa41243 100644 --- a/translations/es/7-TimeSeries/2-ARIMA/README.md +++ b/translations/es/7-TimeSeries/2-ARIMA/README.md @@ -1,12 +1,3 @@ - # Pronóstico de series temporales con ARIMA En la lección anterior, aprendiste un poco sobre el pronóstico de series temporales y cargaste un conjunto de datos que muestra las fluctuaciones de la carga eléctrica a lo largo de un período de tiempo. diff --git a/translations/es/7-TimeSeries/2-ARIMA/assignment.md b/translations/es/7-TimeSeries/2-ARIMA/assignment.md index 99aaccdc1..9bd6f1725 100644 --- a/translations/es/7-TimeSeries/2-ARIMA/assignment.md +++ b/translations/es/7-TimeSeries/2-ARIMA/assignment.md @@ -1,12 +1,3 @@ - # Un nuevo modelo ARIMA ## Instrucciones diff --git a/translations/es/7-TimeSeries/2-ARIMA/solution/Julia/README.md b/translations/es/7-TimeSeries/2-ARIMA/solution/Julia/README.md index a8cbb918b..7a0aaee20 100644 --- a/translations/es/7-TimeSeries/2-ARIMA/solution/Julia/README.md +++ b/translations/es/7-TimeSeries/2-ARIMA/solution/Julia/README.md @@ -1,12 +1,3 @@ - --- diff --git a/translations/es/7-TimeSeries/2-ARIMA/solution/R/README.md b/translations/es/7-TimeSeries/2-ARIMA/solution/R/README.md index 2f385d982..3e1c9dd06 100644 --- a/translations/es/7-TimeSeries/2-ARIMA/solution/R/README.md +++ b/translations/es/7-TimeSeries/2-ARIMA/solution/R/README.md @@ -1,12 +1,3 @@ - --- diff --git a/translations/es/7-TimeSeries/3-SVR/README.md b/translations/es/7-TimeSeries/3-SVR/README.md index e8dd91e5c..ac22e9f0c 100644 --- a/translations/es/7-TimeSeries/3-SVR/README.md +++ b/translations/es/7-TimeSeries/3-SVR/README.md @@ -1,12 +1,3 @@ - # Pronóstico de Series Temporales con Support Vector Regressor En la lección anterior, aprendiste a usar el modelo ARIMA para realizar predicciones de series temporales. Ahora explorarás el modelo Support Vector Regressor, que es un modelo de regresión utilizado para predecir datos continuos. diff --git a/translations/es/7-TimeSeries/3-SVR/assignment.md b/translations/es/7-TimeSeries/3-SVR/assignment.md index c664909f3..3d32e7328 100644 --- a/translations/es/7-TimeSeries/3-SVR/assignment.md +++ b/translations/es/7-TimeSeries/3-SVR/assignment.md @@ -1,12 +1,3 @@ - # Un nuevo modelo SVR ## Instrucciones [^1] diff --git a/translations/es/7-TimeSeries/README.md b/translations/es/7-TimeSeries/README.md index 66855bc8a..b3256d99b 100644 --- a/translations/es/7-TimeSeries/README.md +++ b/translations/es/7-TimeSeries/README.md @@ -1,12 +1,3 @@ - # Introducción a la predicción de series temporales ¿Qué es la predicción de series temporales? Se trata de predecir eventos futuros analizando las tendencias del pasado. diff --git a/translations/es/8-Reinforcement/1-QLearning/README.md b/translations/es/8-Reinforcement/1-QLearning/README.md index d57e68ade..bd18e2364 100644 --- a/translations/es/8-Reinforcement/1-QLearning/README.md +++ b/translations/es/8-Reinforcement/1-QLearning/README.md @@ -1,12 +1,3 @@ - # Introducción al Aprendizaje por Refuerzo y Q-Learning ![Resumen del refuerzo en el aprendizaje automático en un sketchnote](../../../../sketchnotes/ml-reinforcement.png) diff --git a/translations/es/8-Reinforcement/1-QLearning/assignment.md b/translations/es/8-Reinforcement/1-QLearning/assignment.md index 6d64468c9..8ff0bce48 100644 --- a/translations/es/8-Reinforcement/1-QLearning/assignment.md +++ b/translations/es/8-Reinforcement/1-QLearning/assignment.md @@ -1,12 +1,3 @@ - # Un Mundo Más Realista En nuestra situación, Peter podía moverse casi sin cansarse ni tener hambre. En un mundo más realista, tiene que sentarse y descansar de vez en cuando, y también alimentarse. Hagamos nuestro mundo más realista implementando las siguientes reglas: diff --git a/translations/es/8-Reinforcement/1-QLearning/solution/Julia/README.md b/translations/es/8-Reinforcement/1-QLearning/solution/Julia/README.md index 29e50d216..3e1c9dd06 100644 --- a/translations/es/8-Reinforcement/1-QLearning/solution/Julia/README.md +++ b/translations/es/8-Reinforcement/1-QLearning/solution/Julia/README.md @@ -1,12 +1,3 @@ - --- diff --git a/translations/es/8-Reinforcement/1-QLearning/solution/R/README.md b/translations/es/8-Reinforcement/1-QLearning/solution/R/README.md index b4a569165..3e1c9dd06 100644 --- a/translations/es/8-Reinforcement/1-QLearning/solution/R/README.md +++ b/translations/es/8-Reinforcement/1-QLearning/solution/R/README.md @@ -1,12 +1,3 @@ - --- diff --git a/translations/es/8-Reinforcement/2-Gym/README.md b/translations/es/8-Reinforcement/2-Gym/README.md index 95aa4a77b..dbaaf748e 100644 --- a/translations/es/8-Reinforcement/2-Gym/README.md +++ b/translations/es/8-Reinforcement/2-Gym/README.md @@ -1,12 +1,3 @@ - # CartPole Patinaje El problema que resolvimos en la lección anterior puede parecer un problema de juguete, sin mucha aplicación en escenarios de la vida real. Sin embargo, este no es el caso, ya que muchos problemas del mundo real comparten características similares, como jugar al ajedrez o al Go. Son similares porque también tenemos un tablero con reglas definidas y un **estado discreto**. diff --git a/translations/es/8-Reinforcement/2-Gym/assignment.md b/translations/es/8-Reinforcement/2-Gym/assignment.md index 7ab5037b7..260f17912 100644 --- a/translations/es/8-Reinforcement/2-Gym/assignment.md +++ b/translations/es/8-Reinforcement/2-Gym/assignment.md @@ -1,12 +1,3 @@ - # Entrenar Mountain Car [OpenAI Gym](http://gym.openai.com) ha sido diseñado de tal manera que todos los entornos proporcionan la misma API, es decir, los mismos métodos `reset`, `step` y `render`, y las mismas abstracciones de **espacio de acción** y **espacio de observación**. Por lo tanto, debería ser posible adaptar los mismos algoritmos de aprendizaje por refuerzo a diferentes entornos con cambios mínimos en el código. diff --git a/translations/es/8-Reinforcement/2-Gym/solution/Julia/README.md b/translations/es/8-Reinforcement/2-Gym/solution/Julia/README.md index 11228dcab..3e1c9dd06 100644 --- a/translations/es/8-Reinforcement/2-Gym/solution/Julia/README.md +++ b/translations/es/8-Reinforcement/2-Gym/solution/Julia/README.md @@ -1,12 +1,3 @@ - --- diff --git a/translations/es/8-Reinforcement/2-Gym/solution/R/README.md b/translations/es/8-Reinforcement/2-Gym/solution/R/README.md index 3c4820f7c..231af8f9e 100644 --- a/translations/es/8-Reinforcement/2-Gym/solution/R/README.md +++ b/translations/es/8-Reinforcement/2-Gym/solution/R/README.md @@ -1,12 +1,3 @@ - --- diff --git a/translations/es/8-Reinforcement/README.md b/translations/es/8-Reinforcement/README.md index cbdf08783..fd536a1c5 100644 --- a/translations/es/8-Reinforcement/README.md +++ b/translations/es/8-Reinforcement/README.md @@ -1,12 +1,3 @@ - # Introducción al aprendizaje por refuerzo El aprendizaje por refuerzo, RL, se considera uno de los paradigmas básicos del aprendizaje automático, junto con el aprendizaje supervisado y el aprendizaje no supervisado. RL trata sobre decisiones: tomar las decisiones correctas o, al menos, aprender de ellas. diff --git a/translations/es/9-Real-World/1-Applications/README.md b/translations/es/9-Real-World/1-Applications/README.md index 4e4f85966..5c790ec21 100644 --- a/translations/es/9-Real-World/1-Applications/README.md +++ b/translations/es/9-Real-World/1-Applications/README.md @@ -1,12 +1,3 @@ - # Posdata: Aprendizaje automático en el mundo real ![Resumen del aprendizaje automático en el mundo real en un sketchnote](../../../../sketchnotes/ml-realworld.png) diff --git a/translations/es/9-Real-World/1-Applications/assignment.md b/translations/es/9-Real-World/1-Applications/assignment.md index c7196f58e..323688479 100644 --- a/translations/es/9-Real-World/1-Applications/assignment.md +++ b/translations/es/9-Real-World/1-Applications/assignment.md @@ -1,12 +1,3 @@ - # Una búsqueda del tesoro de ML ## Instrucciones diff --git a/translations/es/9-Real-World/2-Debugging-ML-Models/README.md b/translations/es/9-Real-World/2-Debugging-ML-Models/README.md index 56a74bf00..fdefa6cb5 100644 --- a/translations/es/9-Real-World/2-Debugging-ML-Models/README.md +++ b/translations/es/9-Real-World/2-Debugging-ML-Models/README.md @@ -1,12 +1,3 @@ - # Posdata: Depuración de modelos de aprendizaje automático utilizando componentes del panel de IA Responsable ## [Cuestionario previo a la clase](https://ff-quizzes.netlify.app/en/ml/) diff --git a/translations/es/9-Real-World/2-Debugging-ML-Models/assignment.md b/translations/es/9-Real-World/2-Debugging-ML-Models/assignment.md index 8e9cfbdea..24ddd5d44 100644 --- a/translations/es/9-Real-World/2-Debugging-ML-Models/assignment.md +++ b/translations/es/9-Real-World/2-Debugging-ML-Models/assignment.md @@ -1,12 +1,3 @@ - # Explora el panel de Responsible AI (RAI) ## Instrucciones diff --git a/translations/es/9-Real-World/README.md b/translations/es/9-Real-World/README.md index 748231582..ad2d82d8b 100644 --- a/translations/es/9-Real-World/README.md +++ b/translations/es/9-Real-World/README.md @@ -1,12 +1,3 @@ - # Posdata: Aplicaciones reales del aprendizaje automático clásico En esta sección del currículo, se te presentarán algunas aplicaciones reales del aprendizaje automático clásico. Hemos investigado en internet para encontrar artículos y documentos técnicos sobre aplicaciones que han utilizado estas estrategias, evitando redes neuronales, aprendizaje profundo e inteligencia artificial tanto como sea posible. Aprende cómo se utiliza el aprendizaje automático en sistemas empresariales, aplicaciones ecológicas, finanzas, arte y cultura, entre otros. diff --git a/translations/es/AGENTS.md b/translations/es/AGENTS.md index 758dfc7cc..b36ab06d1 100644 --- a/translations/es/AGENTS.md +++ b/translations/es/AGENTS.md @@ -1,12 +1,3 @@ - # AGENTS.md ## Resumen del Proyecto diff --git a/translations/es/CODE_OF_CONDUCT.md b/translations/es/CODE_OF_CONDUCT.md index 88a45ecd7..057f8cff3 100644 --- a/translations/es/CODE_OF_CONDUCT.md +++ b/translations/es/CODE_OF_CONDUCT.md @@ -1,12 +1,3 @@ - # Código de Conducta de Código Abierto de Microsoft Este proyecto ha adoptado el [Código de Conducta de Código Abierto de Microsoft](https://opensource.microsoft.com/codeofconduct/). diff --git a/translations/es/CONTRIBUTING.md b/translations/es/CONTRIBUTING.md index 8c6fe7d80..977cee2eb 100644 --- a/translations/es/CONTRIBUTING.md +++ b/translations/es/CONTRIBUTING.md @@ -1,12 +1,3 @@ - # Contribuir Este proyecto da la bienvenida a contribuciones y sugerencias. La mayoría de las contribuciones requieren que aceptes un Acuerdo de Licencia de Contribuidor (CLA) declarando que tienes el derecho de, y efectivamente otorgas, los derechos para usar tu contribución. Para más detalles, visita https://cla.microsoft.com. diff --git a/translations/es/README.md b/translations/es/README.md index 7631f2a01..3565552b0 100644 --- a/translations/es/README.md +++ b/translations/es/README.md @@ -1,32 +1,23 @@ - -[![GitHub license](https://img.shields.io/github/license/microsoft/ML-For-Beginners.svg)](https://github.com/microsoft/ML-For-Beginners/blob/master/LICENSE) -[![GitHub contributors](https://img.shields.io/github/contributors/microsoft/ML-For-Beginners.svg)](https://GitHub.com/microsoft/ML-For-Beginners/graphs/contributors/) -[![GitHub issues](https://img.shields.io/github/issues/microsoft/ML-For-Beginners.svg)](https://GitHub.com/microsoft/ML-For-Beginners/issues/) -[![GitHub pull-requests](https://img.shields.io/github/issues-pr/microsoft/ML-For-Beginners.svg)](https://GitHub.com/microsoft/ML-For-Beginners/pulls/) -[![PRs Welcome](https://img.shields.io/badge/PRs-welcome-brightgreen.svg?style=flat-square)](http://makeapullrequest.com) - -[![GitHub watchers](https://img.shields.io/github/watchers/microsoft/ML-For-Beginners.svg?style=social&label=Watch)](https://GitHub.com/microsoft/ML-For-Beginners/watchers/) -[![GitHub forks](https://img.shields.io/github/forks/microsoft/ML-For-Beginners.svg?style=social&label=Fork)](https://GitHub.com/microsoft/ML-For-Beginners/network/) -[![GitHub stars](https://img.shields.io/github/stars/microsoft/ML-For-Beginners.svg?style=social&label=Star)](https://GitHub.com/microsoft/ML-For-Beginners/stargazers/) +[![Licencia de GitHub](https://img.shields.io/github/license/microsoft/ML-For-Beginners.svg)](https://github.com/microsoft/ML-For-Beginners/blob/master/LICENSE) +[![Colaboradores de GitHub](https://img.shields.io/github/contributors/microsoft/ML-For-Beginners.svg)](https://GitHub.com/microsoft/ML-For-Beginners/graphs/contributors/) +[![Incidencias de GitHub](https://img.shields.io/github/issues/microsoft/ML-For-Beginners.svg)](https://GitHub.com/microsoft/ML-For-Beginners/issues/) +[![Pull-requests de GitHub](https://img.shields.io/github/issues-pr/microsoft/ML-For-Beginners.svg)](https://GitHub.com/microsoft/ML-For-Beginners/pulls/) +[![PRs Bienvenidos](https://img.shields.io/badge/PRs-welcome-brightgreen.svg?style=flat-square)](http://makeapullrequest.com) + +[![Observadores de GitHub](https://img.shields.io/github/watchers/microsoft/ML-For-Beginners.svg?style=social&label=Watch)](https://GitHub.com/microsoft/ML-For-Beginners/watchers/) +[![Bifurcaciones de GitHub](https://img.shields.io/github/forks/microsoft/ML-For-Beginners.svg?style=social&label=Fork)](https://GitHub.com/microsoft/ML-For-Beginners/network/) +[![Estrellas de GitHub](https://img.shields.io/github/stars/microsoft/ML-For-Beginners.svg?style=social&label=Star)](https://GitHub.com/microsoft/ML-For-Beginners/stargazers/) ### 🌐 Soporte Multilingüe -#### Soportado vía GitHub Action (Automatizado y Siempre Actualizado) +#### Soportado mediante GitHub Action (Automatizado y Siempre Actualizado) -[Árabe](../ar/README.md) | [Bengalí](../bn/README.md) | [Búlgaro](../bg/README.md) | [Burmés (Myanmar)](../my/README.md) | [Chino (Simplificado)](../zh/README.md) | [Chino (Tradicional, Hong Kong)](../hk/README.md) | [Chino (Tradicional, Macao)](../mo/README.md) | [Chino (Tradicional, Taiwán)](../tw/README.md) | [Croata](../hr/README.md) | [Checo](../cs/README.md) | [Danés](../da/README.md) | [Neerlandés](../nl/README.md) | [Estonio](../et/README.md) | [Finlandés](../fi/README.md) | [Francés](../fr/README.md) | [Alemán](../de/README.md) | [Griego](../el/README.md) | [Hebreo](../he/README.md) | [Hindi](../hi/README.md) | [Húngaro](../hu/README.md) | [Indonesio](../id/README.md) | [Italiano](../it/README.md) | [Japonés](../ja/README.md) | [Kannada](../kn/README.md) | [Coreano](../ko/README.md) | [Lituano](../lt/README.md) | [Malayo](../ms/README.md) | [Malayalam](../ml/README.md) | [Maratí](../mr/README.md) | [Nepalí](../ne/README.md) | [Pidgin Nigeriano](../pcm/README.md) | 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Taiwán)](../zh-TW/README.md) | [Croata](../hr/README.md) | [Checo](../cs/README.md) | [Danés](../da/README.md) | [Neerlandés](../nl/README.md) | [Estonio](../et/README.md) | [Finlandés](../fi/README.md) | [Francés](../fr/README.md) | [Alemán](../de/README.md) | [Griego](../el/README.md) | [Hebreo](../he/README.md) | [Hindi](../hi/README.md) | [Húngaro](../hu/README.md) | [Indonesio](../id/README.md) | [Italiano](../it/README.md) | [Japonés](../ja/README.md) | [Kannada](../kn/README.md) | [Coreano](../ko/README.md) | [Lituano](../lt/README.md) | [Malayo](../ms/README.md) | [Malabar](../ml/README.md) | [Maratí](../mr/README.md) | [Nepalí](../ne/README.md) | [Pidgin Nigeriano](../pcm/README.md) | [Noruego](../no/README.md) | [Persa (Farsi)](../fa/README.md) | [Polaco](../pl/README.md) | [Portugués (Brasil)](../pt-BR/README.md) | [Portugués (Portugal)](../pt-PT/README.md) | [Punjabi (Gurmukhi)](../pa/README.md) | [Rumano](../ro/README.md) | [Ruso](../ru/README.md) | [Serbio (Cirílico)](../sr/README.md) | [Eslovaco](../sk/README.md) | [Esloveno](../sl/README.md) | [Español](./README.md) | [Suajili](../sw/README.md) | [Sueco](../sv/README.md) | [Tagalo (Filipino)](../tl/README.md) | [Tamil](../ta/README.md) | [Telugu](../te/README.md) | [Tailandés](../th/README.md) | [Turco](../tr/README.md) | [Ucraniano](../uk/README.md) | [Urdu](../ur/README.md) | [Vietnamita](../vi/README.md) -> **¿Prefieres Clonar Localmente?** +> **¿Prefieres clonar localmente?** -> Este repositorio incluye traducciones en más de 50 idiomas, lo que incrementa significativamente el tamaño de la descarga. Para clonar sin las traducciones, usa sparse checkout: +> Este repositorio incluye más de 50 traducciones de idiomas que aumentan significativamente el tamaño de la descarga. Para clonar sin traducciones, usa checkout esparcido: > ```bash > git clone --filter=blob:none --sparse https://github.com/microsoft/ML-For-Beginners.git > cd ML-For-Beginners @@ -39,58 +30,58 @@ CO_OP_TRANSLATOR_METADATA: [![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG) -Tenemos una serie en Discord sobre aprender con IA en curso, aprende más y únete en [Serie Aprende con IA](https://aka.ms/learnwithai/discord) del 18 al 30 de septiembre de 2025. Recibirás consejos y trucos sobre cómo usar GitHub Copilot para Ciencia de Datos. +Tenemos una serie en Discord para aprender con IA en curso, aprende más y únete en [Learn with AI Series](https://aka.ms/learnwithai/discord) del 18 al 30 de septiembre de 2025. Obtendrás consejos y trucos para usar GitHub Copilot en Ciencia de Datos. -![Serie Aprende con IA](../../../../translated_images/es/3.9b58fd8d6c373c20.webp) +![Serie Aprende con IA](../../translated_images/es/3.9b58fd8d6c373c20.webp) -# Aprendizaje Automático para Principiantes - Un Currículo +# Aprendizaje Automático para Principiantes - Un Plan de Estudios -> 🌍 Viaja alrededor del mundo mientras exploramos el Aprendizaje Automático a través de culturas del mundo 🌍 +> 🌍 Viaja por el mundo mientras exploramos el Aprendizaje Automático mediante culturas del mundo 🌍 -Los Cloud Advocates de Microsoft se complacen en ofrecer un currículo de 12 semanas y 26 lecciones sobre **Aprendizaje Automático**. En este currículo, aprenderás sobre lo que a veces se llama **aprendizaje automático clásico**, usando principalmente Scikit-learn como biblioteca y evitando el aprendizaje profundo, que se cubre en nuestro [currículo de IA para Principiantes](https://aka.ms/ai4beginners). ¡Combina estas lecciones con nuestro [currículo de Ciencia de Datos para Principiantes](https://aka.ms/ds4beginners)! +Los defensores de la nube en Microsoft se complacen en ofrecer un plan de estudios de 12 semanas y 26 lecciones sobre **Aprendizaje Automático**. En este plan de estudios, aprenderás sobre lo que a veces se llama **aprendizaje automático clásico**, usando principalmente Scikit-learn como biblioteca y evitando el aprendizaje profundo, que está cubierto en nuestro [plan de estudios de IA para Principiantes](https://aka.ms/ai4beginners). Combina estas lecciones con nuestro ['Ciencia de Datos para Principiantes'](https://aka.ms/ds4beginners), ¡también! -Viaja con nosotros por el mundo mientras aplicamos estas técnicas clásicas a datos de muchas regiones del mundo. Cada lección incluye cuestionarios antes y después, instrucciones escritas para completar la lección, una solución, una tarea y más. Nuestra pedagogía basada en proyectos te permite aprender construyendo, una forma comprobada para que las nuevas habilidades se "fijen". +Viaja con nosotros por todo el mundo mientras aplicamos estas técnicas clásicas a datos de muchas áreas del mundo. Cada lección incluye cuestionarios previos y posteriores, instrucciones escritas para completar la lección, una solución, una tarea, y más. Nuestra pedagogía basada en proyectos te permite aprender mientras construyes, una manera comprobada para que las nuevas habilidades se fijen. -**✍️ Un cálido agradecimiento a nuestros autores** Jen Looper, Stephen Howell, Francesca Lazzeri, Tomomi Imura, Cassie Breviu, Dmitry Soshnikov, Chris Noring, Anirban Mukherjee, Ornella Altunyan, Ruth Yakubu y Amy Boyd +**✍️ Un agradecimiento sincero a nuestros autores** Jen Looper, Stephen Howell, Francesca Lazzeri, Tomomi Imura, Cassie Breviu, Dmitry Soshnikov, Chris Noring, Anirban Mukherjee, Ornella Altunyan, Ruth Yakubu y Amy Boyd -**🎨 Gracias también a nuestros ilustradores** Tomomi Imura, Dasani Madipalli, y Jen Looper +**🎨 También gracias a nuestros ilustradores** Tomomi Imura, Dasani Madipalli, y Jen Looper -**🙏 Agradecimientos especiales 🙏 a nuestros Embajadores Estudiantiles de Microsoft autores, revisores y colaboradores de contenido**, en especial Rishit Dagli, Muhammad Sakib Khan Inan, Rohan Raj, Alexandru Petrescu, Abhishek Jaiswal, Nawrin Tabassum, Ioan Samuila, y Snigdha Agarwal +**🙏 Agradecimientos especiales 🙏 a nuestros autores, revisores y colaboradores de contenido Estudiantes Embajadores de Microsoft**, en especial Rishit Dagli, Muhammad Sakib Khan Inan, Rohan Raj, Alexandru Petrescu, Abhishek Jaiswal, Nawrin Tabassum, Ioan Samuila, y Snigdha Agarwal -**🤩 Gratitud extra a los Embajadores Estudiantiles de Microsoft Eric Wanjau, Jasleen Sondhi y Vidushi Gupta por nuestras lecciones en R!** +**🤩 Gratitud extra a los Estudiantes Embajadores de Microsoft Eric Wanjau, Jasleen Sondhi, y Vidushi Gupta por nuestras lecciones en R!** -# Primeros Pasos +# Comenzando Sigue estos pasos: -1. **Haz un fork del repositorio**: Haz clic en el botón "Fork" en la esquina superior derecha de esta página. -2. **Clona el repositorio**: `git clone https://github.com/microsoft/ML-For-Beginners.git` +1. **Haz Fork del Repositorio**: Haz clic en el botón "Fork" en la esquina superior derecha de esta página. +2. **Clona el Repositorio**: `git clone https://github.com/microsoft/ML-For-Beginners.git` > [encuentra todos los recursos adicionales para este curso en nuestra colección de Microsoft Learn](https://learn.microsoft.com/en-us/collections/qrqzamz1nn2wx3?WT.mc_id=academic-77952-bethanycheum) -> 🔧 **¿Necesitas ayuda?** Consulta nuestra [Guía de solución de problemas](TROUBLESHOOTING.md) para resolver problemas comunes con la instalación, configuración y ejecución de las lecciones. +> 🔧 **¿Necesitas ayuda?** Consulta nuestra [Guía de Solución de Problemas](TROUBLESHOOTING.md) para soluciones a problemas comunes con la instalación, configuración y ejecución de lecciones. -**[Estudiantes](https://aka.ms/student-page)**, para usar este currículo, haz fork de todo el repositorio a tu propia cuenta de GitHub y completa los ejercicios por tu cuenta o en grupo: +**[Estudiantes](https://aka.ms/student-page)**, para usar este plan de estudios, haz fork del repositorio completo en tu propia cuenta de GitHub y completa los ejercicios por tu cuenta o con un grupo: - Comienza con un cuestionario previo a la lección. -- Lee la lección y completa las actividades, haciendo pausas y reflexionando en cada evaluación de conocimiento. -- Intenta crear los proyectos comprendiendo las lecciones en lugar de ejecutar directamente el código solución; sin embargo, ese código está disponible en las carpetas `/solution` de cada lección orientada a proyecto. +- Lee la lección y completa las actividades, pausando y reflexionando en cada chequeo de conocimiento. +- Intenta crear los proyectos comprendiendo las lecciones en lugar de ejecutar el código de solución; sin embargo, ese código está disponible en las carpetas `/solution` en cada lección orientada a proyectos. - Realiza el cuestionario posterior a la lección. - Completa el desafío. - Completa la tarea. -- Después de completar un grupo de lecciones, visita el [Foro de Discusión](https://github.com/microsoft/ML-For-Beginners/discussions) y "aprende en voz alta" llenando la rúbrica PAT correspondiente. Un 'PAT' es una Herramienta de Evaluación de Progreso, una rúbrica que completas para avanzar en tu aprendizaje. También puedes interactuar con otras PATs para que aprendamos juntos. +- Después de completar un grupo de lecciones, visita el [Foro de Discusión](https://github.com/microsoft/ML-For-Beginners/discussions) y "aprende en voz alta" completando la rúbrica PAT correspondiente. Un 'PAT' es una Herramienta de Evaluación de Progreso que es una rúbrica que completas para profundizar tu aprendizaje. También puedes reaccionar a otros PATs para que aprendamos juntos. -> Para estudio adicional, recomendamos seguir estos módulos y rutas de aprendizaje de [Microsoft Learn](https://docs.microsoft.com/en-us/users/jenlooper-2911/collections/k7o7tg1gp306q4?WT.mc_id=academic-77952-leestott). +> Para estudios adicionales, recomendamos seguir estos módulos y rutas de aprendizaje de [Microsoft Learn](https://docs.microsoft.com/en-us/users/jenlooper-2911/collections/k7o7tg1gp306q4?WT.mc_id=academic-77952-leestott). -**Profesores**, hemos [incluido algunas sugerencias](for-teachers.md) sobre cómo usar este currículo. +**Profesores**, hemos [incluido algunas sugerencias](for-teachers.md) sobre cómo usar este plan de estudios. --- ## Videos explicativos -Algunas de las lecciones están disponibles en formato video corto. Puedes encontrar todos estos videos integrados en las lecciones o en la [lista de reproducción ML for Beginners en el canal de Microsoft Developer en YouTube](https://aka.ms/ml-beginners-videos) haciendo clic en la imagen abajo. +Algunas de las lecciones están disponibles en formato de video corto. Puedes encontrar todos estos en línea en las lecciones, o en la [lista de reproducción ML para Principiantes en el canal de Microsoft Developer en YouTube](https://aka.ms/ml-beginners-videos) haciendo clic en la imagen a continuación. -[![Banner ML for beginners](../../../../translated_images/es/ml-for-beginners-video-banner.63f694a100034bc6.webp)](https://aka.ms/ml-beginners-videos) +[![Banner de ML para principiantes](../../translated_images/es/ml-for-beginners-video-banner.63f694a100034bc6.webp)](https://aka.ms/ml-beginners-videos) --- @@ -100,79 +91,79 @@ Algunas de las lecciones están disponibles en formato video corto. Puedes encon **Gif por** [Mohit Jaisal](https://linkedin.com/in/mohitjaisal) -> 🎥 ¡Haz clic en la imagen de arriba para ver un video sobre el proyecto y las personas que lo crearon! +> 🎥 ¡Haz clic en la imagen de arriba para un video sobre el proyecto y las personas que lo crearon! --- ## Pedagogía -Hemos elegido dos principios pedagógicos al construir este currículo: asegurar que sea práctico y **basado en proyectos** y que incluya **cuestionarios frecuentes**. Además, este currículo tiene un **tema** común para darle cohesión. +Hemos elegido dos principios pedagógicos al construir este plan de estudios: asegurar que sea práctico y **basado en proyectos** y que incluya **cuestionarios frecuentes**. Además, este plan de estudios tiene un **tema común** para darle cohesión. -Al asegurar que el contenido se alinee con proyectos, el proceso se vuelve más atractivo para los estudiantes y la retención de conceptos se aumenta. Además, un cuestionario de bajo riesgo antes de la clase establece la intención del estudiante para aprender un tema, mientras que un segundo cuestionario después de la clase asegura una mayor retención. Este currículo fue diseñado para ser flexible y divertido y puede tomarse completo o en partes. Los proyectos empiezan pequeños y se vuelven progresivamente más complejos al final del ciclo de 12 semanas. Este currículo también incluye un posfacio sobre aplicaciones reales de ML, que puede usarse como crédito adicional o como base para discusión. +Al garantizar que el contenido esté alineado con proyectos, el proceso se vuelve más atractivo para los estudiantes y se aumenta la retención de conceptos. Además, un cuestionario de bajo impacto antes de la clase establece la intención del estudiante hacia el aprendizaje de un tema, mientras que un segundo cuestionario después de la clase asegura una retención adicional. Este plan de estudios fue diseñado para ser flexible y divertido y puede tomarse en su totalidad o en partes. Los proyectos comienzan pequeños y se vuelven cada vez más complejos hasta el final del ciclo de 12 semanas. Este plan de estudios también incluye un posfacio sobre aplicaciones reales de ML, que puede usarse como crédito extra o como base para discusión. -> Consulta nuestro [Código de Conducta](CODE_OF_CONDUCT.md), [Contribuciones](CONTRIBUTING.md), [Traducción](TRANSLATIONS.md) y [Solución de Problemas](TROUBLESHOOTING.md). ¡Agradecemos tus comentarios constructivos! +> Consulta nuestras guías de [Código de Conducta](CODE_OF_CONDUCT.md), [Contribuciones](CONTRIBUTING.md), [Traducción](TRANSLATIONS.md) y [Solución de Problemas](TROUBLESHOOTING.md). ¡Agradecemos tus comentarios constructivos! ## Cada lección incluye -- sketchnote opcional -- video complementario opcional -- vídeo explicativo (solo algunas lecciones) -- [cuestionario previo a la lección](https://ff-quizzes.netlify.app/en/ml/) +- sketchnotes opcionales +- vídeo complementario opcional +- video explicativo (solo algunas lecciones) +- [cuestionario previo de calentamiento](https://ff-quizzes.netlify.app/en/ml/) - lección escrita -- para lecciones basadas en proyectos, guías paso a paso para construir el proyecto -- evaluaciones de conocimiento +- para lecciones basadas en proyectos, guías paso a paso de cómo construir el proyecto +- chequeos de conocimiento - un desafío -- lectura complementaria +- lectura suplementaria - tarea - [cuestionario posterior a la lección](https://ff-quizzes.netlify.app/en/ml/) -> **Una nota sobre los idiomas**: Estas lecciones están escritas principalmente en Python, pero muchas también están disponibles en R. Para completar una lección en R, dirígete a la carpeta `/solution` y busca las lecciones en R. Incluyen una extensión .rmd que representa un archivo **R Markdown** que puede definirse simplemente como una integración de `fragmentos de código` (de R u otros lenguajes) y un `encabezado YAML` (que guía cómo formatear salidas como PDF) en un `documento Markdown`. Por tanto, sirve como un marco ejemplar para la autoría en ciencia de datos ya que permite combinar tu código, su salida y tus reflexiones escribiéndolas en Markdown. Además, los documentos R Markdown pueden renderizarse a formatos de salida como PDF, HTML o Word. -> **Una nota sobre los cuestionarios**: Todos los cuestionarios están contenidos en la [carpeta Quiz App](../../quiz-app), con 52 cuestionarios en total de tres preguntas cada uno. Están enlazados desde las lecciones, pero la aplicación de cuestionarios puede ejecutarse localmente; siga las instrucciones en la carpeta `quiz-app` para alojar localmente o desplegar en Azure. - -| Número de Lección | Tema | Agrupación de Lecciones | Objetivos de Aprendizaje | Lección Enlazada | Autor | -| :----------------: | :------------------------------------------------------------: | :---------------------------------------------------------: | ----------------------------------------------------------------------------------------------------------------------------------- | :--------------------------------------------------------------------------------------------------------------------------------------: | :------------------------------------------------: | -| 01 | Introducción al aprendizaje automático | [Introducción](1-Introduction/README.md) | Aprenda los conceptos básicos detrás del aprendizaje automático | [Lección](1-Introduction/1-intro-to-ML/README.md) | Muhammad | -| 02 | Historia del aprendizaje automático | [Introducción](1-Introduction/README.md) | Aprenda la historia que subyace en este campo | [Lección](1-Introduction/2-history-of-ML/README.md) | Jen y Amy | -| 03 | Equidad y aprendizaje automático | [Introducción](1-Introduction/README.md) | ¿Cuáles son los problemas filosóficos importantes sobre la equidad que los estudiantes deben considerar al construir y aplicar modelos de ML? | [Lección](1-Introduction/3-fairness/README.md) | Tomomi | -| 04 | Técnicas para el aprendizaje automático | [Introducción](1-Introduction/README.md) | ¿Qué técnicas usan los investigadores de ML para construir modelos de ML? | [Lección](1-Introduction/4-techniques-of-ML/README.md) | Chris y Jen | -| 05 | Introducción a la regresión | [Regresión](2-Regression/README.md) | Comience con Python y Scikit-learn para modelos de regresión | [Python](2-Regression/1-Tools/README.md) • [R](../../2-Regression/1-Tools/solution/R/lesson_1.html) | Jen • Eric Wanjau | -| 06 | Precios de calabazas en Norteamérica 🎃 | [Regresión](2-Regression/README.md) | Visualice y limpie datos en preparación para ML | [Python](2-Regression/2-Data/README.md) • [R](../../2-Regression/2-Data/solution/R/lesson_2.html) | Jen • Eric Wanjau | -| 07 | Precios de calabazas en Norteamérica 🎃 | [Regresión](2-Regression/README.md) | Construya modelos de regresión lineal y polinómica | [Python](2-Regression/3-Linear/README.md) • [R](../../2-Regression/3-Linear/solution/R/lesson_3.html) | Jen y Dmitry • Eric Wanjau | -| 08 | Precios de calabazas en Norteamérica 🎃 | [Regresión](2-Regression/README.md) | Construya un modelo de regresión logística | [Python](2-Regression/4-Logistic/README.md) • [R](../../2-Regression/4-Logistic/solution/R/lesson_4.html) | Jen • Eric Wanjau | -| 09 | Una aplicación web 🔌 | [Aplicación web](3-Web-App/README.md) | Construya una aplicación web para usar su modelo entrenado | [Python](3-Web-App/1-Web-App/README.md) | Jen | -| 10 | Introducción a la clasificación | [Clasificación](4-Classification/README.md) | Limpie, prepare y visualice sus datos; introducción a la clasificación | [Python](4-Classification/1-Introduction/README.md) • [R](../../4-Classification/1-Introduction/solution/R/lesson_10.html) | Jen y Cassie • Eric Wanjau | -| 11 | Deliciosas cocinas asiáticas e indias 🍜 | [Clasificación](4-Classification/README.md) | Introducción a los clasificadores | [Python](4-Classification/2-Classifiers-1/README.md) • [R](../../4-Classification/2-Classifiers-1/solution/R/lesson_11.html) | Jen y Cassie • Eric Wanjau | -| 12 | Deliciosas cocinas asiáticas e indias 🍜 | [Clasificación](4-Classification/README.md) | Más clasificadores | [Python](4-Classification/3-Classifiers-2/README.md) • [R](../../4-Classification/3-Classifiers-2/solution/R/lesson_12.html) | Jen y Cassie • Eric Wanjau | -| 13 | Deliciosas cocinas asiáticas e indias 🍜 | [Clasificación](4-Classification/README.md) | Construya una aplicación web recomendadora usando su modelo | [Python](4-Classification/4-Applied/README.md) | Jen | -| 14 | Introducción a clustering | [Clustering](5-Clustering/README.md) | Limpie, prepare y visualice sus datos; introducción al clustering | [Python](5-Clustering/1-Visualize/README.md) • [R](../../5-Clustering/1-Visualize/solution/R/lesson_14.html) | Jen • Eric Wanjau | -| 15 | Explorando gustos musicales nigerianos 🎧 | [Clustering](5-Clustering/README.md) | Explore el método de clustering K-Means | [Python](5-Clustering/2-K-Means/README.md) • [R](../../5-Clustering/2-K-Means/solution/R/lesson_15.html) | Jen • Eric Wanjau | -| 16 | Introducción al procesamiento de lenguaje natural ☕️ | [Procesamiento de lenguaje natural](6-NLP/README.md) | Aprenda los conceptos básicos sobre PLN construyendo un bot simple | [Python](6-NLP/1-Introduction-to-NLP/README.md) | Stephen | -| 17 | Tareas comunes de PLN ☕️ | [Procesamiento de lenguaje natural](6-NLP/README.md) | Profundice su conocimiento de PLN entendiendo tareas comunes necesarias al tratar con estructuras del lenguaje | [Python](6-NLP/2-Tasks/README.md) | Stephen | -| 18 | Traducción y análisis de sentimientos ♥️ | [Procesamiento de lenguaje natural](6-NLP/README.md) | Traducción y análisis de sentimientos con Jane Austen | [Python](6-NLP/3-Translation-Sentiment/README.md) | Stephen | -| 19 | Hoteles románticos de Europa ♥️ | [Procesamiento de lenguaje natural](6-NLP/README.md) | Análisis de sentimientos con reseñas de hoteles 1 | [Python](6-NLP/4-Hotel-Reviews-1/README.md) | Stephen | -| 20 | Hoteles románticos de Europa ♥️ | [Procesamiento de lenguaje natural](6-NLP/README.md) | Análisis de sentimientos con reseñas de hoteles 2 | [Python](6-NLP/5-Hotel-Reviews-2/README.md) | Stephen | -| 21 | Introducción a la predicción de series temporales | [Series temporales](7-TimeSeries/README.md) | Introducción a la predicción de series temporales | [Python](7-TimeSeries/1-Introduction/README.md) | Francesca | -| 22 | ⚡️ Uso mundial de energía ⚡️ - predicción de series temporales con ARIMA | [Series temporales](7-TimeSeries/README.md) | Predicción de series temporales con ARIMA | [Python](7-TimeSeries/2-ARIMA/README.md) | Francesca | -| 23 | ⚡️ Uso mundial de energía ⚡️ - predicción de series temporales con SVR | [Series temporales](7-TimeSeries/README.md) | Predicción de series temporales con Support Vector Regressor | [Python](7-TimeSeries/3-SVR/README.md) | Anirban | -| 24 | Introducción al aprendizaje por refuerzo | [Aprendizaje por refuerzo](8-Reinforcement/README.md) | Introducción al aprendizaje por refuerzo con Q-Learning | [Python](8-Reinforcement/1-QLearning/README.md) | Dmitry | -| 25 | ¡Ayuda a Peter a evitar al lobo! 🐺 | [Aprendizaje por refuerzo](8-Reinforcement/README.md) | Aprendizaje por refuerzo Gym | [Python](8-Reinforcement/2-Gym/README.md) | Dmitry | -| Epílogo | Escenarios y aplicaciones reales de ML | [ML en el mundo real](9-Real-World/README.md) | Aplicaciones interesantes y reveladoras en la vida real del ML clásico | [Lección](9-Real-World/1-Applications/README.md) | Equipo | -| Epílogo | Depuración de modelos en ML usando el panel de RAI | [ML en el mundo real](9-Real-World/README.md) | Depuración de modelos en aprendizaje automático usando componentes del panel Responsible AI | [Lección](9-Real-World/2-Debugging-ML-Models/README.md) | Ruth Yakubu | - -> [encuentre todos los recursos adicionales para este curso en nuestra colección de Microsoft Learn](https://learn.microsoft.com/en-us/collections/qrqzamz1nn2wx3?WT.mc_id=academic-77952-bethanycheum) +> **Una nota sobre los idiomas**: Estas lecciones están escritas principalmente en Python, pero muchas también están disponibles en R. Para completar una lección en R, ve a la carpeta `/solution` y busca las lecciones en R. Incluyen una extensión .rmd que representa un archivo **R Markdown**, que puede definirse simplemente como una combinación de `fragmentos de código` (de R u otros lenguajes) y un `encabezado YAML` (que guía cómo formatear salidas como PDF) en un `documento Markdown`. Como tal, sirve como un marco ejemplar para la autoría en ciencia de datos, ya que te permite combinar tu código, su salida y tus pensamientos permitiéndote escribirlos en Markdown. Además, los documentos R Markdown pueden convertirse a formatos de salida como PDF, HTML o Word. +> **Una nota sobre los cuestionarios**: Todos los cuestionarios están contenidos en la [carpeta Quiz App](../../quiz-app), con un total de 52 cuestionarios de tres preguntas cada uno. Se enlazan desde las lecciones, pero la aplicación del cuestionario se puede ejecutar localmente; siga las instrucciones en la carpeta `quiz-app` para alojar localmente o desplegar en Azure. + +| Número de lección | Tema | Grupo de lecciones | Objetivos de aprendizaje | Lección enlazada | Autor | +| :----------------: | :------------------------------------------------------------: | :----------------------------------------------------: | ---------------------------------------------------------------------------------------------------------------------------------- | :----------------------------------------------------------------------------------------------------------------------------------------: | :------------------------------------------------: | +| 01 | Introducción al aprendizaje automático | [Introducción](1-Introduction/README.md) | Aprender los conceptos básicos detrás del aprendizaje automático | [Lección](1-Introduction/1-intro-to-ML/README.md) | Muhammad | +| 02 | La historia del aprendizaje automático | [Introducción](1-Introduction/README.md) | Aprender la historia que subyace a este campo | [Lección](1-Introduction/2-history-of-ML/README.md) | Jen y Amy | +| 03 | Justicia y aprendizaje automático | [Introducción](1-Introduction/README.md) | ¿Cuáles son los problemas filosóficos importantes sobre la justicia que los estudiantes deben considerar al construir y aplicar modelos de ML? | [Lección](1-Introduction/3-fairness/README.md) | Tomomi | +| 04 | Técnicas para aprendizaje automático | [Introducción](1-Introduction/README.md) | ¿Qué técnicas usan los investigadores de ML para construir modelos de aprendizaje automático? | [Lección](1-Introduction/4-techniques-of-ML/README.md) | Chris y Jen | +| 05 | Introducción a la regresión | [Regresión](2-Regression/README.md) | Comenzar con Python y Scikit-learn para modelos de regresión | [Python](2-Regression/1-Tools/README.md) • [R](../../2-Regression/1-Tools/solution/R/lesson_1.html) | Jen • Eric Wanjau | +| 06 | Precios de calabaza en Norteamérica 🎃 | [Regresión](2-Regression/README.md) | Visualizar y limpiar datos en preparación para el ML | [Python](2-Regression/2-Data/README.md) • [R](../../2-Regression/2-Data/solution/R/lesson_2.html) | Jen • Eric Wanjau | +| 07 | Precios de calabaza en Norteamérica 🎃 | [Regresión](2-Regression/README.md) | Construir modelos de regresión lineal y polinómica | [Python](2-Regression/3-Linear/README.md) • [R](../../2-Regression/3-Linear/solution/R/lesson_3.html) | Jen y Dmitry • Eric Wanjau | +| 08 | Precios de calabaza en Norteamérica 🎃 | [Regresión](2-Regression/README.md) | Construir un modelo de regresión logística | [Python](2-Regression/4-Logistic/README.md) • [R](../../2-Regression/4-Logistic/solution/R/lesson_4.html) | Jen • Eric Wanjau | +| 09 | Una aplicación web 🔌 | [Aplicación web](3-Web-App/README.md) | Construir una aplicación web para usar tu modelo entrenado | [Python](3-Web-App/1-Web-App/README.md) | Jen | +| 10 | Introducción a la clasificación | [Clasificación](4-Classification/README.md) | Limpiar, preparar y visualizar tus datos; introducción a la clasificación | [Python](4-Classification/1-Introduction/README.md) • [R](../../4-Classification/1-Introduction/solution/R/lesson_10.html) | Jen y Cassie • Eric Wanjau | +| 11 | Deliciosas cocinas asiáticas e indias 🍜 | [Clasificación](4-Classification/README.md) | Introducción a los clasificadores | [Python](4-Classification/2-Classifiers-1/README.md) • [R](../../4-Classification/2-Classifiers-1/solution/R/lesson_11.html) | Jen y Cassie • Eric Wanjau | +| 12 | Deliciosas cocinas asiáticas e indias 🍜 | [Clasificación](4-Classification/README.md) | Más clasificadores | [Python](4-Classification/3-Classifiers-2/README.md) • [R](../../4-Classification/3-Classifiers-2/solution/R/lesson_12.html) | Jen y Cassie • Eric Wanjau | +| 13 | Deliciosas cocinas asiáticas e indias 🍜 | [Clasificación](4-Classification/README.md) | Construir una aplicación web recomendadora usando tu modelo | [Python](4-Classification/4-Applied/README.md) | Jen | +| 14 | Introducción al agrupamiento | [Agrupamiento](5-Clustering/README.md) | Limpiar, preparar y visualizar tus datos; introducción al agrupamiento | [Python](5-Clustering/1-Visualize/README.md) • [R](../../5-Clustering/1-Visualize/solution/R/lesson_14.html) | Jen • Eric Wanjau | +| 15 | Explorando gustos musicales nigerianos 🎧 | [Agrupamiento](5-Clustering/README.md) | Explorar el método de agrupamiento K-Means | [Python](5-Clustering/2-K-Means/README.md) • [R](../../5-Clustering/2-K-Means/solution/R/lesson_15.html) | Jen • Eric Wanjau | +| 16 | Introducción al procesamiento de lenguaje natural ☕️ | [Procesamiento de lenguaje natural](6-NLP/README.md) | Aprender lo básico sobre PLN construyendo un bot sencillo | [Python](6-NLP/1-Introduction-to-NLP/README.md) | Stephen | +| 17 | Tareas comunes en PLN ☕️ | [Procesamiento de lenguaje natural](6-NLP/README.md) | Profundizar tu conocimiento en PLN entendiendo las tareas comunes requeridas al tratar con estructuras lingüísticas | [Python](6-NLP/2-Tasks/README.md) | Stephen | +| 18 | Traducción y análisis de sentimiento ♥️ | [Procesamiento de lenguaje natural](6-NLP/README.md) | Traducción y análisis de sentimiento con Jane Austen | [Python](6-NLP/3-Translation-Sentiment/README.md) | Stephen | +| 19 | Hoteles románticos de Europa ♥️ | [Procesamiento de lenguaje natural](6-NLP/README.md) | Análisis de sentimiento con reseñas de hoteles 1 | [Python](6-NLP/4-Hotel-Reviews-1/README.md) | Stephen | +| 20 | Hoteles románticos de Europa ♥️ | [Procesamiento de lenguaje natural](6-NLP/README.md) | Análisis de sentimiento con reseñas de hoteles 2 | [Python](6-NLP/5-Hotel-Reviews-2/README.md) | Stephen | +| 21 | Introducción a la predicción de series temporales | [Series temporales](7-TimeSeries/README.md) | Introducción a la predicción de series temporales | [Python](7-TimeSeries/1-Introduction/README.md) | Francesca | +| 22 | ⚡️ Uso mundial de energía ⚡️ - predicción de series temporales con ARIMA | [Series temporales](7-TimeSeries/README.md) | Predicción de series temporales con ARIMA | [Python](7-TimeSeries/2-ARIMA/README.md) | Francesca | +| 23 | ⚡️ Uso mundial de energía ⚡️ - predicción de series temporales con SVR | [Series temporales](7-TimeSeries/README.md) | Predicción de series temporales con regresor de vectores soporte (SVR) | [Python](7-TimeSeries/3-SVR/README.md) | Anirban | +| 24 | Introducción al aprendizaje por refuerzo | [Aprendizaje por refuerzo](8-Reinforcement/README.md) | Introducción a aprendizaje por refuerzo con Q-Learning | [Python](8-Reinforcement/1-QLearning/README.md) | Dmitry | +| 25 | Ayuda a Peter a evitar al lobo! 🐺 | [Aprendizaje por refuerzo](8-Reinforcement/README.md) | Aprendizaje por refuerzo Gym | [Python](8-Reinforcement/2-Gym/README.md) | Dmitry | +| Posdata | Escenarios y aplicaciones reales de ML | [ML en el mundo real](9-Real-World/README.md) | Aplicaciones interesantes y reveladoras del ML clásico | [Lección](9-Real-World/1-Applications/README.md) | Equipo | +| Posdata | Depuración de modelos en ML usando el panel RAI | [ML en el mundo real](9-Real-World/README.md) | Depuración de modelos en Machine Learning usando componentes del panel de AI responsable | [Lección](9-Real-World/2-Debugging-ML-Models/README.md) | Ruth Yakubu | + +> [encuentra todos los recursos adicionales para este curso en nuestra colección Microsoft Learn](https://learn.microsoft.com/en-us/collections/qrqzamz1nn2wx3?WT.mc_id=academic-77952-bethanycheum) ## Acceso sin conexión -Puede ejecutar esta documentación sin conexión utilizando [Docsify](https://docsify.js.org/#/). Haga un fork de este repositorio, [instale Docsify](https://docsify.js.org/#/quickstart) en su máquina local y luego, en la carpeta raíz de este repositorio, escriba `docsify serve`. El sitio web se servirá en el puerto 3000 en su localhost: `localhost:3000`. +Puedes ejecutar esta documentación sin conexión usando [Docsify](https://docsify.js.org/#/). Haz un fork de este repositorio, [instala Docsify](https://docsify.js.org/#/quickstart) en tu máquina local y luego, en la carpeta raíz de este repositorio, escribe `docsify serve`. El sitio web se servirá en el puerto 3000 en tu localhost: `localhost:3000`. ## PDFs -Encuentre un pdf del currículo con enlaces [aquí](https://microsoft.github.io/ML-For-Beginners/pdf/readme.pdf). +Encuentra un pdf del currículo con enlaces [aquí](https://microsoft.github.io/ML-For-Beginners/pdf/readme.pdf). ## 🎒 Otros cursos -¡Nuestro equipo produce otros cursos! Eche un vistazo: +¡Nuestro equipo produce otros cursos! Consulta: ### LangChain @@ -185,11 +176,11 @@ Encuentre un pdf del currículo con enlaces [aquí](https://microsoft.github.io/ [![AZD para principiantes](https://img.shields.io/badge/AZD%20for%20Beginners-0078D4?style=for-the-badge&labelColor=E5E7EB&color=0078D4)](https://github.com/microsoft/AZD-for-beginners?WT.mc_id=academic-105485-koreyst) [![Edge AI para principiantes](https://img.shields.io/badge/Edge%20AI%20for%20Beginners-00B8E4?style=for-the-badge&labelColor=E5E7EB&color=00B8E4)](https://github.com/microsoft/edgeai-for-beginners?WT.mc_id=academic-105485-koreyst) [![MCP para principiantes](https://img.shields.io/badge/MCP%20for%20Beginners-009688?style=for-the-badge&labelColor=E5E7EB&color=009688)](https://github.com/microsoft/mcp-for-beginners?WT.mc_id=academic-105485-koreyst) -[![Agentes de IA para principiantes](https://img.shields.io/badge/AI%20Agents%20for%20Beginners-00C49A?style=for-the-badge&labelColor=E5E7EB&color=00C49A)](https://github.com/microsoft/ai-agents-for-beginners?WT.mc_id=academic-105485-koreyst) +[![Agentes AI para principiantes](https://img.shields.io/badge/AI%20Agents%20for%20Beginners-00C49A?style=for-the-badge&labelColor=E5E7EB&color=00C49A)](https://github.com/microsoft/ai-agents-for-beginners?WT.mc_id=academic-105485-koreyst) --- -### Serie de IA generativa +### Serie de IA Generativa [![IA Generativa para Principiantes](https://img.shields.io/badge/Generative%20AI%20for%20Beginners-8B5CF6?style=for-the-badge&labelColor=E5E7EB&color=8B5CF6)](https://github.com/microsoft/generative-ai-for-beginners?WT.mc_id=academic-105485-koreyst) [![IA Generativa (.NET)](https://img.shields.io/badge/Generative%20AI%20(.NET)-9333EA?style=for-the-badge&labelColor=E5E7EB&color=9333EA)](https://github.com/microsoft/Generative-AI-for-beginners-dotnet?WT.mc_id=academic-105485-koreyst) [![IA Generativa (Java)](https://img.shields.io/badge/Generative%20AI%20(Java)-C084FC?style=for-the-badge&labelColor=E5E7EB&color=C084FC)](https://github.com/microsoft/generative-ai-for-beginners-java?WT.mc_id=academic-105485-koreyst) @@ -197,7 +188,7 @@ Encuentre un pdf del currículo con enlaces [aquí](https://microsoft.github.io/ --- -### Aprendizaje Fundamental +### Aprendizaje Central [![ML para Principiantes](https://img.shields.io/badge/ML%20for%20Beginners-22C55E?style=for-the-badge&labelColor=E5E7EB&color=22C55E)](https://aka.ms/ml-beginners?WT.mc_id=academic-105485-koreyst) [![Ciencia de Datos para Principiantes](https://img.shields.io/badge/Data%20Science%20for%20Beginners-84CC16?style=for-the-badge&labelColor=E5E7EB&color=84CC16)](https://aka.ms/datascience-beginners?WT.mc_id=academic-105485-koreyst) [![IA para Principiantes](https://img.shields.io/badge/AI%20for%20Beginners-A3E635?style=for-the-badge&labelColor=E5E7EB&color=A3E635)](https://aka.ms/ai-beginners?WT.mc_id=academic-105485-koreyst) @@ -216,17 +207,17 @@ Encuentre un pdf del currículo con enlaces [aquí](https://microsoft.github.io/ ## Obtener ayuda -Si te quedas atascado o tienes alguna pregunta sobre cómo construir aplicaciones de IA, únete a otros estudiantes y desarrolladores experimentados en discusiones sobre MCP. Es una comunidad de apoyo donde las preguntas son bienvenidas y el conocimiento se comparte libremente. +Si te quedas atascado o tienes alguna pregunta sobre cómo construir aplicaciones de IA, únete a otros aprendices y desarrolladores experimentados en discusiones sobre MCP. Es una comunidad de apoyo donde las preguntas son bienvenidas y el conocimiento se comparte libremente. -[![Discord de Microsoft Foundry](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG) +[![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG) -Si tienes comentarios sobre el producto o encuentras errores mientras desarrollas, visita: +Si tienes comentarios sobre el producto o errores mientras construyes, visita: -[![Foro de Desarrolladores de Microsoft Foundry](https://img.shields.io/badge/GitHub-Microsoft_Foundry_Developer_Forum-blue?style=for-the-badge&logo=github&color=000000&logoColor=fff)](https://aka.ms/foundry/forum) +[![Microsoft Foundry Developer Forum](https://img.shields.io/badge/GitHub-Microsoft_Foundry_Developer_Forum-blue?style=for-the-badge&logo=github&color=000000&logoColor=fff)](https://aka.ms/foundry/forum) --- -**Aviso Legal**: -Este documento ha sido traducido utilizando el servicio de traducción automática [Co-op Translator](https://github.com/Azure/co-op-translator). Aunque nos esforzamos por la precisión, tenga en cuenta que las traducciones automatizadas pueden contener errores o inexactitudes. El documento original en su idioma nativo debe considerarse la fuente autorizada. Para información crítica, se recomienda una traducción profesional realizada por humanos. No nos responsabilizamos de ningún malentendido o interpretación incorrecta que pueda surgir del uso de esta traducción. +**Aviso Legal**: +Este documento ha sido traducido utilizando el servicio de traducción automática [Co-op Translator](https://github.com/Azure/co-op-translator). Aunque nos esforzamos por lograr precisión, tenga en cuenta que las traducciones automáticas pueden contener errores o inexactitudes. El documento original en su idioma nativo debe considerarse la fuente autorizada. Para información crítica, se recomienda traducción profesional realizada por un humano. No nos hacemos responsables por malentendidos o interpretaciones erróneas derivados del uso de esta traducción. \ No newline at end of file diff --git a/translations/es/SECURITY.md b/translations/es/SECURITY.md index 17725bc55..acc7027c1 100644 --- a/translations/es/SECURITY.md +++ b/translations/es/SECURITY.md @@ -1,12 +1,3 @@ - ## Seguridad Microsoft se toma muy en serio la seguridad de nuestros productos y servicios de software, lo que incluye todos los repositorios de código fuente gestionados a través de nuestras organizaciones de GitHub, que incluyen [Microsoft](https://github.com/Microsoft), [Azure](https://github.com/Azure), [DotNet](https://github.com/dotnet), [AspNet](https://github.com/aspnet), [Xamarin](https://github.com/xamarin) y [nuestras organizaciones de GitHub](https://opensource.microsoft.com/). diff --git a/translations/es/SUPPORT.md b/translations/es/SUPPORT.md index cd5194ed1..6e1a4f9f2 100644 --- a/translations/es/SUPPORT.md +++ b/translations/es/SUPPORT.md @@ -1,12 +1,3 @@ - # Soporte ## Cómo reportar problemas y obtener ayuda diff --git a/translations/es/TROUBLESHOOTING.md b/translations/es/TROUBLESHOOTING.md index 50b91bd8d..789bafb97 100644 --- a/translations/es/TROUBLESHOOTING.md +++ b/translations/es/TROUBLESHOOTING.md @@ -1,12 +1,3 @@ - # Guía de Solución de Problemas Esta guía te ayudará a resolver problemas comunes al trabajar con el plan de estudios de Machine Learning para Principiantes. Si no encuentras una solución aquí, consulta nuestras [Discusiones en Discord](https://aka.ms/foundry/discord) o [abre un problema](https://github.com/microsoft/ML-For-Beginners/issues). diff --git a/translations/es/docs/_sidebar.md b/translations/es/docs/_sidebar.md index 8a7b8bb92..5f9fe5c16 100644 --- a/translations/es/docs/_sidebar.md +++ b/translations/es/docs/_sidebar.md @@ -1,12 +1,3 @@ - - Introducción - [Introducción al Aprendizaje Automático](../1-Introduction/1-intro-to-ML/README.md) - [Historia del Aprendizaje Automático](../1-Introduction/2-history-of-ML/README.md) diff --git a/translations/es/for-teachers.md b/translations/es/for-teachers.md index a2e68a4ea..98f9fc346 100644 --- a/translations/es/for-teachers.md +++ b/translations/es/for-teachers.md @@ -1,12 +1,3 @@ - ## Para Educadores ¿Te gustaría usar este plan de estudios en tu aula? ¡Siéntete libre de hacerlo! diff --git a/translations/es/quiz-app/README.md b/translations/es/quiz-app/README.md index 0e025d345..0859ef85c 100644 --- a/translations/es/quiz-app/README.md +++ b/translations/es/quiz-app/README.md @@ -1,12 +1,3 @@ - # Cuestionarios Estos cuestionarios son los cuestionarios previos y posteriores a las clases del currículo de ML en https://aka.ms/ml-beginners diff --git a/translations/es/sketchnotes/LICENSE.md b/translations/es/sketchnotes/LICENSE.md index 0e7919297..08f72d04b 100644 --- a/translations/es/sketchnotes/LICENSE.md +++ b/translations/es/sketchnotes/LICENSE.md @@ -1,12 +1,3 @@ - Attribution-ShareAlike 4.0 Internacional ======================================================================= diff --git a/translations/es/sketchnotes/README.md b/translations/es/sketchnotes/README.md index c948c2737..77a90abc2 100644 --- a/translations/es/sketchnotes/README.md +++ b/translations/es/sketchnotes/README.md @@ -1,12 +1,3 @@ - Todas las notas visuales del currículo se pueden descargar aquí. 🖨 Para imprimir en alta resolución, las versiones TIFF están disponibles en [este repositorio](https://github.com/girliemac/a-picture-is-worth-a-1000-words/tree/main/ml/tiff). diff --git a/translations/fr/.co-op-translator.json b/translations/fr/.co-op-translator.json new file mode 100644 index 000000000..213ed7791 --- /dev/null +++ b/translations/fr/.co-op-translator.json @@ -0,0 +1,596 @@ +{ + "1-Introduction/1-intro-to-ML/README.md": { + "original_hash": "69389392fa6346e0dfa30f664b7b6fec", + "translation_date": "2025-09-04T23:00:52+00:00", + "source_file": "1-Introduction/1-intro-to-ML/README.md", + "language_code": "fr" + }, + "1-Introduction/1-intro-to-ML/assignment.md": { + "original_hash": "4c4698044bb8af52cfb6388a4ee0e53b", + "translation_date": "2025-09-03T23:38:26+00:00", + "source_file": "1-Introduction/1-intro-to-ML/assignment.md", + "language_code": "fr" + }, + "1-Introduction/2-history-of-ML/README.md": { + "original_hash": "6a05fec147e734c3e6bfa54505648e2b", + "translation_date": "2025-09-04T23:01:16+00:00", + "source_file": 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b/translations/fr/1-Introduction/1-intro-to-ML/README.md index fab12aa51..43d37c4a7 100644 --- a/translations/fr/1-Introduction/1-intro-to-ML/README.md +++ b/translations/fr/1-Introduction/1-intro-to-ML/README.md @@ -1,12 +1,3 @@ - # Introduction au machine learning ## [Quiz avant le cours](https://ff-quizzes.netlify.app/en/ml/) diff --git a/translations/fr/1-Introduction/1-intro-to-ML/assignment.md b/translations/fr/1-Introduction/1-intro-to-ML/assignment.md index df18cef87..be2f85413 100644 --- a/translations/fr/1-Introduction/1-intro-to-ML/assignment.md +++ b/translations/fr/1-Introduction/1-intro-to-ML/assignment.md @@ -1,12 +1,3 @@ - # Démarrez rapidement ## Instructions diff --git a/translations/fr/1-Introduction/2-history-of-ML/README.md b/translations/fr/1-Introduction/2-history-of-ML/README.md index fd48ad93e..50d55f036 100644 --- a/translations/fr/1-Introduction/2-history-of-ML/README.md +++ b/translations/fr/1-Introduction/2-history-of-ML/README.md @@ -1,12 +1,3 @@ - # Histoire de l'apprentissage automatique ![Résumé de l'histoire de l'apprentissage automatique sous forme de sketchnote](../../../../sketchnotes/ml-history.png) diff --git a/translations/fr/1-Introduction/2-history-of-ML/assignment.md b/translations/fr/1-Introduction/2-history-of-ML/assignment.md index fd2e21801..c87806eeb 100644 --- a/translations/fr/1-Introduction/2-history-of-ML/assignment.md +++ b/translations/fr/1-Introduction/2-history-of-ML/assignment.md @@ -1,12 +1,3 @@ - # Créer une chronologie ## Instructions diff --git a/translations/fr/1-Introduction/3-fairness/README.md b/translations/fr/1-Introduction/3-fairness/README.md index 34b150bba..47567baac 100644 --- a/translations/fr/1-Introduction/3-fairness/README.md +++ b/translations/fr/1-Introduction/3-fairness/README.md @@ -1,12 +1,3 @@ - # Construire des solutions de Machine Learning avec une IA responsable ![Résumé de l'IA responsable dans le Machine Learning sous forme de sketchnote](../../../../sketchnotes/ml-fairness.png) diff --git a/translations/fr/1-Introduction/3-fairness/assignment.md b/translations/fr/1-Introduction/3-fairness/assignment.md index 3fdf983f0..1f795b41b 100644 --- a/translations/fr/1-Introduction/3-fairness/assignment.md +++ b/translations/fr/1-Introduction/3-fairness/assignment.md @@ -1,12 +1,3 @@ - # Explorez la boîte à outils IA responsable ## Instructions diff --git a/translations/fr/1-Introduction/4-techniques-of-ML/README.md b/translations/fr/1-Introduction/4-techniques-of-ML/README.md index 918d6eef2..406c96626 100644 --- a/translations/fr/1-Introduction/4-techniques-of-ML/README.md +++ b/translations/fr/1-Introduction/4-techniques-of-ML/README.md @@ -1,12 +1,3 @@ - # Techniques de l'apprentissage automatique Le processus de création, d'utilisation et de maintenance des modèles d'apprentissage automatique ainsi que des données qu'ils utilisent est très différent de nombreux autres flux de travail de développement. Dans cette leçon, nous allons démystifier ce processus et présenter les principales techniques que vous devez connaître. Vous allez : diff --git a/translations/fr/1-Introduction/4-techniques-of-ML/assignment.md b/translations/fr/1-Introduction/4-techniques-of-ML/assignment.md index c3c201bc2..74b4a414c 100644 --- a/translations/fr/1-Introduction/4-techniques-of-ML/assignment.md +++ b/translations/fr/1-Introduction/4-techniques-of-ML/assignment.md @@ -1,12 +1,3 @@ - # Interviewer un data scientist ## Instructions diff --git a/translations/fr/1-Introduction/README.md b/translations/fr/1-Introduction/README.md index 378ffd247..05065c036 100644 --- a/translations/fr/1-Introduction/README.md +++ b/translations/fr/1-Introduction/README.md @@ -1,12 +1,3 @@ - # Introduction au machine learning Dans cette section du programme, vous serez initié aux concepts de base qui sous-tendent le domaine du machine learning, ce qu'il est, et vous découvrirez son histoire ainsi que les techniques utilisées par les chercheurs pour travailler avec lui. Explorons ensemble ce nouvel univers du ML ! diff --git a/translations/fr/2-Regression/1-Tools/README.md b/translations/fr/2-Regression/1-Tools/README.md index 19caf920d..8ad44bd55 100644 --- a/translations/fr/2-Regression/1-Tools/README.md +++ b/translations/fr/2-Regression/1-Tools/README.md @@ -1,12 +1,3 @@ - # Commencez avec Python et Scikit-learn pour les modèles de régression ![Résumé des régressions dans une sketchnote](../../../../sketchnotes/ml-regression.png) diff --git a/translations/fr/2-Regression/1-Tools/assignment.md b/translations/fr/2-Regression/1-Tools/assignment.md index 4cef9f055..652d153fd 100644 --- a/translations/fr/2-Regression/1-Tools/assignment.md +++ b/translations/fr/2-Regression/1-Tools/assignment.md @@ -1,12 +1,3 @@ - # Régression avec Scikit-learn ## Instructions diff --git a/translations/fr/2-Regression/1-Tools/solution/Julia/README.md b/translations/fr/2-Regression/1-Tools/solution/Julia/README.md index baf8235e0..930beca32 100644 --- a/translations/fr/2-Regression/1-Tools/solution/Julia/README.md +++ b/translations/fr/2-Regression/1-Tools/solution/Julia/README.md @@ -1,12 +1,3 @@ - --- diff --git a/translations/fr/2-Regression/2-Data/README.md b/translations/fr/2-Regression/2-Data/README.md index fee2dadc1..bb6e2b73a 100644 --- a/translations/fr/2-Regression/2-Data/README.md +++ b/translations/fr/2-Regression/2-Data/README.md @@ -1,12 +1,3 @@ - # Construire un modèle de régression avec Scikit-learn : préparer et visualiser les données ![Infographie sur la visualisation des données](../../../../2-Regression/2-Data/images/data-visualization.png) diff --git a/translations/fr/2-Regression/2-Data/assignment.md b/translations/fr/2-Regression/2-Data/assignment.md index fb9208e76..34e86b73c 100644 --- a/translations/fr/2-Regression/2-Data/assignment.md +++ b/translations/fr/2-Regression/2-Data/assignment.md @@ -1,12 +1,3 @@ - # Explorer les visualisations Il existe plusieurs bibliothèques disponibles pour la visualisation de données. Créez des visualisations en utilisant les données de citrouille de cette leçon avec matplotlib et seaborn dans un notebook d'exemple. Quelles bibliothèques sont les plus faciles à utiliser ? diff --git a/translations/fr/2-Regression/2-Data/solution/Julia/README.md b/translations/fr/2-Regression/2-Data/solution/Julia/README.md index af4774545..930beca32 100644 --- a/translations/fr/2-Regression/2-Data/solution/Julia/README.md +++ b/translations/fr/2-Regression/2-Data/solution/Julia/README.md @@ -1,12 +1,3 @@ - --- diff --git a/translations/fr/2-Regression/3-Linear/README.md b/translations/fr/2-Regression/3-Linear/README.md index 70b587790..1612c6980 100644 --- a/translations/fr/2-Regression/3-Linear/README.md +++ b/translations/fr/2-Regression/3-Linear/README.md @@ -1,12 +1,3 @@ - # Construire un modèle de régression avec Scikit-learn : quatre approches de régression ![Infographie sur la régression linéaire vs polynomiale](../../../../2-Regression/3-Linear/images/linear-polynomial.png) @@ -114,11 +105,11 @@ Maintenant que vous comprenez les mathématiques derrière la régression linéa Dans la leçon précédente, vous avez probablement vu que le prix moyen pour différents mois ressemble à ceci : -Prix moyen par mois +Prix moyen par mois Cela suggère qu'il pourrait y avoir une certaine corrélation, et nous pouvons essayer d'entraîner un modèle de régression linéaire pour prédire la relation entre `Mois` et `Prix`, ou entre `JourDeLAn` et `Prix`. Voici le nuage de points qui montre cette dernière relation : -Nuage de points du prix vs jour de l'année +Nuage de points du prix vs jour de l'année Voyons s'il existe une corrélation en utilisant la fonction `corr` : @@ -137,7 +128,7 @@ for i,var in enumerate(new_pumpkins['Variety'].unique()): ax = df.plot.scatter('DayOfYear','Price',ax=ax,c=colors[i],label=var) ``` -Nuage de points du prix vs jour de l'année +Nuage de points du prix vs jour de l'année Notre enquête suggère que la variété a plus d'effet sur le prix global que la date de vente réelle. Nous pouvons le voir avec un graphique en barres : @@ -145,7 +136,7 @@ Notre enquête suggère que la variété a plus d'effet sur le prix global que l new_pumpkins.groupby('Variety')['Price'].mean().plot(kind='bar') ``` -Graphique en barres du prix vs variété +Graphique en barres du prix vs variété Concentrons-nous pour le moment uniquement sur une variété de citrouilles, le 'type tarte', et voyons quel effet la date a sur le prix : @@ -153,7 +144,7 @@ Concentrons-nous pour le moment uniquement sur une variété de citrouilles, le pie_pumpkins = new_pumpkins[new_pumpkins['Variety']=='PIE TYPE'] pie_pumpkins.plot.scatter('DayOfYear','Price') ``` -Nuage de points du prix vs jour de l'année +Nuage de points du prix vs jour de l'année Si nous calculons maintenant la corrélation entre `Prix` et `JourDeLAn` en utilisant la fonction `corr`, nous obtiendrons quelque chose comme `-0.27` - ce qui signifie qu'entraîner un modèle prédictif a du sens. @@ -227,7 +218,7 @@ plt.scatter(X_test,y_test) plt.plot(X_test,pred) ``` -Régression linéaire +Régression linéaire ## Régression polynomiale @@ -256,7 +247,7 @@ Utiliser `PolynomialFeatures(2)` signifie que nous inclurons tous les polynômes Les pipelines peuvent être utilisés de la même manière que l'objet `LinearRegression` original, c'est-à-dire que nous pouvons `fit` le pipeline, puis utiliser `predict` pour obtenir les résultats de prédiction. Voici le graphique montrant les données de test et la courbe d'approximation : -Régression polynomiale +Régression polynomiale Avec la régression polynomiale, nous pouvons obtenir un MSE légèrement inférieur et un coefficient de détermination plus élevé, mais pas de manière significative. Nous devons prendre en compte d'autres caractéristiques ! @@ -274,7 +265,7 @@ Dans un monde idéal, nous voulons pouvoir prédire les prix pour différentes v Voici comment le prix moyen dépend de la variété : -Prix moyen par variété +Prix moyen par variété Pour prendre en compte la variété, nous devons d'abord la convertir en forme numérique, ou **l'encoder**. Il existe plusieurs façons de le faire : diff --git a/translations/fr/2-Regression/3-Linear/assignment.md b/translations/fr/2-Regression/3-Linear/assignment.md index 9b9022531..979af27bd 100644 --- a/translations/fr/2-Regression/3-Linear/assignment.md +++ b/translations/fr/2-Regression/3-Linear/assignment.md @@ -1,12 +1,3 @@ - # Créer un modèle de régression ## Instructions diff --git a/translations/fr/2-Regression/3-Linear/solution/Julia/README.md b/translations/fr/2-Regression/3-Linear/solution/Julia/README.md index 3d64b7f31..930beca32 100644 --- a/translations/fr/2-Regression/3-Linear/solution/Julia/README.md +++ b/translations/fr/2-Regression/3-Linear/solution/Julia/README.md @@ -1,12 +1,3 @@ - --- diff --git a/translations/fr/2-Regression/4-Logistic/README.md b/translations/fr/2-Regression/4-Logistic/README.md index 5e87eb097..050a1e448 100644 --- a/translations/fr/2-Regression/4-Logistic/README.md +++ b/translations/fr/2-Regression/4-Logistic/README.md @@ -1,12 +1,3 @@ - # Régression logistique pour prédire des catégories ![Infographie sur la régression logistique vs linéaire](../../../../2-Regression/4-Logistic/images/linear-vs-logistic.png) diff --git a/translations/fr/2-Regression/4-Logistic/assignment.md b/translations/fr/2-Regression/4-Logistic/assignment.md index b6b8ea084..f759ff083 100644 --- a/translations/fr/2-Regression/4-Logistic/assignment.md +++ b/translations/fr/2-Regression/4-Logistic/assignment.md @@ -1,12 +1,3 @@ - # Réessayer une Régression ## Instructions diff --git a/translations/fr/2-Regression/4-Logistic/solution/Julia/README.md b/translations/fr/2-Regression/4-Logistic/solution/Julia/README.md index d6213603f..930beca32 100644 --- a/translations/fr/2-Regression/4-Logistic/solution/Julia/README.md +++ b/translations/fr/2-Regression/4-Logistic/solution/Julia/README.md @@ -1,12 +1,3 @@ - --- diff --git a/translations/fr/2-Regression/README.md b/translations/fr/2-Regression/README.md index 9a15df989..6e3aa9f13 100644 --- a/translations/fr/2-Regression/README.md +++ b/translations/fr/2-Regression/README.md @@ -1,12 +1,3 @@ - # Modèles de régression pour l'apprentissage automatique ## Sujet régional : Modèles de régression pour les prix des citrouilles en Amérique du Nord 🎃 diff --git a/translations/fr/3-Web-App/1-Web-App/README.md b/translations/fr/3-Web-App/1-Web-App/README.md index ddf68fcc5..ca5dbd578 100644 --- a/translations/fr/3-Web-App/1-Web-App/README.md +++ b/translations/fr/3-Web-App/1-Web-App/README.md @@ -1,12 +1,3 @@ - # Construire une application web pour utiliser un modèle de machine learning Dans cette leçon, vous allez entraîner un modèle de machine learning sur un ensemble de données hors du commun : _les observations d'OVNI au cours du siècle dernier_, provenant de la base de données de NUFORC. diff --git a/translations/fr/3-Web-App/1-Web-App/assignment.md b/translations/fr/3-Web-App/1-Web-App/assignment.md index 79697c7c5..4bbcf1f92 100644 --- a/translations/fr/3-Web-App/1-Web-App/assignment.md +++ b/translations/fr/3-Web-App/1-Web-App/assignment.md @@ -1,12 +1,3 @@ - # Essayez un modèle différent ## Instructions diff --git a/translations/fr/3-Web-App/README.md b/translations/fr/3-Web-App/README.md index 8540793fd..88b130e46 100644 --- a/translations/fr/3-Web-App/README.md +++ b/translations/fr/3-Web-App/README.md @@ -1,12 +1,3 @@ - # Construire une application web pour utiliser votre modèle ML Dans cette section du programme, vous serez initié à un sujet appliqué en apprentissage automatique : comment sauvegarder votre modèle Scikit-learn sous forme de fichier pouvant être utilisé pour faire des prédictions dans une application web. Une fois le modèle sauvegardé, vous apprendrez à l'utiliser dans une application web construite avec Flask. Vous commencerez par créer un modèle à partir de données sur les observations d'OVNI ! Ensuite, vous construirez une application web qui vous permettra de saisir un nombre de secondes avec une valeur de latitude et de longitude pour prédire quel pays a signalé avoir vu un OVNI. diff --git a/translations/fr/4-Classification/1-Introduction/README.md b/translations/fr/4-Classification/1-Introduction/README.md index 14285c0dc..b163eac9a 100644 --- a/translations/fr/4-Classification/1-Introduction/README.md +++ b/translations/fr/4-Classification/1-Introduction/README.md @@ -1,12 +1,3 @@ - # Introduction à la classification Dans ces quatre leçons, vous allez explorer un aspect fondamental de l'apprentissage automatique classique : _la classification_. Nous allons examiner l'utilisation de divers algorithmes de classification avec un ensemble de données sur les cuisines brillantes d'Asie et d'Inde. Préparez-vous à avoir l'eau à la bouche ! diff --git a/translations/fr/4-Classification/1-Introduction/assignment.md b/translations/fr/4-Classification/1-Introduction/assignment.md index 4af6bc911..a88aa1084 100644 --- a/translations/fr/4-Classification/1-Introduction/assignment.md +++ b/translations/fr/4-Classification/1-Introduction/assignment.md @@ -1,12 +1,3 @@ - # Explorer les méthodes de classification ## Instructions diff --git a/translations/fr/4-Classification/1-Introduction/solution/Julia/README.md b/translations/fr/4-Classification/1-Introduction/solution/Julia/README.md index 4d19529ef..930beca32 100644 --- a/translations/fr/4-Classification/1-Introduction/solution/Julia/README.md +++ b/translations/fr/4-Classification/1-Introduction/solution/Julia/README.md @@ -1,12 +1,3 @@ - --- diff --git a/translations/fr/4-Classification/2-Classifiers-1/README.md b/translations/fr/4-Classification/2-Classifiers-1/README.md index 3d2179be8..6ca323d94 100644 --- a/translations/fr/4-Classification/2-Classifiers-1/README.md +++ b/translations/fr/4-Classification/2-Classifiers-1/README.md @@ -1,12 +1,3 @@ - # Classificateurs de cuisine 1 Dans cette leçon, vous utiliserez le jeu de données que vous avez sauvegardé lors de la dernière leçon, rempli de données équilibrées et nettoyées sur les cuisines. diff --git a/translations/fr/4-Classification/2-Classifiers-1/assignment.md b/translations/fr/4-Classification/2-Classifiers-1/assignment.md index 0a8f8b140..c3f494468 100644 --- a/translations/fr/4-Classification/2-Classifiers-1/assignment.md +++ b/translations/fr/4-Classification/2-Classifiers-1/assignment.md @@ -1,12 +1,3 @@ - # Étudiez les solveurs ## Instructions diff --git a/translations/fr/4-Classification/2-Classifiers-1/solution/Julia/README.md b/translations/fr/4-Classification/2-Classifiers-1/solution/Julia/README.md index 48f012f59..930beca32 100644 --- a/translations/fr/4-Classification/2-Classifiers-1/solution/Julia/README.md +++ b/translations/fr/4-Classification/2-Classifiers-1/solution/Julia/README.md @@ -1,12 +1,3 @@ - --- diff --git a/translations/fr/4-Classification/3-Classifiers-2/README.md b/translations/fr/4-Classification/3-Classifiers-2/README.md index eb64fa6c5..b8653d694 100644 --- a/translations/fr/4-Classification/3-Classifiers-2/README.md +++ b/translations/fr/4-Classification/3-Classifiers-2/README.md @@ -1,12 +1,3 @@ - # Classificateurs de cuisine 2 Dans cette deuxième leçon sur la classification, vous allez explorer davantage de méthodes pour classifier des données numériques. Vous apprendrez également les implications du choix d'un classificateur plutôt qu'un autre. diff --git a/translations/fr/4-Classification/3-Classifiers-2/assignment.md b/translations/fr/4-Classification/3-Classifiers-2/assignment.md index 82d5ca644..cb7536cd5 100644 --- a/translations/fr/4-Classification/3-Classifiers-2/assignment.md +++ b/translations/fr/4-Classification/3-Classifiers-2/assignment.md @@ -1,12 +1,3 @@ - # Jeu de Paramètres ## Instructions diff --git a/translations/fr/4-Classification/3-Classifiers-2/solution/Julia/README.md b/translations/fr/4-Classification/3-Classifiers-2/solution/Julia/README.md index babd2080d..15fa5d64a 100644 --- a/translations/fr/4-Classification/3-Classifiers-2/solution/Julia/README.md +++ b/translations/fr/4-Classification/3-Classifiers-2/solution/Julia/README.md @@ -1,12 +1,3 @@ - --- diff --git a/translations/fr/4-Classification/4-Applied/README.md b/translations/fr/4-Classification/4-Applied/README.md index d58b43f95..cc2fba04e 100644 --- a/translations/fr/4-Classification/4-Applied/README.md +++ b/translations/fr/4-Classification/4-Applied/README.md @@ -1,12 +1,3 @@ - # Construire une application web de recommandation de cuisine Dans cette leçon, vous allez créer un modèle de classification en utilisant certaines des techniques apprises dans les leçons précédentes, ainsi que le délicieux ensemble de données sur les cuisines utilisé tout au long de cette série. De plus, vous allez construire une petite application web pour utiliser un modèle sauvegardé, en exploitant le runtime web d'Onnx. diff --git a/translations/fr/4-Classification/4-Applied/assignment.md b/translations/fr/4-Classification/4-Applied/assignment.md index 0f701edf1..20c551759 100644 --- a/translations/fr/4-Classification/4-Applied/assignment.md +++ b/translations/fr/4-Classification/4-Applied/assignment.md @@ -1,12 +1,3 @@ - # Construire un système de recommandation ## Instructions diff --git a/translations/fr/4-Classification/README.md b/translations/fr/4-Classification/README.md index 8c0aa2e77..c4e443cc6 100644 --- a/translations/fr/4-Classification/README.md +++ b/translations/fr/4-Classification/README.md @@ -1,12 +1,3 @@ - # Commencer avec la classification ## Sujet régional : Délicieuses cuisines asiatiques et indiennes 🍜 diff --git a/translations/fr/5-Clustering/1-Visualize/README.md b/translations/fr/5-Clustering/1-Visualize/README.md index dbc24f51b..ce45a6962 100644 --- a/translations/fr/5-Clustering/1-Visualize/README.md +++ b/translations/fr/5-Clustering/1-Visualize/README.md @@ -1,12 +1,3 @@ - # Introduction à la classification par regroupement La classification par regroupement est un type d'[apprentissage non supervisé](https://wikipedia.org/wiki/Unsupervised_learning) qui suppose qu'un ensemble de données est non étiqueté ou que ses entrées ne sont pas associées à des sorties prédéfinies. Elle utilise divers algorithmes pour trier les données non étiquetées et fournir des regroupements en fonction des motifs qu'elle discerne dans les données. diff --git a/translations/fr/5-Clustering/1-Visualize/assignment.md b/translations/fr/5-Clustering/1-Visualize/assignment.md index 8ed34fc6d..0f418b9e4 100644 --- a/translations/fr/5-Clustering/1-Visualize/assignment.md +++ b/translations/fr/5-Clustering/1-Visualize/assignment.md @@ -1,12 +1,3 @@ - # Recherchez d'autres visualisations pour le clustering ## Instructions diff --git a/translations/fr/5-Clustering/1-Visualize/solution/Julia/README.md b/translations/fr/5-Clustering/1-Visualize/solution/Julia/README.md index ee20959fb..f7576c139 100644 --- a/translations/fr/5-Clustering/1-Visualize/solution/Julia/README.md +++ b/translations/fr/5-Clustering/1-Visualize/solution/Julia/README.md @@ -1,12 +1,3 @@ - --- diff --git a/translations/fr/5-Clustering/2-K-Means/README.md b/translations/fr/5-Clustering/2-K-Means/README.md index 06a10e816..93331153b 100644 --- a/translations/fr/5-Clustering/2-K-Means/README.md +++ b/translations/fr/5-Clustering/2-K-Means/README.md @@ -1,12 +1,3 @@ - # Regroupement K-Means ## [Quiz avant le cours](https://ff-quizzes.netlify.app/en/ml/) diff --git a/translations/fr/5-Clustering/2-K-Means/assignment.md b/translations/fr/5-Clustering/2-K-Means/assignment.md index 0bce0c3a9..069782ff9 100644 --- a/translations/fr/5-Clustering/2-K-Means/assignment.md +++ b/translations/fr/5-Clustering/2-K-Means/assignment.md @@ -1,12 +1,3 @@ - # Essayez différentes méthodes de regroupement ## Instructions diff --git a/translations/fr/5-Clustering/2-K-Means/solution/Julia/README.md b/translations/fr/5-Clustering/2-K-Means/solution/Julia/README.md index 22beb17e4..f7576c139 100644 --- a/translations/fr/5-Clustering/2-K-Means/solution/Julia/README.md +++ b/translations/fr/5-Clustering/2-K-Means/solution/Julia/README.md @@ -1,12 +1,3 @@ - --- diff --git a/translations/fr/5-Clustering/README.md b/translations/fr/5-Clustering/README.md index 0ad664280..fffe39e0f 100644 --- a/translations/fr/5-Clustering/README.md +++ b/translations/fr/5-Clustering/README.md @@ -1,12 +1,3 @@ - # Modèles de clustering pour l'apprentissage automatique Le clustering est une tâche d'apprentissage automatique qui cherche à identifier des objets similaires et à les regrouper dans des groupes appelés clusters. Ce qui distingue le clustering des autres approches en apprentissage automatique, c'est que tout se fait automatiquement. En fait, on peut dire que c'est l'opposé de l'apprentissage supervisé. diff --git a/translations/fr/6-NLP/1-Introduction-to-NLP/README.md b/translations/fr/6-NLP/1-Introduction-to-NLP/README.md index 627d0fa94..2ec4543c1 100644 --- a/translations/fr/6-NLP/1-Introduction-to-NLP/README.md +++ b/translations/fr/6-NLP/1-Introduction-to-NLP/README.md @@ -1,12 +1,3 @@ - # Introduction au traitement du langage naturel Cette leçon couvre une brève histoire et les concepts importants du *traitement du langage naturel*, un sous-domaine de la *linguistique computationnelle*. diff --git a/translations/fr/6-NLP/1-Introduction-to-NLP/assignment.md b/translations/fr/6-NLP/1-Introduction-to-NLP/assignment.md index 9485741aa..d22b18b62 100644 --- a/translations/fr/6-NLP/1-Introduction-to-NLP/assignment.md +++ b/translations/fr/6-NLP/1-Introduction-to-NLP/assignment.md @@ -1,12 +1,3 @@ - # Recherche d'un bot ## Instructions diff --git a/translations/fr/6-NLP/2-Tasks/README.md b/translations/fr/6-NLP/2-Tasks/README.md index 8e28ea690..21723fc6c 100644 --- a/translations/fr/6-NLP/2-Tasks/README.md +++ b/translations/fr/6-NLP/2-Tasks/README.md @@ -1,12 +1,3 @@ - # Tâches et techniques courantes en traitement du langage naturel Pour la plupart des tâches de *traitement du langage naturel*, le texte à traiter doit être décomposé, analysé, et les résultats doivent être stockés ou croisés avec des règles et des ensembles de données. Ces tâches permettent au programmeur de déduire le _sens_, l'_intention_ ou simplement la _fréquence_ des termes et des mots dans un texte. diff --git a/translations/fr/6-NLP/2-Tasks/assignment.md b/translations/fr/6-NLP/2-Tasks/assignment.md index cf053711b..f52e41426 100644 --- a/translations/fr/6-NLP/2-Tasks/assignment.md +++ b/translations/fr/6-NLP/2-Tasks/assignment.md @@ -1,12 +1,3 @@ - # Faire parler un bot ## Instructions diff --git a/translations/fr/6-NLP/3-Translation-Sentiment/README.md b/translations/fr/6-NLP/3-Translation-Sentiment/README.md index 6055e23cf..3bf83f624 100644 --- a/translations/fr/6-NLP/3-Translation-Sentiment/README.md +++ b/translations/fr/6-NLP/3-Translation-Sentiment/README.md @@ -1,12 +1,3 @@ - # Traduction et analyse de sentiment avec ML Dans les leçons précédentes, vous avez appris à créer un bot basique en utilisant `TextBlob`, une bibliothèque qui intègre l'apprentissage automatique en arrière-plan pour effectuer des tâches de traitement du langage naturel (NLP) telles que l'extraction de syntagmes nominaux. Un autre défi important en linguistique computationnelle est la _traduction_ précise d'une phrase d'une langue parlée ou écrite à une autre. diff --git a/translations/fr/6-NLP/3-Translation-Sentiment/assignment.md b/translations/fr/6-NLP/3-Translation-Sentiment/assignment.md index 2d594be1b..bcd04c7a8 100644 --- a/translations/fr/6-NLP/3-Translation-Sentiment/assignment.md +++ b/translations/fr/6-NLP/3-Translation-Sentiment/assignment.md @@ -1,12 +1,3 @@ - # Licence poétique ## Instructions diff --git a/translations/fr/6-NLP/3-Translation-Sentiment/solution/Julia/README.md b/translations/fr/6-NLP/3-Translation-Sentiment/solution/Julia/README.md index 8d7b7460c..f7576c139 100644 --- a/translations/fr/6-NLP/3-Translation-Sentiment/solution/Julia/README.md +++ b/translations/fr/6-NLP/3-Translation-Sentiment/solution/Julia/README.md @@ -1,12 +1,3 @@ - --- diff --git a/translations/fr/6-NLP/3-Translation-Sentiment/solution/R/README.md b/translations/fr/6-NLP/3-Translation-Sentiment/solution/R/README.md index 5f59ea05c..4fce8243f 100644 --- a/translations/fr/6-NLP/3-Translation-Sentiment/solution/R/README.md +++ b/translations/fr/6-NLP/3-Translation-Sentiment/solution/R/README.md @@ -1,12 +1,3 @@ - --- diff --git a/translations/fr/6-NLP/4-Hotel-Reviews-1/README.md b/translations/fr/6-NLP/4-Hotel-Reviews-1/README.md index 956366e08..bbde13c26 100644 --- a/translations/fr/6-NLP/4-Hotel-Reviews-1/README.md +++ b/translations/fr/6-NLP/4-Hotel-Reviews-1/README.md @@ -1,12 +1,3 @@ - # Analyse de sentiment avec les avis d'hôtels - traitement des données Dans cette section, vous utiliserez les techniques des leçons précédentes pour effectuer une analyse exploratoire des données sur un grand ensemble de données. Une fois que vous aurez une bonne compréhension de l'utilité des différentes colonnes, vous apprendrez : diff --git a/translations/fr/6-NLP/4-Hotel-Reviews-1/assignment.md b/translations/fr/6-NLP/4-Hotel-Reviews-1/assignment.md index 4fa182902..8aa4a39a5 100644 --- a/translations/fr/6-NLP/4-Hotel-Reviews-1/assignment.md +++ b/translations/fr/6-NLP/4-Hotel-Reviews-1/assignment.md @@ -1,12 +1,3 @@ - # NLTK ## Instructions diff --git a/translations/fr/6-NLP/4-Hotel-Reviews-1/solution/Julia/README.md b/translations/fr/6-NLP/4-Hotel-Reviews-1/solution/Julia/README.md index b0a083ae1..f7576c139 100644 --- a/translations/fr/6-NLP/4-Hotel-Reviews-1/solution/Julia/README.md +++ b/translations/fr/6-NLP/4-Hotel-Reviews-1/solution/Julia/README.md @@ -1,12 +1,3 @@ - --- diff --git a/translations/fr/6-NLP/4-Hotel-Reviews-1/solution/R/README.md b/translations/fr/6-NLP/4-Hotel-Reviews-1/solution/R/README.md index 363f1cb9b..f7576c139 100644 --- a/translations/fr/6-NLP/4-Hotel-Reviews-1/solution/R/README.md +++ b/translations/fr/6-NLP/4-Hotel-Reviews-1/solution/R/README.md @@ -1,12 +1,3 @@ - --- diff --git a/translations/fr/6-NLP/5-Hotel-Reviews-2/README.md b/translations/fr/6-NLP/5-Hotel-Reviews-2/README.md index 2ee1517d3..e99bc15f9 100644 --- a/translations/fr/6-NLP/5-Hotel-Reviews-2/README.md +++ b/translations/fr/6-NLP/5-Hotel-Reviews-2/README.md @@ -1,12 +1,3 @@ - # Analyse de sentiment avec des avis d'hôtels Maintenant que vous avez exploré le jeu de données en détail, il est temps de filtrer les colonnes et d'utiliser des techniques de NLP sur le jeu de données pour obtenir de nouvelles informations sur les hôtels. diff --git a/translations/fr/6-NLP/5-Hotel-Reviews-2/assignment.md b/translations/fr/6-NLP/5-Hotel-Reviews-2/assignment.md index 4be89bcba..a67f7e739 100644 --- a/translations/fr/6-NLP/5-Hotel-Reviews-2/assignment.md +++ b/translations/fr/6-NLP/5-Hotel-Reviews-2/assignment.md @@ -1,12 +1,3 @@ - # Essayez un autre jeu de données ## Instructions diff --git a/translations/fr/6-NLP/5-Hotel-Reviews-2/solution/Julia/README.md b/translations/fr/6-NLP/5-Hotel-Reviews-2/solution/Julia/README.md index 5e5140a83..930beca32 100644 --- a/translations/fr/6-NLP/5-Hotel-Reviews-2/solution/Julia/README.md +++ b/translations/fr/6-NLP/5-Hotel-Reviews-2/solution/Julia/README.md @@ -1,12 +1,3 @@ - --- diff --git a/translations/fr/6-NLP/5-Hotel-Reviews-2/solution/R/README.md b/translations/fr/6-NLP/5-Hotel-Reviews-2/solution/R/README.md index 78edbefba..f7576c139 100644 --- a/translations/fr/6-NLP/5-Hotel-Reviews-2/solution/R/README.md +++ b/translations/fr/6-NLP/5-Hotel-Reviews-2/solution/R/README.md @@ -1,12 +1,3 @@ - --- diff --git a/translations/fr/6-NLP/README.md b/translations/fr/6-NLP/README.md index 20f9ed708..51b2a9539 100644 --- a/translations/fr/6-NLP/README.md +++ b/translations/fr/6-NLP/README.md @@ -1,12 +1,3 @@ - # Introduction au traitement du langage naturel Le traitement du langage naturel (NLP) est la capacité d'un programme informatique à comprendre le langage humain tel qu'il est parlé et écrit — appelé langage naturel. C'est une composante de l'intelligence artificielle (IA). Le NLP existe depuis plus de 50 ans et trouve ses racines dans le domaine de la linguistique. Tout le domaine est orienté vers l'aide aux machines pour comprendre et traiter le langage humain. Cela peut ensuite être utilisé pour effectuer des tâches comme la vérification orthographique ou la traduction automatique. Il possède une variété d'applications concrètes dans de nombreux domaines, notamment la recherche médicale, les moteurs de recherche et l'intelligence d'affaires. diff --git a/translations/fr/6-NLP/data/README.md b/translations/fr/6-NLP/data/README.md index dd283e3bc..77881c6ee 100644 --- a/translations/fr/6-NLP/data/README.md +++ b/translations/fr/6-NLP/data/README.md @@ -1,12 +1,3 @@ - Téléchargez les données des avis sur l'hôtel dans ce dossier. --- diff --git a/translations/fr/7-TimeSeries/1-Introduction/README.md b/translations/fr/7-TimeSeries/1-Introduction/README.md index 1f24bd552..620d3577e 100644 --- a/translations/fr/7-TimeSeries/1-Introduction/README.md +++ b/translations/fr/7-TimeSeries/1-Introduction/README.md @@ -1,12 +1,3 @@ - # Introduction à la prévision des séries temporelles ![Résumé des séries temporelles dans un sketchnote](../../../../sketchnotes/ml-timeseries.png) diff --git a/translations/fr/7-TimeSeries/1-Introduction/assignment.md b/translations/fr/7-TimeSeries/1-Introduction/assignment.md index 7b224d59a..293048040 100644 --- a/translations/fr/7-TimeSeries/1-Introduction/assignment.md +++ b/translations/fr/7-TimeSeries/1-Introduction/assignment.md @@ -1,12 +1,3 @@ - # Visualiser d'autres séries temporelles ## Instructions diff --git a/translations/fr/7-TimeSeries/1-Introduction/solution/Julia/README.md b/translations/fr/7-TimeSeries/1-Introduction/solution/Julia/README.md index 8c7433c0d..15fa5d64a 100644 --- a/translations/fr/7-TimeSeries/1-Introduction/solution/Julia/README.md +++ b/translations/fr/7-TimeSeries/1-Introduction/solution/Julia/README.md @@ -1,12 +1,3 @@ - --- diff --git a/translations/fr/7-TimeSeries/1-Introduction/solution/R/README.md b/translations/fr/7-TimeSeries/1-Introduction/solution/R/README.md index 734166a13..15fa5d64a 100644 --- a/translations/fr/7-TimeSeries/1-Introduction/solution/R/README.md +++ b/translations/fr/7-TimeSeries/1-Introduction/solution/R/README.md @@ -1,12 +1,3 @@ - --- diff --git a/translations/fr/7-TimeSeries/2-ARIMA/README.md b/translations/fr/7-TimeSeries/2-ARIMA/README.md index 72fe96a94..b0272e921 100644 --- a/translations/fr/7-TimeSeries/2-ARIMA/README.md +++ b/translations/fr/7-TimeSeries/2-ARIMA/README.md @@ -1,12 +1,3 @@ - # Prévision des séries temporelles avec ARIMA Dans la leçon précédente, vous avez appris un peu sur la prévision des séries temporelles et chargé un ensemble de données montrant les fluctuations de la charge électrique sur une période donnée. diff --git a/translations/fr/7-TimeSeries/2-ARIMA/assignment.md b/translations/fr/7-TimeSeries/2-ARIMA/assignment.md index 02a97eecd..ba0d5df4c 100644 --- a/translations/fr/7-TimeSeries/2-ARIMA/assignment.md +++ b/translations/fr/7-TimeSeries/2-ARIMA/assignment.md @@ -1,12 +1,3 @@ - # Un nouveau modèle ARIMA ## Instructions diff --git a/translations/fr/7-TimeSeries/2-ARIMA/solution/Julia/README.md b/translations/fr/7-TimeSeries/2-ARIMA/solution/Julia/README.md index 348654d66..69d0a788c 100644 --- a/translations/fr/7-TimeSeries/2-ARIMA/solution/Julia/README.md +++ b/translations/fr/7-TimeSeries/2-ARIMA/solution/Julia/README.md @@ -1,12 +1,3 @@ - --- diff --git a/translations/fr/7-TimeSeries/2-ARIMA/solution/R/README.md b/translations/fr/7-TimeSeries/2-ARIMA/solution/R/README.md index 8973d268b..864b83c09 100644 --- a/translations/fr/7-TimeSeries/2-ARIMA/solution/R/README.md +++ b/translations/fr/7-TimeSeries/2-ARIMA/solution/R/README.md @@ -1,12 +1,3 @@ - --- diff --git a/translations/fr/7-TimeSeries/3-SVR/README.md b/translations/fr/7-TimeSeries/3-SVR/README.md index b5283735c..5449d902a 100644 --- a/translations/fr/7-TimeSeries/3-SVR/README.md +++ b/translations/fr/7-TimeSeries/3-SVR/README.md @@ -1,12 +1,3 @@ - # Prévision des séries temporelles avec le Support Vector Regressor Dans la leçon précédente, vous avez appris à utiliser le modèle ARIMA pour effectuer des prédictions sur des séries temporelles. Maintenant, vous allez découvrir le modèle Support Vector Regressor, un modèle de régression utilisé pour prédire des données continues. diff --git a/translations/fr/7-TimeSeries/3-SVR/assignment.md b/translations/fr/7-TimeSeries/3-SVR/assignment.md index 4d612e92a..572238d8f 100644 --- a/translations/fr/7-TimeSeries/3-SVR/assignment.md +++ b/translations/fr/7-TimeSeries/3-SVR/assignment.md @@ -1,12 +1,3 @@ - # Un nouveau modèle SVR ## Instructions [^1] diff --git a/translations/fr/7-TimeSeries/README.md b/translations/fr/7-TimeSeries/README.md index 238ce00ed..c57142e2a 100644 --- a/translations/fr/7-TimeSeries/README.md +++ b/translations/fr/7-TimeSeries/README.md @@ -1,12 +1,3 @@ - # Introduction à la prévision des séries temporelles Qu'est-ce que la prévision des séries temporelles ? Il s'agit de prédire des événements futurs en analysant les tendances du passé. diff --git a/translations/fr/8-Reinforcement/1-QLearning/README.md b/translations/fr/8-Reinforcement/1-QLearning/README.md index c209a0543..b12aa18dc 100644 --- a/translations/fr/8-Reinforcement/1-QLearning/README.md +++ b/translations/fr/8-Reinforcement/1-QLearning/README.md @@ -1,12 +1,3 @@ - # Introduction à l'apprentissage par renforcement et au Q-Learning ![Résumé du renforcement dans l'apprentissage automatique sous forme de sketchnote](../../../../sketchnotes/ml-reinforcement.png) diff --git a/translations/fr/8-Reinforcement/1-QLearning/assignment.md b/translations/fr/8-Reinforcement/1-QLearning/assignment.md index a49777040..6f50eb643 100644 --- a/translations/fr/8-Reinforcement/1-QLearning/assignment.md +++ b/translations/fr/8-Reinforcement/1-QLearning/assignment.md @@ -1,12 +1,3 @@ - # Un monde plus réaliste Dans notre situation, Peter pouvait se déplacer presque sans se fatiguer ni avoir faim. Dans un monde plus réaliste, il doit s'asseoir et se reposer de temps en temps, et aussi se nourrir. Rendons notre monde plus réaliste en appliquant les règles suivantes : diff --git a/translations/fr/8-Reinforcement/1-QLearning/solution/Julia/README.md b/translations/fr/8-Reinforcement/1-QLearning/solution/Julia/README.md index 1867a9cb5..930beca32 100644 --- a/translations/fr/8-Reinforcement/1-QLearning/solution/Julia/README.md +++ b/translations/fr/8-Reinforcement/1-QLearning/solution/Julia/README.md @@ -1,12 +1,3 @@ - --- diff --git a/translations/fr/8-Reinforcement/1-QLearning/solution/R/README.md b/translations/fr/8-Reinforcement/1-QLearning/solution/R/README.md index 28363d663..930beca32 100644 --- a/translations/fr/8-Reinforcement/1-QLearning/solution/R/README.md +++ b/translations/fr/8-Reinforcement/1-QLearning/solution/R/README.md @@ -1,12 +1,3 @@ - --- diff --git a/translations/fr/8-Reinforcement/2-Gym/README.md b/translations/fr/8-Reinforcement/2-Gym/README.md index 52ee40d92..28b27c603 100644 --- a/translations/fr/8-Reinforcement/2-Gym/README.md +++ b/translations/fr/8-Reinforcement/2-Gym/README.md @@ -1,12 +1,3 @@ - ## Prérequis Dans cette leçon, nous utiliserons une bibliothèque appelée **OpenAI Gym** pour simuler différents **environnements**. Vous pouvez exécuter le code de cette leçon localement (par exemple, depuis Visual Studio Code), auquel cas la simulation s'ouvrira dans une nouvelle fenêtre. Si vous exécutez le code en ligne, vous devrez peut-être apporter quelques ajustements au code, comme décrit [ici](https://towardsdatascience.com/rendering-openai-gym-envs-on-binder-and-google-colab-536f99391cc7). diff --git a/translations/fr/8-Reinforcement/2-Gym/assignment.md b/translations/fr/8-Reinforcement/2-Gym/assignment.md index e7542be70..d8b1bb8e4 100644 --- a/translations/fr/8-Reinforcement/2-Gym/assignment.md +++ b/translations/fr/8-Reinforcement/2-Gym/assignment.md @@ -1,12 +1,3 @@ - # Entraîner une voiture de montagne [OpenAI Gym](http://gym.openai.com) a été conçu de manière à ce que tous les environnements fournissent la même API - c'est-à-dire les mêmes méthodes `reset`, `step` et `render`, ainsi que les mêmes abstractions de **espace d'action** et **espace d'observation**. Ainsi, il devrait être possible d'adapter les mêmes algorithmes d'apprentissage par renforcement à différents environnements avec des modifications minimales du code. diff --git a/translations/fr/8-Reinforcement/2-Gym/solution/Julia/README.md b/translations/fr/8-Reinforcement/2-Gym/solution/Julia/README.md index e09d1b9b6..f7576c139 100644 --- a/translations/fr/8-Reinforcement/2-Gym/solution/Julia/README.md +++ b/translations/fr/8-Reinforcement/2-Gym/solution/Julia/README.md @@ -1,12 +1,3 @@ - --- diff --git a/translations/fr/8-Reinforcement/2-Gym/solution/R/README.md b/translations/fr/8-Reinforcement/2-Gym/solution/R/README.md index 9f5448d39..f7576c139 100644 --- a/translations/fr/8-Reinforcement/2-Gym/solution/R/README.md +++ b/translations/fr/8-Reinforcement/2-Gym/solution/R/README.md @@ -1,12 +1,3 @@ - --- diff --git a/translations/fr/8-Reinforcement/README.md b/translations/fr/8-Reinforcement/README.md index c397513d2..1b319e90b 100644 --- a/translations/fr/8-Reinforcement/README.md +++ b/translations/fr/8-Reinforcement/README.md @@ -1,12 +1,3 @@ - # Introduction à l'apprentissage par renforcement L'apprentissage par renforcement, ou RL, est considéré comme l'un des paradigmes fondamentaux de l'apprentissage automatique, aux côtés de l'apprentissage supervisé et non supervisé. Le RL concerne les décisions : prendre les bonnes décisions ou, à défaut, apprendre de celles-ci. diff --git a/translations/fr/9-Real-World/1-Applications/README.md b/translations/fr/9-Real-World/1-Applications/README.md index 22b026d76..5249b9860 100644 --- a/translations/fr/9-Real-World/1-Applications/README.md +++ b/translations/fr/9-Real-World/1-Applications/README.md @@ -1,12 +1,3 @@ - # Postscript : L'apprentissage automatique dans le monde réel ![Résumé de l'apprentissage automatique dans le monde réel sous forme de sketchnote](../../../../sketchnotes/ml-realworld.png) diff --git a/translations/fr/9-Real-World/1-Applications/assignment.md b/translations/fr/9-Real-World/1-Applications/assignment.md index 73593e886..af6e70fc5 100644 --- a/translations/fr/9-Real-World/1-Applications/assignment.md +++ b/translations/fr/9-Real-World/1-Applications/assignment.md @@ -1,12 +1,3 @@ - # Une chasse au trésor en apprentissage automatique ## Instructions diff --git a/translations/fr/9-Real-World/2-Debugging-ML-Models/README.md b/translations/fr/9-Real-World/2-Debugging-ML-Models/README.md index 9327e85db..b505dddc2 100644 --- a/translations/fr/9-Real-World/2-Debugging-ML-Models/README.md +++ b/translations/fr/9-Real-World/2-Debugging-ML-Models/README.md @@ -1,12 +1,3 @@ - # Postscript : Débogage de modèles en apprentissage automatique avec les composants du tableau de bord AI responsable ## [Quiz avant la leçon](https://ff-quizzes.netlify.app/en/ml/) diff --git a/translations/fr/9-Real-World/2-Debugging-ML-Models/assignment.md b/translations/fr/9-Real-World/2-Debugging-ML-Models/assignment.md index f4c9f45d7..2cb4ae01e 100644 --- a/translations/fr/9-Real-World/2-Debugging-ML-Models/assignment.md +++ b/translations/fr/9-Real-World/2-Debugging-ML-Models/assignment.md @@ -1,12 +1,3 @@ - # Explorez le tableau de bord Responsible AI (RAI) ## Instructions diff --git a/translations/fr/9-Real-World/README.md b/translations/fr/9-Real-World/README.md index 5356ccd77..fbf58d72d 100644 --- a/translations/fr/9-Real-World/README.md +++ b/translations/fr/9-Real-World/README.md @@ -1,12 +1,3 @@ - # Postscript : Applications réelles de l'apprentissage automatique classique Dans cette section du programme, vous serez introduit à quelques applications réelles de l'apprentissage automatique classique. Nous avons parcouru l'internet pour trouver des articles et des publications sur des applications ayant utilisé ces stratégies, en évitant autant que possible les réseaux neuronaux, l'apprentissage profond et l'intelligence artificielle. Découvrez comment l'apprentissage automatique est utilisé dans les systèmes d'entreprise, les applications écologiques, la finance, les arts et la culture, et bien plus encore. diff --git a/translations/fr/AGENTS.md b/translations/fr/AGENTS.md index b21b30e79..696eddcf6 100644 --- a/translations/fr/AGENTS.md +++ b/translations/fr/AGENTS.md @@ -1,12 +1,3 @@ - # AGENTS.md ## Aperçu du projet diff --git a/translations/fr/CODE_OF_CONDUCT.md b/translations/fr/CODE_OF_CONDUCT.md index 69e8f96ff..c8eabb695 100644 --- a/translations/fr/CODE_OF_CONDUCT.md +++ b/translations/fr/CODE_OF_CONDUCT.md @@ -1,12 +1,3 @@ - # Code de conduite Open Source de Microsoft Ce projet a adopté le [Code de conduite Open Source de Microsoft](https://opensource.microsoft.com/codeofconduct/). diff --git a/translations/fr/CONTRIBUTING.md b/translations/fr/CONTRIBUTING.md index d3251d126..4113b46c1 100644 --- a/translations/fr/CONTRIBUTING.md +++ b/translations/fr/CONTRIBUTING.md @@ -1,12 +1,3 @@ - # Contribuer Ce projet accueille avec plaisir les contributions et suggestions. La plupart des contributions nécessitent que vous acceptiez un Accord de Licence de Contributeur (CLA) déclarant que vous avez le droit, et que vous accordez effectivement, les droits nécessaires pour que nous puissions utiliser votre contribution. Pour plus de détails, visitez https://cla.microsoft.com. diff --git a/translations/fr/README.md b/translations/fr/README.md index 3387cebb1..6ff1c1187 100644 --- a/translations/fr/README.md +++ b/translations/fr/README.md @@ -1,104 +1,95 @@ - -[![GitHub license](https://img.shields.io/github/license/microsoft/ML-For-Beginners.svg)](https://github.com/microsoft/ML-For-Beginners/blob/master/LICENSE) -[![GitHub contributors](https://img.shields.io/github/contributors/microsoft/ML-For-Beginners.svg)](https://GitHub.com/microsoft/ML-For-Beginners/graphs/contributors/) -[![GitHub issues](https://img.shields.io/github/issues/microsoft/ML-For-Beginners.svg)](https://GitHub.com/microsoft/ML-For-Beginners/issues/) -[![GitHub pull-requests](https://img.shields.io/github/issues-pr/microsoft/ML-For-Beginners.svg)](https://GitHub.com/microsoft/ML-For-Beginners/pulls/) -[![PRs Welcome](https://img.shields.io/badge/PRs-welcome-brightgreen.svg?style=flat-square)](http://makeapullrequest.com) - -[![GitHub watchers](https://img.shields.io/github/watchers/microsoft/ML-For-Beginners.svg?style=social&label=Watch)](https://GitHub.com/microsoft/ML-For-Beginners/watchers/) -[![GitHub forks](https://img.shields.io/github/forks/microsoft/ML-For-Beginners.svg?style=social&label=Fork)](https://GitHub.com/microsoft/ML-For-Beginners/network/) -[![GitHub stars](https://img.shields.io/github/stars/microsoft/ML-For-Beginners.svg?style=social&label=Star)](https://GitHub.com/microsoft/ML-For-Beginners/stargazers/) - -### 🌐 Support Multilingue - -#### Pris en charge via GitHub Action (Automatisé & Toujours à Jour) +[![Licence GitHub](https://img.shields.io/github/license/microsoft/ML-For-Beginners.svg)](https://github.com/microsoft/ML-For-Beginners/blob/master/LICENSE) +[![Contributeurs GitHub](https://img.shields.io/github/contributors/microsoft/ML-For-Beginners.svg)](https://GitHub.com/microsoft/ML-For-Beginners/graphs/contributors/) +[![Problèmes GitHub](https://img.shields.io/github/issues/microsoft/ML-For-Beginners.svg)](https://GitHub.com/microsoft/ML-For-Beginners/issues/) +[![Demandes de tirage GitHub](https://img.shields.io/github/issues-pr/microsoft/ML-For-Beginners.svg)](https://GitHub.com/microsoft/ML-For-Beginners/pulls/) +[![PRs Bienvenus](https://img.shields.io/badge/PRs-welcome-brightgreen.svg?style=flat-square)](http://makeapullrequest.com) + +[![Observateurs GitHub](https://img.shields.io/github/watchers/microsoft/ML-For-Beginners.svg?style=social&label=Watch)](https://GitHub.com/microsoft/ML-For-Beginners/watchers/) +[![Fourches GitHub](https://img.shields.io/github/forks/microsoft/ML-For-Beginners.svg?style=social&label=Fork)](https://GitHub.com/microsoft/ML-For-Beginners/network/) +[![Étoiles GitHub](https://img.shields.io/github/stars/microsoft/ML-For-Beginners.svg?style=social&label=Star)](https://GitHub.com/microsoft/ML-For-Beginners/stargazers/) + +### 🌐 Support multilingue + +#### Pris en charge via GitHub Action (Automatisé et toujours à jour) -[Arabe](../ar/README.md) | [Bengali](../bn/README.md) | [Bulgare](../bg/README.md) | [Birman (Myanmar)](../my/README.md) | [Chinois (Simplifié)](../zh/README.md) | [Chinois (Traditionnel, Hong Kong)](../hk/README.md) | [Chinois (Traditionnel, Macao)](../mo/README.md) | [Chinois (Traditionnel, Taïwan)](../tw/README.md) | [Croate](../hr/README.md) | [Tchèque](../cs/README.md) | [Danois](../da/README.md) | [Néerlandais](../nl/README.md) | [Estonien](../et/README.md) | [Finnois](../fi/README.md) | [Français](./README.md) | [Allemand](../de/README.md) | [Grec](../el/README.md) | [Hébreu](../he/README.md) | [Hindi](../hi/README.md) | [Hongrois](../hu/README.md) | [Indonésien](../id/README.md) | [Italien](../it/README.md) | [Japonais](../ja/README.md) | [Kannada](../kn/README.md) | [Coréen](../ko/README.md) | [Lituanien](../lt/README.md) | [Malais](../ms/README.md) | [Malayalam](../ml/README.md) | [Marathi](../mr/README.md) | [Népalais](../ne/README.md) | [Pidgin nigérian](../pcm/README.md) | [Norvégien](../no/README.md) | [Persan (Farsi)](../fa/README.md) | [Polonais](../pl/README.md) | [Portugais (Brésil)](../br/README.md) | [Portugais (Portugal)](../pt/README.md) | [Pendjabi (Gurmukhi)](../pa/README.md) | [Roumain](../ro/README.md) | [Russe](../ru/README.md) | [Serbe (Cyrillique)](../sr/README.md) | [Slovaque](../sk/README.md) | [Slovène](../sl/README.md) | [Espagnol](../es/README.md) | [Swahili](../sw/README.md) | [Suédois](../sv/README.md) | [Tagalog (Filipino)](../tl/README.md) | [Tamoul](../ta/README.md) | [Telugu](../te/README.md) | [Thaïlandais](../th/README.md) | [Turc](../tr/README.md) | [Ukrainien](../uk/README.md) | [Ourdou](../ur/README.md) | [Vietnamien](../vi/README.md) +[Arabe](../ar/README.md) | [Bengali](../bn/README.md) | [Bulgare](../bg/README.md) | [Birman (Myanmar)](../my/README.md) | [Chinois (simplifié)](../zh-CN/README.md) | [Chinois (traditionnel, Hong Kong)](../zh-HK/README.md) | [Chinois (traditionnel, Macao)](../zh-MO/README.md) | [Chinois (traditionnel, Taïwan)](../zh-TW/README.md) | [Croate](../hr/README.md) | [Tchèque](../cs/README.md) | [Danois](../da/README.md) | [Néerlandais](../nl/README.md) | [Estonien](../et/README.md) | [Finnois](../fi/README.md) | [Français](./README.md) | [Allemand](../de/README.md) | [Grec](../el/README.md) | [Hébreu](../he/README.md) | [Hindi](../hi/README.md) | [Hongrois](../hu/README.md) | [Indonésien](../id/README.md) | [Italien](../it/README.md) | [Japonais](../ja/README.md) | [Kannada](../kn/README.md) | [Coréen](../ko/README.md) | [Lituanien](../lt/README.md) | [Malais](../ms/README.md) | [Malayalam](../ml/README.md) | [Marathi](../mr/README.md) | [Népalais](../ne/README.md) | [Pidgin nigérian](../pcm/README.md) | [Norvégien](../no/README.md) | [Persan (Farsi)](../fa/README.md) | [Polonais](../pl/README.md) | [Portugais (Brésil)](../pt-BR/README.md) | [Portugais (Portugal)](../pt-PT/README.md) | [Pendjabi (Gurmukhi)](../pa/README.md) | [Roumain](../ro/README.md) | [Russe](../ru/README.md) | [Serbe (cyrillique)](../sr/README.md) | [Slovaque](../sk/README.md) | [Slovène](../sl/README.md) | [Espagnol](../es/README.md) | [Swahili](../sw/README.md) | [Suédois](../sv/README.md) | [Tagalog (Filipino)](../tl/README.md) | [Tamoul](../ta/README.md) | [Télougou](../te/README.md) | [Thaï](../th/README.md) | [Turc](../tr/README.md) | [Ukrainien](../uk/README.md) | [Ourdou](../ur/README.md) | [Vietnamien](../vi/README.md) > **Vous préférez cloner localement ?** -> Ce dépôt comprend plus de 50 traductions linguistiques, ce qui augmente considérablement la taille de téléchargement. Pour cloner sans les traductions, utilisez le checkout partiel : +> Ce dépôt inclut plus de 50 traductions de langues, ce qui augmente considérablement la taille du téléchargement. Pour cloner sans les traductions, utilisez le checkout sparse : > ```bash > git clone --filter=blob:none --sparse https://github.com/microsoft/ML-For-Beginners.git > cd ML-For-Beginners > git sparse-checkout set --no-cone '/*' '!translations' '!translated_images' > ``` -> Cela vous fournit tout ce dont vous avez besoin pour compléter le cours avec un téléchargement beaucoup plus rapide. +> Cela vous donne tout ce dont vous avez besoin pour compléter le cours avec un téléchargement beaucoup plus rapide. -#### Rejoignez Notre Communauté +#### Rejoignez notre communauté [![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG) -Nous avons une série Discord apprendre avec l'IA en cours, pour en savoir plus et nous rejoindre, consultez [Learn with AI Series](https://aka.ms/learnwithai/discord) du 18 au 30 septembre 2025. Vous recevrez astuces et conseils pour utiliser GitHub Copilot en Data Science. +Nous avons une série Discord "learn with AI" en cours, apprenez-en plus et rejoignez-nous sur [Learn with AI Series](https://aka.ms/learnwithai/discord) du 18 au 30 septembre 2025. Vous recevrez des astuces et conseils pour utiliser GitHub Copilot en Data Science. -![Learn with AI series](../../../../translated_images/fr/3.9b58fd8d6c373c20.webp) +![Série Learn with AI](../../translated_images/fr/3.9b58fd8d6c373c20.webp) -# Apprentissage Automatique pour Débutants - Un Programme +# Machine Learning pour débutants - Un programme -> 🌍 Faites le tour du monde en explorant l'Apprentissage Automatique à travers les cultures mondiales 🌍 +> 🌍 Voyagez autour du monde en explorant le Machine Learning au travers des cultures mondiales 🌍 -Les Cloud Advocates de Microsoft ont le plaisir de proposer un programme de 12 semaines, 26 leçons entièrement dédié à **l’Apprentissage Automatique**. Dans ce cursus, vous apprendrez ce que l’on appelle parfois **l’apprentissage automatique classique**, utilisant principalement Scikit-learn comme bibliothèque et évitant le deep learning, qui est couvert dans notre [curriculum IA pour débutants](https://aka.ms/ai4beginners). Associez ces leçons à notre [curriculum 'Data Science pour débutants'](https://aka.ms/ds4beginners) également ! +Les Cloud Advocates de Microsoft sont heureux d’offrir un programme de 12 semaines, 26 leçons consacré au **Machine Learning**. Dans ce programme, vous apprendrez ce que l’on appelle parfois le **machine learning classique**, utilisant principalement Scikit-learn comme bibliothèque et évitant le deep learning, couvert dans notre [programme AI pour débutants](https://aka.ms/ai4beginners). Associez ces leçons à notre [programme Data Science pour débutants](https://aka.ms/ds4beginners), aussi ! -Voyagez avec nous autour du monde en appliquant ces techniques classiques à des données provenant de nombreuses régions du globe. Chaque leçon comprend des quiz avant et après la leçon, des instructions écrites pour compléter la leçon, une solution, un devoir, et plus encore. Notre pédagogie basée sur les projets vous permet d’apprendre en construisant, une méthode éprouvée pour que les nouvelles compétences « collent ». +Voyagez avec nous autour du monde en appliquant ces techniques classiques à des données de nombreuses régions. Chaque leçon inclut des quiz avant et après la leçon, des instructions écrites pour la compléter, une solution, un devoir, et plus encore. Notre pédagogie basée sur des projets vous permet d'apprendre en construisant, une méthode éprouvée pour ancrer les nouvelles compétences. -**✍️ Un grand merci à nos auteurs** Jen Looper, Stephen Howell, Francesca Lazzeri, Tomomi Imura, Cassie Breviu, Dmitry Soshnikov, Chris Noring, Anirban Mukherjee, Ornella Altunyan, Ruth Yakubu et Amy Boyd +**✍️ Un grand merci à nos auteurs** Jen Looper, Stephen Howell, Francesca Lazzeri, Tomomi Imura, Cassie Breviu, Dmitry Soshnikov, Chris Noring, Anirban Mukherjee, Ornella Altunyan, Ruth Yakubu, et Amy Boyd -**🎨 Merci également à nos illustrateurs** Tomomi Imura, Dasani Madipalli, et Jen Looper +**🎨 Merci également à nos illustrateurs** Tomomi Imura, Dasani Madipalli et Jen Looper -**🙏 Remerciements particuliers 🙏 à nos auteurs, relecteurs et contributeurs étudiants Microsoft**, notamment Rishit Dagli, Muhammad Sakib Khan Inan, Rohan Raj, Alexandru Petrescu, Abhishek Jaiswal, Nawrin Tabassum, Ioan Samuila, et Snigdha Agarwal +**🙏 Remerciements particuliers 🙏 à nos auteurs étudiants ambassadeurs Microsoft, relecteurs, et contributeurs de contenu**, notamment Rishit Dagli, Muhammad Sakib Khan Inan, Rohan Raj, Alexandru Petrescu, Abhishek Jaiswal, Nawrin Tabassum, Ioan Samuila, et Snigdha Agarwal -**🤩 Une gratitude spéciale aux Ambassadeurs Étudiants Microsoft Eric Wanjau, Jasleen Sondhi, et Vidushi Gupta pour nos leçons R !** +**🤩 Merci tout spécial aux ambassadeurs étudiants Microsoft Eric Wanjau, Jasleen Sondhi et Vidushi Gupta pour nos leçons en R !** -# Commencer +# Premiers pas Suivez ces étapes : -1. **Forkez le dépôt** : Cliquez sur le bouton « Fork » en haut à droite de cette page. -2. **Clonez le dépôt** : `git clone https://github.com/microsoft/ML-For-Beginners.git` +1. **Forkez le dépôt** : Cliquez sur le bouton "Fork" en haut à droite de cette page. +2. **Clonez le dépôt** : `git clone https://github.com/microsoft/ML-For-Beginners.git` -> [trouvez toutes les ressources supplémentaires pour ce cours dans notre collection Microsoft Learn](https://learn.microsoft.com/en-us/collections/qrqzamz1nn2wx3?WT.mc_id=academic-77952-bethanycheum) +> [tous les ressources supplémentaires pour ce cours sont dans notre collection Microsoft Learn](https://learn.microsoft.com/en-us/collections/qrqzamz1nn2wx3?WT.mc_id=academic-77952-bethanycheum) -> 🔧 **Besoin d’aide ?** Consultez notre [Guide de dépannage](TROUBLESHOOTING.md) pour les solutions aux problèmes fréquents d’installation, de configuration et d’exécution des leçons. +> 🔧 **Besoin d'aide ?** Consultez notre [Guide de dépannage](TROUBLESHOOTING.md) pour les solutions aux problèmes fréquents d’installation, configuration et exécution des leçons. -**[Étudiants](https://aka.ms/student-page)**, pour utiliser ce programme, forkez tout le dépôt sur votre propre compte GitHub et réalisez les exercices seul ou en groupe : +**[Étudiants](https://aka.ms/student-page)**, pour utiliser ce programme, forkez le dépôt entier vers votre propre compte GitHub et faites les exercices seul ou en groupe : -- Commencez par un quiz avant la leçon. -- Lisez la leçon et complétez les activités, en faisant des pauses pour réfléchir à chaque point de contrôle des connaissances. -- Essayez de créer les projets en comprenant les leçons plutôt qu’en exécutant simplement le code solution ; cependant, ce code est disponible dans les dossiers `/solution` de chaque leçon orientée projet. -- Passez le quiz après la leçon. -- Réalisez le challenge. -- Effectuez le devoir. -- Après avoir terminé un groupe de leçons, visitez le [forum de discussion](https://github.com/microsoft/ML-For-Beginners/discussions) et « apprenez à voix haute » en remplissant la grille PAT appropriée. Un 'PAT' est un outil d’évaluation des progrès que vous remplissez pour approfondir votre apprentissage. Vous pouvez aussi réagir aux autres PAT afin que nous apprenions ensemble. +- Commencez par un quiz pré-conférence. +- Lisez la leçon et complétez les activités, en faisant des pauses pour réfléchir à chaque vérification de connaissances. +- Essayez de créer les projets en comprenant les leçons plutôt qu’en exécutant directement le code de solution ; toutefois ce code est disponible dans les dossiers `/solution` de chaque leçon orientée projet. +- Faites le quiz post-conférence. +- Complétez le défi. +- Faites le devoir. +- Après avoir terminé un groupe de leçons, visitez le [Forum de discussion](https://github.com/microsoft/ML-For-Beginners/discussions) et "apprenez à voix haute" en remplissant la grille PAT appropriée. Un 'PAT' est un outil d’évaluation des progrès que vous remplissez pour approfondir votre apprentissage. Vous pouvez aussi réagir aux PAT des autres pour apprendre ensemble. -> Pour aller plus loin, nous recommandons de suivre ces modules et parcours d’apprentissage [Microsoft Learn](https://docs.microsoft.com/en-us/users/jenlooper-2911/collections/k7o7tg1gp306q4?WT.mc_id=academic-77952-leestott). +> Pour continuer vos études, nous recommandons de suivre ces modules et parcours d’apprentissage [Microsoft Learn](https://docs.microsoft.com/en-us/users/jenlooper-2911/collections/k7o7tg1gp306q4?WT.mc_id=academic-77952-leestott). -**Enseignants**, nous avons [inclus quelques suggestions](for-teachers.md) sur la façon d’utiliser ce programme. +**Enseignants**, nous avons [inclus quelques suggestions](for-teachers.md) sur la manière d’utiliser ce curriculum. --- ## Vidéos explicatives -Certaines leçons sont disponibles sous forme de courtes vidéos. Vous pouvez les trouver intégrées dans les leçons, ou sur la [playlist ML for Beginners de la chaîne Microsoft Developer YouTube](https://aka.ms/ml-beginners-videos) en cliquant sur l’image ci-dessous. +Certaines leçons sont disponibles en format vidéo courte. Vous pouvez toutes les retrouver intégrées dans les leçons, ou dans la [playlist ML for Beginners sur la chaîne Microsoft Developer YouTube](https://aka.ms/ml-beginners-videos) en cliquant sur l’image ci-dessous. -[![ML for beginners banner](../../../../translated_images/fr/ml-for-beginners-video-banner.63f694a100034bc6.webp)](https://aka.ms/ml-beginners-videos) +[![Bannière ML pour débutants](../../translated_images/fr/ml-for-beginners-video-banner.63f694a100034bc6.webp)](https://aka.ms/ml-beginners-videos) --- -## Rencontrez l’Équipe +## Rencontrez l’équipe -[![Promo video](../../images/ml.gif)](https://youtu.be/Tj1XWrDSYJU) +[![Vidéo promo](../../images/ml.gif)](https://youtu.be/Tj1XWrDSYJU) -**Gif par** [Mohit Jaisal](https://linkedin.com/in/mohitjaisal) +**Gif créé par** [Mohit Jaisal](https://linkedin.com/in/mohitjaisal) > 🎥 Cliquez sur l’image ci-dessus pour une vidéo sur le projet et les personnes qui l’ont créé ! @@ -106,68 +97,68 @@ Certaines leçons sont disponibles sous forme de courtes vidéos. Vous pouvez le ## Pédagogie -Nous avons choisi deux principes pédagogiques lors de la création de ce programme : s’assurer qu’il soit **basé sur des projets pratiques** et qu’il inclue des **quiz fréquents**. De plus, ce programme a un **thème** commun pour lui donner de la cohésion. - -En veillant à ce que le contenu soit aligné sur des projets, le processus devient plus engageant pour les étudiants et la rétention des concepts sera augmentée. De plus, un quiz à faibles enjeux avant une classe oriente l’intention de l’étudiant vers l’apprentissage d’un sujet, tandis qu’un second quiz après la classe assure une meilleure rétention. Ce programme a été conçu pour être flexible et amusant et peut être réalisé en totalité ou en partie. Les projets commencent petits et deviennent de plus en plus complexes au fil des 12 semaines. Ce programme inclut également un épilogue sur les applications réelles de l’apprentissage automatique, pouvant servir de capital supplémentaire ou de base de discussion. - -> Retrouvez notre [Code de conduite](CODE_OF_CONDUCT.md), [Contribuer](CONTRIBUTING.md), [Traduction](TRANSLATIONS.md) et [Dépannage](TROUBLESHOOTING.md). Vos retours constructifs sont les bienvenus ! - -## Chaque leçon comprend - -- un sketchnote optionnel -- une vidéo complémentaire optionnelle -- une vidéo explicative (certaines leçons uniquement) -- [quiz préparatoire avant la leçon](https://ff-quizzes.netlify.app/en/ml/) -- une leçon écrite -- pour les leçons basées sur un projet, des guides étape par étape pour construire le projet -- des points de contrôle des connaissances -- un challenge -- des lectures complémentaires -- un devoir -- [quiz après la leçon](https://ff-quizzes.netlify.app/en/ml/) - -> **Une note sur les langues** : Ces leçons sont principalement écrites en Python, mais beaucoup sont aussi disponibles en R. Pour compléter une leçon en R, allez dans le dossier `/solution` et cherchez les leçons R. Elles comprennent une extension .rmd qui représente un fichier **R Markdown** pouvant être défini simplement comme un mélange de « blocs de code » (de R ou autres langages) et d’un « en-tête YAML » (qui guide la mise en forme des sorties telles que PDF) dans un document Markdown. En tant que tel, il sert de cadre d’auteur exemplaire pour la science des données puisqu’il vous permet de combiner votre code, sa sortie et vos pensées en les écrivant en Markdown. De plus, les documents R Markdown peuvent être rendus à des formats de sortie comme PDF, HTML ou Word. -> **Une note à propos des quiz** : Tous les quiz sont contenus dans le [dossier Quiz App](../../quiz-app), pour un total de 52 quiz composés de trois questions chacun. Ils sont liés depuis les leçons mais l’application de quiz peut être exécutée localement ; suivez les instructions dans le dossier `quiz-app` pour héberger localement ou déployer sur Azure. - -| Numéro de la leçon | Sujet | Regroupement des leçons | Objectifs d’apprentissage | Leçon liée | Auteur | -| :-----------------: | :------------------------------------------------------------: | :---------------------------------------------------------: | --------------------------------------------------------------------------------------------------------------------------------- | :--------------------------------------------------------------------------------------------------------------------------------------: | :--------------------------------------------------: | -| 01 | Introduction à l’apprentissage automatique | [Introduction](1-Introduction/README.md) | Apprenez les concepts de base de l’apprentissage automatique | [Leçon](1-Introduction/1-intro-to-ML/README.md) | Muhammad | -| 02 | L’histoire de l’apprentissage automatique | [Introduction](1-Introduction/README.md) | Apprenez l’histoire sous-jacente à ce domaine | [Leçon](1-Introduction/2-history-of-ML/README.md) | Jen et Amy | -| 03 | Équité et apprentissage automatique | [Introduction](1-Introduction/README.md) | Quels sont les enjeux philosophiques importants liés à l’équité que les étudiants doivent considérer lorsqu’ils construisent et appliquent des modèles ML ? | [Leçon](1-Introduction/3-fairness/README.md) | Tomomi | -| 04 | Techniques d’apprentissage automatique | [Introduction](1-Introduction/README.md) | Quelles techniques les chercheurs ML utilisent-ils pour construire des modèles ML ? | [Leçon](1-Introduction/4-techniques-of-ML/README.md) | Chris et Jen | -| 05 | Introduction à la régression | [Régression](2-Regression/README.md) | Commencez avec Python et Scikit-learn pour les modèles de régression | [Python](2-Regression/1-Tools/README.md) • [R](../../2-Regression/1-Tools/solution/R/lesson_1.html) | Jen • Eric Wanjau | -| 06 | Prix des citrouilles en Amérique du Nord 🎃 | [Régression](2-Regression/README.md) | Visualisez et nettoyez les données en préparation pour le ML | [Python](2-Regression/2-Data/README.md) • [R](../../2-Regression/2-Data/solution/R/lesson_2.html) | Jen • Eric Wanjau | -| 07 | Prix des citrouilles en Amérique du Nord 🎃 | [Régression](2-Regression/README.md) | Construisez des modèles de régression linéaire et polynomiale | [Python](2-Regression/3-Linear/README.md) • [R](../../2-Regression/3-Linear/solution/R/lesson_3.html) | Jen et Dmitry • Eric Wanjau | -| 08 | Prix des citrouilles en Amérique du Nord 🎃 | [Régression](2-Regression/README.md) | Construisez un modèle de régression logistique | [Python](2-Regression/4-Logistic/README.md) • [R](../../2-Regression/4-Logistic/solution/R/lesson_4.html) | Jen • Eric Wanjau | -| 09 | Une application web 🔌 | [Application web](3-Web-App/README.md) | Construisez une application web pour utiliser votre modèle entraîné | [Python](3-Web-App/1-Web-App/README.md) | Jen | -| 10 | Introduction à la classification | [Classification](4-Classification/README.md) | Nettoyez, préparez et visualisez vos données ; introduction à la classification | [Python](4-Classification/1-Introduction/README.md) • [R](../../4-Classification/1-Introduction/solution/R/lesson_10.html) | Jen et Cassie • Eric Wanjau | -| 11 | Cuisines asiatiques et indiennes délicieuses 🍜 | [Classification](4-Classification/README.md) | Introduction aux classificateurs | [Python](4-Classification/2-Classifiers-1/README.md) • [R](../../4-Classification/2-Classifiers-1/solution/R/lesson_11.html) | Jen et Cassie • Eric Wanjau | -| 12 | Cuisines asiatiques et indiennes délicieuses 🍜 | [Classification](4-Classification/README.md) | Plus de classificateurs | [Python](4-Classification/3-Classifiers-2/README.md) • [R](../../4-Classification/3-Classifiers-2/solution/R/lesson_12.html) | Jen et Cassie • Eric Wanjau | -| 13 | Cuisines asiatiques et indiennes délicieuses 🍜 | [Classification](4-Classification/README.md) | Construisez une application web de recommandation utilisant votre modèle | [Python](4-Classification/4-Applied/README.md) | Jen | -| 14 | Introduction au clustering | [Clustering](5-Clustering/README.md) | Nettoyez, préparez et visualisez vos données ; introduction au clustering | [Python](5-Clustering/1-Visualize/README.md) • [R](../../5-Clustering/1-Visualize/solution/R/lesson_14.html) | Jen • Eric Wanjau | -| 15 | Exploration des goûts musicaux nigérians 🎧 | [Clustering](5-Clustering/README.md) | Explorez la méthode de clustering K-Means | [Python](5-Clustering/2-K-Means/README.md) • [R](../../5-Clustering/2-K-Means/solution/R/lesson_15.html) | Jen • Eric Wanjau | -| 16 | Introduction au traitement automatique du langage naturel ☕️ | [Traitement du langage naturel](6-NLP/README.md) | Apprenez les bases du TAL en construisant un bot simple | [Python](6-NLP/1-Introduction-to-NLP/README.md) | Stephen | -| 17 | Tâches courantes en TAL ☕️ | [Traitement du langage naturel](6-NLP/README.md) | Approfondissez vos connaissances en TAL en comprenant les tâches courantes nécessaires au traitement des structures linguistiques | [Python](6-NLP/2-Tasks/README.md) | Stephen | -| 18 | Traduction et analyse de sentiment ♥️ | [Traitement du langage naturel](6-NLP/README.md) | Traduction et analyse de sentiment avec Jane Austen | [Python](6-NLP/3-Translation-Sentiment/README.md) | Stephen | -| 19 | Hôtels romantiques d’Europe ♥️ | [Traitement du langage naturel](6-NLP/README.md) | Analyse de sentiment avec des avis d’hôtels 1 | [Python](6-NLP/4-Hotel-Reviews-1/README.md) | Stephen | -| 20 | Hôtels romantiques d’Europe ♥️ | [Traitement du langage naturel](6-NLP/README.md) | Analyse de sentiment avec des avis d’hôtels 2 | [Python](6-NLP/5-Hotel-Reviews-2/README.md) | Stephen | -| 21 | Introduction à la prévision des séries temporelles | [Séries temporelles](7-TimeSeries/README.md) | Introduction à la prévision des séries temporelles | [Python](7-TimeSeries/1-Introduction/README.md) | Francesca | -| 22 | ⚡️ Consommation d’électricité mondiale ⚡️ — prévision avec ARIMA | [Séries temporelles](7-TimeSeries/README.md) | Prévision de séries temporelles avec ARIMA | [Python](7-TimeSeries/2-ARIMA/README.md) | Francesca | -| 23 | ⚡️ Consommation d’électricité mondiale ⚡️ — prévision avec SVR | [Séries temporelles](7-TimeSeries/README.md) | Prévision de séries temporelles avec Support Vector Regressor | [Python](7-TimeSeries/3-SVR/README.md) | Anirban | -| 24 | Introduction à l’apprentissage par renforcement | [Apprentissage par renforcement](8-Reinforcement/README.md) | Introduction à l’apprentissage par renforcement avec Q-Learning | [Python](8-Reinforcement/1-QLearning/README.md) | Dmitry | -| 25 | Aidez Peter à éviter le loup ! 🐺 | [Apprentissage par renforcement](8-Reinforcement/README.md) | Gym en apprentissage par renforcement | [Python](8-Reinforcement/2-Gym/README.md) | Dmitry | -| Post-scriptum | Scénarios et applications ML réels | [ML dans la nature](9-Real-World/README.md) | Applications réelles intéressantes et révélatrices de l’apprentissage automatique classique | [Leçon](9-Real-World/1-Applications/README.md) | Équipe | -| Post-scriptum | Débogage de modèles ML avec le tableau de bord RAI | [ML dans la nature](9-Real-World/README.md) | Débogage de modèles en apprentissage automatique utilisant les composants du tableau de bord Responsible AI | [Leçon](9-Real-World/2-Debugging-ML-Models/README.md) | Ruth Yakubu | +Nous avons choisi deux principes pédagogiques lors de la construction de ce programme : garantir qu’il soit **pratique, axé sur les projets**, et qu’il intègre des **quiz fréquents**. De plus, ce programme a un **thème commun** pour lui donner de la cohésion. + +En s’assurant que le contenu est aligné sur des projets, le processus devient plus engageant pour les étudiants et la rétention des concepts sera accrue. De plus, un quiz à faible enjeu avant la classe fixe l’intention d’apprentissage de l’étudiant, tandis qu’un deuxième quiz après la classe assure une meilleure rétention. Ce programme a été conçu pour être flexible et ludique, et peut être suivi en totalité ou partiellement. Les projets commencent petits et deviennent de plus en plus complexes à la fin des 12 semaines. Ce programme inclut également un post-scriptum sur les applications réelles du ML, qui peut être utilisé comme un travail supplémentaire ou comme base à une discussion. + +> Trouvez nos guides [Code de conduite](CODE_OF_CONDUCT.md), [Contribution](CONTRIBUTING.md), [Traduction](TRANSLATIONS.md), et [Dépannage](TROUBLESHOOTING.md). Nous accueillons vos retours constructifs ! + +## Chaque leçon inclut + +- sketchnote optionnel +- vidéo complémentaire optionnelle +- vidéo explicative (certaines leçons seulement) +- [quiz d’échauffement pré-conférence](https://ff-quizzes.netlify.app/en/ml/) +- leçon écrite +- pour les leçons basées sur un projet, guides étape par étape pour construire le projet +- vérifications des connaissances +- un défi +- lectures complémentaires +- devoir +- [quiz post-conférence](https://ff-quizzes.netlify.app/en/ml/) + +> **Un mot sur les langues** : Ces leçons sont principalement écrites en Python, mais beaucoup sont aussi disponibles en R. Pour faire une leçon en R, allez dans le dossier `/solution` et cherchez les leçons R. Elles incluent une extension .rmd, qui représente un fichier **R Markdown** que l’on peut simplement définir comme un mélange de `blocs de code` (en R ou autres langues) et d’`entête YAML` (qui guide la mise en forme des sorties comme PDF) dans un `document Markdown`. En ce sens, il sert de cadre idéal pour l’écriture en data science, puisqu’il vous permet de combiner votre code, sa sortie, et vos réflexions en les écrivant en Markdown. De plus, les documents R Markdown peuvent être exportés vers des formats comme PDF, HTML ou Word. +> **Une note à propos des quiz** : Tous les quiz sont contenus dans le [dossier Quiz App](../../quiz-app), pour un total de 52 quiz composés de trois questions chacun. Ils sont liés depuis les leçons, mais l'application de quiz peut être exécutée localement ; suivez les instructions dans le dossier `quiz-app` pour héberger ou déployer localement sur Azure. + +| Numéro de la leçon | Sujet | Regroupement des leçons | Objectifs d'apprentissage | Leçon liée | Auteur | +| :-----------------: | :----------------------------------------------------------: | :----------------------------------------------------: | ------------------------------------------------------------------------------------------------------------------------------- | :---------------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------: | +| 01 | Introduction à l'apprentissage machine | [Introduction](1-Introduction/README.md) | Apprenez les concepts de base derrière l'apprentissage machine | [Leçon](1-Introduction/1-intro-to-ML/README.md) | Muhammad | +| 02 | L'histoire de l'apprentissage machine | [Introduction](1-Introduction/README.md) | Apprenez l'histoire sous-jacente de ce domaine | [Leçon](1-Introduction/2-history-of-ML/README.md) | Jen et Amy | +| 03 | Équité et apprentissage machine | [Introduction](1-Introduction/README.md) | Quels sont les enjeux philosophiques importants autour de l'équité que les étudiants doivent considérer lors de la construction et de l'application de modèles ML ? | [Leçon](1-Introduction/3-fairness/README.md) | Tomomi | +| 04 | Techniques pour l'apprentissage machine | [Introduction](1-Introduction/README.md) | Quelles techniques les chercheurs en ML utilisent-ils pour construire des modèles ML ? | [Leçon](1-Introduction/4-techniques-of-ML/README.md) | Chris et Jen | +| 05 | Introduction à la régression | [Régression](2-Regression/README.md) | Débutez avec Python et Scikit-learn pour les modèles de régression | [Python](2-Regression/1-Tools/README.md) • [R](../../2-Regression/1-Tools/solution/R/lesson_1.html) | Jen • Eric Wanjau | +| 06 | Prix des citrouilles en Amérique du Nord 🎃 | [Régression](2-Regression/README.md) | Visualisez et nettoyez les données en préparation pour le ML | [Python](2-Regression/2-Data/README.md) • [R](../../2-Regression/2-Data/solution/R/lesson_2.html) | Jen • Eric Wanjau | +| 07 | Prix des citrouilles en Amérique du Nord 🎃 | [Régression](2-Regression/README.md) | Construisez des modèles de régression linéaire et polynomiale | [Python](2-Regression/3-Linear/README.md) • [R](../../2-Regression/3-Linear/solution/R/lesson_3.html) | Jen et Dmitry • Eric Wanjau | +| 08 | Prix des citrouilles en Amérique du Nord 🎃 | [Régression](2-Regression/README.md) | Construisez un modèle de régression logistique | [Python](2-Regression/4-Logistic/README.md) • [R](../../2-Regression/4-Logistic/solution/R/lesson_4.html) | Jen • Eric Wanjau | +| 09 | Une application Web 🔌 | [Application Web](3-Web-App/README.md) | Construisez une application web pour utiliser votre modèle entraîné | [Python](3-Web-App/1-Web-App/README.md) | Jen | +| 10 | Introduction à la classification | [Classification](4-Classification/README.md) | Nettoyez, préparez et visualisez vos données ; introduction à la classification | [Python](4-Classification/1-Introduction/README.md) • [R](../../4-Classification/1-Introduction/solution/R/lesson_10.html) | Jen et Cassie • Eric Wanjau | +| 11 | Plats délicieux asiatiques et indiens 🍜 | [Classification](4-Classification/README.md) | Introduction aux classificateurs | [Python](4-Classification/2-Classifiers-1/README.md) • [R](../../4-Classification/2-Classifiers-1/solution/R/lesson_11.html) | Jen et Cassie • Eric Wanjau | +| 12 | Plats délicieux asiatiques et indiens 🍜 | [Classification](4-Classification/README.md) | Plus de classificateurs | [Python](4-Classification/3-Classifiers-2/README.md) • [R](../../4-Classification/3-Classifiers-2/solution/R/lesson_12.html) | Jen et Cassie • Eric Wanjau | +| 13 | Plats délicieux asiatiques et indiens 🍜 | [Classification](4-Classification/README.md) | Construisez une application web de recommandation utilisant votre modèle | [Python](4-Classification/4-Applied/README.md) | Jen | +| 14 | Introduction à la classification | [Clustering](5-Clustering/README.md) | Nettoyez, préparez et visualisez vos données ; introduction au clustering | [Python](5-Clustering/1-Visualize/README.md) • [R](../../5-Clustering/1-Visualize/solution/R/lesson_14.html) | Jen • Eric Wanjau | +| 15 | Exploration des goûts musicaux nigérians 🎧 | [Clustering](5-Clustering/README.md) | Explorez la méthode de clustering K-Means | [Python](5-Clustering/2-K-Means/README.md) • [R](../../5-Clustering/2-K-Means/solution/R/lesson_15.html) | Jen • Eric Wanjau | +| 16 | Introduction au traitement du langage naturel ☕️ | [Traitement du langage naturel](6-NLP/README.md) | Apprenez les bases du NLP en construisant un bot simple | [Python](6-NLP/1-Introduction-to-NLP/README.md) | Stephen | +| 17 | Tâches courantes en NLP ☕️ | [Traitement du langage naturel](6-NLP/README.md) | Approfondissez vos connaissances en NLP en comprenant les tâches courantes lors de la gestion des structures linguistiques | [Python](6-NLP/2-Tasks/README.md) | Stephen | +| 18 | Traduction et analyse des sentiments ♥️ | [Traitement du langage naturel](6-NLP/README.md) | Traduction et analyse des sentiments avec Jane Austen | [Python](6-NLP/3-Translation-Sentiment/README.md) | Stephen | +| 19 | Hôtels romantiques d'Europe ♥️ | [Traitement du langage naturel](6-NLP/README.md) | Analyse des sentiments avec des avis d'hôtels 1 | [Python](6-NLP/4-Hotel-Reviews-1/README.md) | Stephen | +| 20 | Hôtels romantiques d'Europe ♥️ | [Traitement du langage naturel](6-NLP/README.md) | Analyse des sentiments avec des avis d'hôtels 2 | [Python](6-NLP/5-Hotel-Reviews-2/README.md) | Stephen | +| 21 | Introduction aux prévisions de séries temporelles | [Séries temporelles](7-TimeSeries/README.md) | Introduction à la prévision de séries temporelles | [Python](7-TimeSeries/1-Introduction/README.md) | Francesca | +| 22 | ⚡️ Consommation électrique mondiale ⚡️ - prévision de séries temporelles avec ARIMA | [Séries temporelles](7-TimeSeries/README.md) | Prévision de séries temporelles avec ARIMA | [Python](7-TimeSeries/2-ARIMA/README.md) | Francesca | +| 23 | ⚡️ Consommation électrique mondiale ⚡️ - prévision de séries temporelles avec SVR | [Séries temporelles](7-TimeSeries/README.md) | Prévision de séries temporelles avec support vector regressor | [Python](7-TimeSeries/3-SVR/README.md) | Anirban | +| 24 | Introduction à l'apprentissage par renforcement | [Apprentissage par renforcement](8-Reinforcement/README.md) | Introduction à l'apprentissage par renforcement avec Q-Learning | [Python](8-Reinforcement/1-QLearning/README.md) | Dmitry | +| 25 | Aidez Peter à éviter le loup ! 🐺 | [Apprentissage par renforcement](8-Reinforcement/README.md) | Gym d'apprentissage par renforcement | [Python](8-Reinforcement/2-Gym/README.md) | Dmitry | +| Post-scriptum | Scénarios et applications ML du monde réel | [ML dans la nature](9-Real-World/README.md) | Applications intéressantes et révélatrices du ML classique dans le monde réel | [Leçon](9-Real-World/1-Applications/README.md) | Équipe | +| Post-scriptum | Débogage de modèles ML avec le tableau de bord RAI | [ML dans la nature](9-Real-World/README.md) | Débogage de modèles en apprentissage machine en utilisant les composants du tableau de bord AI Responsable | [Leçon](9-Real-World/2-Debugging-ML-Models/README.md) | Ruth Yakubu | > [trouvez toutes les ressources supplémentaires pour ce cours dans notre collection Microsoft Learn](https://learn.microsoft.com/en-us/collections/qrqzamz1nn2wx3?WT.mc_id=academic-77952-bethanycheum) ## Accès hors ligne -Vous pouvez consulter cette documentation hors ligne en utilisant [Docsify](https://docsify.js.org/#/). Forkez ce dépôt, [installez Docsify](https://docsify.js.org/#/quickstart) sur votre machine locale, puis dans le dossier racine de ce dépôt, tapez `docsify serve`. Le site web sera servi sur le port 3000 sur votre localhost : `localhost:3000`. +Vous pouvez consulter cette documentation hors ligne en utilisant [Docsify](https://docsify.js.org/#/). Forkez ce dépôt, [installez Docsify](https://docsify.js.org/#/quickstart) sur votre machine locale, puis dans le dossier racine de ce dépôt, tapez `docsify serve`. Le site sera servi sur le port 3000 sur votre localhost : `localhost:3000`. ## PDFs -Trouvez un PDF du programme avec les liens [ici](https://microsoft.github.io/ML-For-Beginners/pdf/readme.pdf). +Trouvez un pdf du programme avec liens [ici](https://microsoft.github.io/ML-For-Beginners/pdf/readme.pdf). ## 🎒 Autres cours @@ -189,44 +180,44 @@ Notre équipe produit d'autres cours ! Découvrez : --- -### Série d’IA générative -[![Intelligence Artificielle Générative pour Débutants](https://img.shields.io/badge/Generative%20AI%20for%20Beginners-8B5CF6?style=for-the-badge&labelColor=E5E7EB&color=8B5CF6)](https://github.com/microsoft/generative-ai-for-beginners?WT.mc_id=academic-105485-koreyst) -[![Intelligence Artificielle Générative (.NET)](https://img.shields.io/badge/Generative%20AI%20(.NET)-9333EA?style=for-the-badge&labelColor=E5E7EB&color=9333EA)](https://github.com/microsoft/Generative-AI-for-beginners-dotnet?WT.mc_id=academic-105485-koreyst) -[![Intelligence Artificielle Générative (Java)](https://img.shields.io/badge/Generative%20AI%20(Java)-C084FC?style=for-the-badge&labelColor=E5E7EB&color=C084FC)](https://github.com/microsoft/generative-ai-for-beginners-java?WT.mc_id=academic-105485-koreyst) -[![Intelligence Artificielle Générative (JavaScript)](https://img.shields.io/badge/Generative%20AI%20(JavaScript)-E879F9?style=for-the-badge&labelColor=E5E7EB&color=E879F9)](https://github.com/microsoft/generative-ai-with-javascript?WT.mc_id=academic-105485-koreyst) +### Série Intelligence Artificielle Générative +[![IA générative pour débutants](https://img.shields.io/badge/Generative%20AI%20for%20Beginners-8B5CF6?style=for-the-badge&labelColor=E5E7EB&color=8B5CF6)](https://github.com/microsoft/generative-ai-for-beginners?WT.mc_id=academic-105485-koreyst) +[![IA générative (.NET)](https://img.shields.io/badge/Generative%20AI%20(.NET)-9333EA?style=for-the-badge&labelColor=E5E7EB&color=9333EA)](https://github.com/microsoft/Generative-AI-for-beginners-dotnet?WT.mc_id=academic-105485-koreyst) +[![IA générative (Java)](https://img.shields.io/badge/Generative%20AI%20(Java)-C084FC?style=for-the-badge&labelColor=E5E7EB&color=C084FC)](https://github.com/microsoft/generative-ai-for-beginners-java?WT.mc_id=academic-105485-koreyst) +[![IA générative (JavaScript)](https://img.shields.io/badge/Generative%20AI%20(JavaScript)-E879F9?style=for-the-badge&labelColor=E5E7EB&color=E879F9)](https://github.com/microsoft/generative-ai-with-javascript?WT.mc_id=academic-105485-koreyst) --- - + ### Apprentissage Fondamental -[![ML pour Débutants](https://img.shields.io/badge/ML%20for%20Beginners-22C55E?style=for-the-badge&labelColor=E5E7EB&color=22C55E)](https://aka.ms/ml-beginners?WT.mc_id=academic-105485-koreyst) -[![Science des Données pour Débutants](https://img.shields.io/badge/Data%20Science%20for%20Beginners-84CC16?style=for-the-badge&labelColor=E5E7EB&color=84CC16)](https://aka.ms/datascience-beginners?WT.mc_id=academic-105485-koreyst) -[![IA pour Débutants](https://img.shields.io/badge/AI%20for%20Beginners-A3E635?style=for-the-badge&labelColor=E5E7EB&color=A3E635)](https://aka.ms/ai-beginners?WT.mc_id=academic-105485-koreyst) -[![Cybersécurité pour Débutants](https://img.shields.io/badge/Cybersecurity%20for%20Beginners-F97316?style=for-the-badge&labelColor=E5E7EB&color=F97316)](https://github.com/microsoft/Security-101?WT.mc_id=academic-96948-sayoung) -[![Développement Web pour Débutants](https://img.shields.io/badge/Web%20Dev%20for%20Beginners-EC4899?style=for-the-badge&labelColor=E5E7EB&color=EC4899)](https://aka.ms/webdev-beginners?WT.mc_id=academic-105485-koreyst) -[![IoT pour Débutants](https://img.shields.io/badge/IoT%20for%20Beginners-14B8A6?style=for-the-badge&labelColor=E5E7EB&color=14B8A6)](https://aka.ms/iot-beginners?WT.mc_id=academic-105485-koreyst) -[![Développement XR pour Débutants](https://img.shields.io/badge/XR%20Development%20for%20Beginners-38BDF8?style=for-the-badge&labelColor=E5E7EB&color=38BDF8)](https://github.com/microsoft/xr-development-for-beginners?WT.mc_id=academic-105485-koreyst) +[![ML pour débutants](https://img.shields.io/badge/ML%20for%20Beginners-22C55E?style=for-the-badge&labelColor=E5E7EB&color=22C55E)](https://aka.ms/ml-beginners?WT.mc_id=academic-105485-koreyst) +[![Science des données pour débutants](https://img.shields.io/badge/Data%20Science%20for%20Beginners-84CC16?style=for-the-badge&labelColor=E5E7EB&color=84CC16)](https://aka.ms/datascience-beginners?WT.mc_id=academic-105485-koreyst) +[![IA pour débutants](https://img.shields.io/badge/AI%20for%20Beginners-A3E635?style=for-the-badge&labelColor=E5E7EB&color=A3E635)](https://aka.ms/ai-beginners?WT.mc_id=academic-105485-koreyst) +[![Cybersécurité pour débutants](https://img.shields.io/badge/Cybersecurity%20for%20Beginners-F97316?style=for-the-badge&labelColor=E5E7EB&color=F97316)](https://github.com/microsoft/Security-101?WT.mc_id=academic-96948-sayoung) +[![Développement web pour débutants](https://img.shields.io/badge/Web%20Dev%20for%20Beginners-EC4899?style=for-the-badge&labelColor=E5E7EB&color=EC4899)](https://aka.ms/webdev-beginners?WT.mc_id=academic-105485-koreyst) +[![IoT pour débutants](https://img.shields.io/badge/IoT%20for%20Beginners-14B8A6?style=for-the-badge&labelColor=E5E7EB&color=14B8A6)](https://aka.ms/iot-beginners?WT.mc_id=academic-105485-koreyst) +[![Développement XR pour débutants](https://img.shields.io/badge/XR%20Development%20for%20Beginners-38BDF8?style=for-the-badge&labelColor=E5E7EB&color=38BDF8)](https://github.com/microsoft/xr-development-for-beginners?WT.mc_id=academic-105485-koreyst) --- - + ### Série Copilot -[![Copilot pour Programmation Assistée par IA](https://img.shields.io/badge/Copilot%20for%20AI%20Paired%20Programming-FACC15?style=for-the-badge&labelColor=E5E7EB&color=FACC15)](https://aka.ms/GitHubCopilotAI?WT.mc_id=academic-105485-koreyst) +[![Copilot pour programmation assistée par IA](https://img.shields.io/badge/Copilot%20for%20AI%20Paired%20Programming-FACC15?style=for-the-badge&labelColor=E5E7EB&color=FACC15)](https://aka.ms/GitHubCopilotAI?WT.mc_id=academic-105485-koreyst) [![Copilot pour C#/.NET](https://img.shields.io/badge/Copilot%20for%20C%23/.NET-FBBF24?style=for-the-badge&labelColor=E5E7EB&color=FBBF24)](https://github.com/microsoft/mastering-github-copilot-for-dotnet-csharp-developers?WT.mc_id=academic-105485-koreyst) [![Aventure Copilot](https://img.shields.io/badge/Copilot%20Adventure-FDE68A?style=for-the-badge&labelColor=E5E7EB&color=FDE68A)](https://github.com/microsoft/CopilotAdventures?WT.mc_id=academic-105485-koreyst) -## Obtenir de l'Aide +## Obtenir de l'aide -Si vous êtes bloqué ou avez des questions sur la création d'applications d'IA. Rejoignez d'autres apprenants et développeurs expérimentés dans les discussions sur MCP. C'est une communauté bienveillante où les questions sont les bienvenues et les connaissances sont librement partagées. +Si vous êtes bloqué ou si vous avez des questions sur la création d'applications IA. Rejoignez des apprenants et des développeurs expérimentés pour discuter du MCP. C'est une communauté bienveillante où les questions sont les bienvenues et le savoir est partagé librement. [![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG) -Si vous avez des retours sur le produit ou rencontrez des erreurs lors de la création, visitez : +Si vous avez des retours sur les produits ou des erreurs lors du développement, rendez-vous sur : -[![Forum des Développeurs Microsoft Foundry](https://img.shields.io/badge/GitHub-Microsoft_Foundry_Developer_Forum-blue?style=for-the-badge&logo=github&color=000000&logoColor=fff)](https://aka.ms/foundry/forum) +[![Forum développeur Microsoft Foundry](https://img.shields.io/badge/GitHub-Microsoft_Foundry_Developer_Forum-blue?style=for-the-badge&logo=github&color=000000&logoColor=fff)](https://aka.ms/foundry/forum) --- **Avertissement** : -Ce document a été traduit à l’aide du service de traduction automatique [Co-op Translator](https://github.com/Azure/co-op-translator). Bien que nous nous efforcions d'assurer l'exactitude, veuillez noter que les traductions automatisées peuvent contenir des erreurs ou des inexactitudes. Le document original dans sa langue d'origine doit être considéré comme la source faisant foi. Pour les informations critiques, une traduction professionnelle réalisée par un humain est recommandée. Nous déclinons toute responsabilité en cas de malentendus ou de mauvaises interprétations résultant de l’utilisation de cette traduction. +Ce document a été traduit à l’aide du service de traduction automatique [Co-op Translator](https://github.com/Azure/co-op-translator). Bien que nous nous efforcions d’assurer la précision, veuillez noter que les traductions automatiques peuvent comporter des erreurs ou des inexactitudes. Le document original dans sa langue d’origine doit être considéré comme la source faisant foi. Pour les informations critiques, il est recommandé de recourir à une traduction professionnelle réalisée par un humain. Nous ne sommes pas responsables des malentendus ou interprétations erronées résultant de l’utilisation de cette traduction. \ No newline at end of file diff --git a/translations/fr/SECURITY.md b/translations/fr/SECURITY.md index 7b9c14c2c..fd96a26cd 100644 --- a/translations/fr/SECURITY.md +++ b/translations/fr/SECURITY.md @@ -1,12 +1,3 @@ - ## Sécurité Microsoft prend très au sérieux la sécurité de ses produits logiciels et services, y compris tous les dépôts de code source gérés via nos organisations GitHub, qui incluent [Microsoft](https://github.com/Microsoft), [Azure](https://github.com/Azure), [DotNet](https://github.com/dotnet), [AspNet](https://github.com/aspnet), [Xamarin](https://github.com/xamarin), et [nos organisations GitHub](https://opensource.microsoft.com/). diff --git a/translations/fr/SUPPORT.md b/translations/fr/SUPPORT.md index 9bd198bf0..0cd72cb76 100644 --- a/translations/fr/SUPPORT.md +++ b/translations/fr/SUPPORT.md @@ -1,12 +1,3 @@ - # Support ## Comment signaler des problèmes et obtenir de l'aide diff --git a/translations/fr/TROUBLESHOOTING.md b/translations/fr/TROUBLESHOOTING.md index a3e0e24cf..91c642e6e 100644 --- a/translations/fr/TROUBLESHOOTING.md +++ b/translations/fr/TROUBLESHOOTING.md @@ -1,12 +1,3 @@ - # Guide de dépannage Ce guide vous aide à résoudre les problèmes courants rencontrés lors de l'utilisation du programme Machine Learning pour les débutants. Si vous ne trouvez pas de solution ici, consultez nos [discussions sur Discord](https://aka.ms/foundry/discord) ou [ouvrez un ticket](https://github.com/microsoft/ML-For-Beginners/issues). diff --git a/translations/fr/docs/_sidebar.md b/translations/fr/docs/_sidebar.md index 6fa975ba4..16f701e59 100644 --- a/translations/fr/docs/_sidebar.md +++ b/translations/fr/docs/_sidebar.md @@ -1,12 +1,3 @@ - - Introduction - [Introduction au Machine Learning](../1-Introduction/1-intro-to-ML/README.md) - [Histoire du Machine Learning](../1-Introduction/2-history-of-ML/README.md) diff --git a/translations/fr/for-teachers.md b/translations/fr/for-teachers.md index b24a05938..825a566cd 100644 --- a/translations/fr/for-teachers.md +++ b/translations/fr/for-teachers.md @@ -1,12 +1,3 @@ - ## Pour les enseignants Souhaitez-vous utiliser ce programme dans votre classe ? N'hésitez pas ! diff --git a/translations/fr/quiz-app/README.md b/translations/fr/quiz-app/README.md index bbff78dd9..0356775db 100644 --- a/translations/fr/quiz-app/README.md +++ b/translations/fr/quiz-app/README.md @@ -1,12 +1,3 @@ - # Quiz Ces quiz sont les quiz avant et après les cours pour le programme ML disponible sur https://aka.ms/ml-beginners diff --git a/translations/fr/sketchnotes/LICENSE.md b/translations/fr/sketchnotes/LICENSE.md index 7c507ebbe..a12c2dd44 100644 --- a/translations/fr/sketchnotes/LICENSE.md +++ b/translations/fr/sketchnotes/LICENSE.md @@ -1,12 +1,3 @@ - Attribution-ShareAlike 4.0 International ======================================================================= diff --git a/translations/fr/sketchnotes/README.md b/translations/fr/sketchnotes/README.md index 9be9fc938..523843d6c 100644 --- a/translations/fr/sketchnotes/README.md +++ b/translations/fr/sketchnotes/README.md @@ -1,12 +1,3 @@ - Tous les sketchnotes du programme peuvent être téléchargés ici. 🖨 Pour une impression en haute résolution, les versions TIFF sont disponibles sur [ce dépôt](https://github.com/girliemac/a-picture-is-worth-a-1000-words/tree/main/ml/tiff). diff --git a/translations/hk/1-Introduction/README.md b/translations/hk/1-Introduction/README.md index cbffc0a13..81deecd71 100644 --- a/translations/hk/1-Introduction/README.md +++ b/translations/hk/1-Introduction/README.md @@ -11,7 +11,7 @@ CO_OP_TRANSLATOR_METADATA: 在這部分課程中，你將了解機器學習領域的基本概念、它是什麼，以及它的歷史和研究人員使用的技術。讓我們一起探索這個嶄新的機器學習世界吧！ -![globe](../../../translated_images/hk/globe.59f26379ceb40428.webp) +![globe](../../../translated_images/zh-HK/globe.59f26379ceb40428.webp) > 照片由 Bill Oxford 提供，來自 Unsplash ### 課程 diff --git a/translations/hk/2-Regression/1-Tools/solution/R/lesson_1-R.ipynb b/translations/hk/2-Regression/1-Tools/solution/R/lesson_1-R.ipynb index e0ae1767c..901ca5b48 100644 --- a/translations/hk/2-Regression/1-Tools/solution/R/lesson_1-R.ipynb +++ b/translations/hk/2-Regression/1-Tools/solution/R/lesson_1-R.ipynb @@ -48,7 +48,7 @@ " width=\"630\"/>\n", "
由 @allison_horst 創作的藝術作品
\n", "\n", - "\n" + "\n" ], "metadata": { "id": "LWNNzfqd6feZ" diff --git a/translations/hk/2-Regression/2-Data/solution/R/lesson_2-R.ipynb b/translations/hk/2-Regression/2-Data/solution/R/lesson_2-R.ipynb index fc7c9c2ac..bce2d0668 100644 --- a/translations/hk/2-Regression/2-Data/solution/R/lesson_2-R.ipynb +++ b/translations/hk/2-Regression/2-Data/solution/R/lesson_2-R.ipynb @@ -49,7 +49,7 @@ "
插圖由 @allison_horst 提供
\n", "\n", "\n", - "\n" + "\n" ], "metadata": { "id": "Pg5aexcOPqAZ" @@ -230,7 +230,7 @@ "
插圖由 @allison_horst 提供
\n", "\n", "\n", - "\n" + "\n" ], "metadata": { "id": "o4jLY5-VZO2C" @@ -532,7 +532,7 @@ "
資訊圖表由 Dasani Madipalli 製作
\n", "\n", "\n", - "\n", + "\n", "\n", "有一句*智慧*的名言是這樣說的：\n", "\n", diff --git a/translations/hk/2-Regression/3-Linear/solution/R/lesson_3-R.ipynb b/translations/hk/2-Regression/3-Linear/solution/R/lesson_3-R.ipynb index 4011cfdba..dc063d129 100644 --- a/translations/hk/2-Regression/3-Linear/solution/R/lesson_3-R.ipynb +++ b/translations/hk/2-Regression/3-Linear/solution/R/lesson_3-R.ipynb @@ -40,7 +40,7 @@ "
資訊圖表由 Dasani Madipalli 製作
\n", "\n", "\n", - "\n", + "\n", "\n", "#### 簡介\n", "\n", @@ -132,7 +132,7 @@ ">\n", "> 換句話說，參考我們南瓜數據的原始問題：「根據月份預測每蒲式耳南瓜的價格」，`X` 代表價格，`Y` 代表銷售月份。\n", ">\n", - "> ![](../../../../../../translated_images/hk/calculation.989aa7822020d9d0ba9fc781f1ab5192f3421be86ebb88026528aef33c37b0d8.png)\n", + "> ![](../../../../../../translated_images/zh-HK/calculation.989aa7822020d9d0ba9fc781f1ab5192f3421be86ebb88026528aef33c37b0d8.png)\n", " 圖解由 Jen Looper 提供\n", "> \n", "> 計算 Y 的值。如果你支付大約 4 美元，那應該是四月！\n", @@ -164,7 +164,7 @@ "
插圖由 @allison_horst 提供
\n", "\n", "\n", - "\n" + "\n" ], "metadata": { "id": "WdUKXk7Bs8-V" @@ -569,7 +569,7 @@ "
Dasani Madipalli 的資訊圖表
\n", "\n", "\n", - "\n" + "\n" ], "metadata": { "id": "YqXjLuWavNxW" @@ -810,7 +810,7 @@ "
資訊圖表由 Dasani Madipalli 製作
\n", "\n", "\n", - "\n" + "\n" ], "metadata": { "id": "HOCqJXLTwtWI" diff --git a/translations/hk/2-Regression/4-Logistic/solution/R/lesson_4-R.ipynb b/translations/hk/2-Regression/4-Logistic/solution/R/lesson_4-R.ipynb index 0e22cad56..fe19a9463 100644 --- a/translations/hk/2-Regression/4-Logistic/solution/R/lesson_4-R.ipynb +++ b/translations/hk/2-Regression/4-Logistic/solution/R/lesson_4-R.ipynb @@ -6,7 +6,7 @@ "source": [ "## 建立邏輯迴歸模型 - 第四課\n", "\n", - "![邏輯迴歸與線性迴歸資訊圖表](../../../../../../translated_images/hk/linear-vs-logistic.ba180bf95e7ee667.webp)\n", + "![邏輯迴歸與線性迴歸資訊圖表](../../../../../../translated_images/zh-HK/linear-vs-logistic.ba180bf95e7ee667.webp)\n", "\n", "#### **[課前測驗](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/15/)**\n", "\n", @@ -78,7 +78,7 @@ "\n", "邏輯回歸不提供與線性回歸相同的功能。前者提供對「二元類別」（例如「橙色或非橙色」）的預測，而後者則能預測「連續值」，例如根據南瓜的產地和收穫時間，*價格會上漲多少*。\n", "\n", - "![Dasani Madipalli 的資訊圖表](../../../../../../translated_images/hk/pumpkin-classifier.562771f104ad5436.webp)\n", + "![Dasani Madipalli 的資訊圖表](../../../../../../translated_images/zh-HK/pumpkin-classifier.562771f104ad5436.webp)\n", "\n", "### 其他分類方式\n", "\n", @@ -88,7 +88,7 @@ "\n", "- **序列式**，涉及有序的類別，適合我們希望按邏輯順序排列結果的情況，例如南瓜按有限的大小（迷你、小、中、大、特大、超大）排序。\n", "\n", - "![多項式 vs 序列式回歸](../../../../../../translated_images/hk/multinomial-vs-ordinal.36701b4850e37d86.webp)\n", + "![多項式 vs 序列式回歸](../../../../../../translated_images/zh-HK/multinomial-vs-ordinal.36701b4850e37d86.webp)\n", "\n", "#### **變數不需要相關**\n", "\n", diff --git a/translations/hk/2-Regression/README.md b/translations/hk/2-Regression/README.md index 0548b422f..ce5a99dce 100644 --- a/translations/hk/2-Regression/README.md +++ b/translations/hk/2-Regression/README.md @@ -12,7 +12,7 @@ CO_OP_TRANSLATOR_METADATA: 在北美，南瓜經常被雕刻成恐怖的臉孔，用於慶祝萬聖節。讓我們一起探索這些迷人的蔬菜吧！ -![jack-o-lanterns](../../../translated_images/hk/jack-o-lanterns.181c661a9212457d.webp) +![jack-o-lanterns](../../../translated_images/zh-HK/jack-o-lanterns.181c661a9212457d.webp) > 圖片由 Beth Teutschmann 提供，來自 Unsplash ## 你將學到什麼 diff --git a/translations/hk/3-Web-App/README.md b/translations/hk/3-Web-App/README.md index 7a8b6133f..a4edcc958 100644 --- a/translations/hk/3-Web-App/README.md +++ b/translations/hk/3-Web-App/README.md @@ -11,7 +11,7 @@ CO_OP_TRANSLATOR_METADATA: 在這部分課程中，你將學習一個應用機器學習的主題：如何將你的 Scikit-learn 模型保存為一個檔案，並在網頁應用程式中使用它進行預測。當模型保存好後，你將學習如何在使用 Flask 建立的網頁應用程式中使用它。首先，你會使用一些關於 UFO 目擊事件的數據來建立模型！接著，你會建立一個網頁應用程式，讓你輸入秒數、緯度和經度值，來預測哪個國家報告了看到 UFO。 -![UFO 停車場](../../../translated_images/hk/ufo.9e787f5161da9d4d.webp) +![UFO 停車場](../../../translated_images/zh-HK/ufo.9e787f5161da9d4d.webp) 照片由 Michael Herren 提供，來自 Unsplash diff --git a/translations/hk/4-Classification/README.md b/translations/hk/4-Classification/README.md index abe15db2e..d64f4c9bd 100644 --- a/translations/hk/4-Classification/README.md +++ b/translations/hk/4-Classification/README.md @@ -13,7 +13,7 @@ CO_OP_TRANSLATOR_METADATA: 在亞洲和印度，飲食文化非常多元且美味！讓我們來看看有關地區料理的數據，試著了解它們的食材。 -![泰國街頭小販](../../../translated_images/hk/thai-food.c47a7a7f9f05c218.webp) +![泰國街頭小販](../../../translated_images/zh-HK/thai-food.c47a7a7f9f05c218.webp) > 照片由 Lisheng Chang 提供，來自 Unsplash ## 你將學到什麼 diff --git a/translations/hk/5-Clustering/README.md b/translations/hk/5-Clustering/README.md index 5ff2eeca2..3c1ebd80b 100644 --- a/translations/hk/5-Clustering/README.md +++ b/translations/hk/5-Clustering/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: 尼日利亞的多元化觀眾擁有多樣化的音樂品味。使用從 Spotify 抓取的數據（靈感來自[這篇文章](https://towardsdatascience.com/country-wise-visual-analysis-of-music-taste-using-spotify-api-seaborn-in-python-77f5b749b421)），讓我們來看看一些在尼日利亞流行的音樂。這個數據集包含了各種歌曲的「舞蹈性」分數、「聲學性」、音量、「語音性」、流行度和能量等數據。探索這些數據中的模式將會非常有趣！ -![唱盤](../../../translated_images/hk/turntable.f2b86b13c53302dc.webp) +![唱盤](../../../translated_images/zh-HK/turntable.f2b86b13c53302dc.webp) > 照片由 Marcela Laskoski 提供，來自 Unsplash diff --git a/translations/hk/6-NLP/README.md b/translations/hk/6-NLP/README.md index 6dec86e6f..246eff23c 100644 --- a/translations/hk/6-NLP/README.md +++ b/translations/hk/6-NLP/README.md @@ -17,7 +17,7 @@ CO_OP_TRANSLATOR_METADATA: 在這些課程中，我們將通過構建小型對話機器人來學習自然語言處理的基礎知識，了解機器學習如何幫助使這些對話變得越來越「智能」。你將穿越時光，與珍·奧斯汀1813年出版的經典小說《傲慢與偏見》中的伊麗莎白·班內特和達西先生進行交流。接著，你將進一步學習如何通過分析歐洲酒店評論來進行情感分析。 -![傲慢與偏見書籍與茶](../../../translated_images/hk/p&p.279f1c49ecd88941.webp) +![傲慢與偏見書籍與茶](../../../translated_images/zh-HK/p&p.279f1c49ecd88941.webp) > 照片由 Elaine Howlin 提供，來自 Unsplash ## 課程 diff --git a/translations/hk/7-TimeSeries/README.md b/translations/hk/7-TimeSeries/README.md index 509366e67..f48a0011b 100644 --- a/translations/hk/7-TimeSeries/README.md +++ b/translations/hk/7-TimeSeries/README.md @@ -17,7 +17,7 @@ CO_OP_TRANSLATOR_METADATA: 我們的地區重點是全球的電力使用，這是一個有趣的數據集，可以用來學習如何根據過去的負載模式來預測未來的電力需求。你可以看到這種預測在商業環境中是多麼有幫助。 -![電網](../../../translated_images/hk/electric-grid.0c21d5214db09ffa.webp) +![電網](../../../translated_images/zh-HK/electric-grid.0c21d5214db09ffa.webp) 照片由 [Peddi Sai hrithik](https://unsplash.com/@shutter_log?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText) 在拉賈斯坦邦的道路上拍攝的電塔，來自 [Unsplash](https://unsplash.com/s/photos/electric-india?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText) diff --git a/translations/hk/8-Reinforcement/README.md b/translations/hk/8-Reinforcement/README.md index 98ae29ef7..e187cfafe 100644 --- a/translations/hk/8-Reinforcement/README.md +++ b/translations/hk/8-Reinforcement/README.md @@ -13,7 +13,7 @@ CO_OP_TRANSLATOR_METADATA: 想像一下你有一個模擬環境，例如股票市場。如果你施加某項規定，會發生什麼事？它會帶來正面還是負面的影響？如果發生負面影響，你需要接受這種_負面強化_，從中學習並改變方向。如果結果是正面的，你需要基於這種_正面強化_進一步發展。 -![彼得與狼](../../../translated_images/hk/peter.779730f9ba3a8a8d.webp) +![彼得與狼](../../../translated_images/zh-HK/peter.779730f9ba3a8a8d.webp) > 彼得和他的朋友需要逃離飢餓的狼！圖片來源：[Jen Looper](https://twitter.com/jenlooper) diff --git a/translations/hk/9-Real-World/README.md b/translations/hk/9-Real-World/README.md index cbfcad4ab..af96b5058 100644 --- a/translations/hk/9-Real-World/README.md +++ b/translations/hk/9-Real-World/README.md @@ -11,7 +11,7 @@ CO_OP_TRANSLATOR_METADATA: 在本課程的這部分，你將了解一些經典機器學習在現實世界中的應用。我們在網絡上搜集了白皮書和文章，介紹使用這些策略的應用，儘量避免涉及神經網絡、深度學習和人工智能。了解機器學習如何應用於商業系統、生態應用、金融、藝術與文化等領域。 -![chess](../../../translated_images/hk/chess.e704a268781bdad8.webp) +![chess](../../../translated_images/zh-HK/chess.e704a268781bdad8.webp) > 照片由 Alexis Fauvet 提供，來源於 Unsplash diff --git a/translations/hk/README.md b/translations/hk/README.md index 03f389f9f..629f829be 100644 --- a/translations/hk/README.md +++ b/translations/hk/README.md @@ -41,7 +41,7 @@ CO_OP_TRANSLATOR_METADATA: 我們正在舉辦 Discord 的 AI 學習系列，了解詳情並於 2025 年 9 月 18 日至 30 日加入我們，詳情請見 [Learn with AI Series](https://aka.ms/learnwithai/discord)。你將獲得使用 GitHub Copilot 於資料科學的技巧與秘訣。 -![Learn with AI series](../../../../translated_images/hk/3.9b58fd8d6c373c20.webp) +![Learn with AI series](../../../../translated_images/zh-HK/3.9b58fd8d6c373c20.webp) # 機器學習入門 - 課程大綱 @@ -89,7 +89,7 @@ CO_OP_TRANSLATOR_METADATA: 部分課程有短片教學，可在課程內嵌連結找到，或在 [Microsoft Developer YouTube 頻道的 ML for Beginners 播放清單](https://aka.ms/ml-beginners-videos) 觀看，點擊下方圖片即可。 -[![ML for beginners banner](../../../../translated_images/hk/ml-for-beginners-video-banner.63f694a100034bc6.webp)](https://aka.ms/ml-beginners-videos) +[![ML for beginners banner](../../../../translated_images/zh-HK/ml-for-beginners-video-banner.63f694a100034bc6.webp)](https://aka.ms/ml-beginners-videos) --- diff --git a/translations/mo/1-Introduction/README.md b/translations/mo/1-Introduction/README.md index 99c00c8d4..39023d90b 100644 --- a/translations/mo/1-Introduction/README.md +++ b/translations/mo/1-Introduction/README.md @@ -11,7 +11,7 @@ CO_OP_TRANSLATOR_METADATA: 在本課程中，您將了解機器學習領域的基本概念、它的定義，以及它的歷史和研究人員使用的技術。讓我們一起探索這個機器學習的新世界吧！ -![globe](../../../translated_images/mo/globe.59f26379ceb40428.webp) +![globe](../../../translated_images/zh-MO/globe.59f26379ceb40428.webp) > 照片由 Bill Oxford 提供，來自 Unsplash ### 課程 diff --git a/translations/mo/2-Regression/1-Tools/solution/R/lesson_1-R.ipynb b/translations/mo/2-Regression/1-Tools/solution/R/lesson_1-R.ipynb index ead53caa4..e25595415 100644 --- a/translations/mo/2-Regression/1-Tools/solution/R/lesson_1-R.ipynb +++ b/translations/mo/2-Regression/1-Tools/solution/R/lesson_1-R.ipynb @@ -48,7 +48,7 @@ " width=\"630\"/>\n", "
插畫作者：@allison_horst
\n", "\n", - "\n" + "\n" ], "metadata": { "id": "LWNNzfqd6feZ" diff --git a/translations/mo/2-Regression/2-Data/solution/R/lesson_2-R.ipynb b/translations/mo/2-Regression/2-Data/solution/R/lesson_2-R.ipynb index 4f3f37016..fb504dd24 100644 --- a/translations/mo/2-Regression/2-Data/solution/R/lesson_2-R.ipynb +++ b/translations/mo/2-Regression/2-Data/solution/R/lesson_2-R.ipynb @@ -49,7 +49,7 @@ "
插圖由 @allison_horst 提供
\n", "\n", "\n", - "\n" + "\n" ], "metadata": { "id": "Pg5aexcOPqAZ" @@ -230,7 +230,7 @@ "
插圖由 @allison_horst 提供
\n", "\n", "\n", - "\n" + "\n" ], "metadata": { "id": "o4jLY5-VZO2C" @@ -535,7 +535,7 @@ "
資訊圖表由 Dasani Madipalli 提供
\n", "\n", "\n", - "\n", + "\n", "\n", "有一句*智慧*的名言是這樣說的：\n", "\n", diff --git a/translations/mo/2-Regression/3-Linear/solution/R/lesson_3-R.ipynb b/translations/mo/2-Regression/3-Linear/solution/R/lesson_3-R.ipynb index e25e8c632..a68d85053 100644 --- a/translations/mo/2-Regression/3-Linear/solution/R/lesson_3-R.ipynb +++ b/translations/mo/2-Regression/3-Linear/solution/R/lesson_3-R.ipynb @@ -40,7 +40,7 @@ "
資訊圖表由 Dasani Madipalli 製作
\n", "\n", "\n", - "\n", + "\n", "\n", "#### 簡介\n", "\n", @@ -132,7 +132,7 @@ ">\n", "> 換句話說，參考我們南瓜數據的原始問題：「根據月份預測每蒲式耳南瓜的價格」，`X` 代表價格，`Y` 代表銷售月份。\n", ">\n", - "> ![](../../../../../../translated_images/mo/calculation.989aa7822020d9d0ba9fc781f1ab5192f3421be86ebb88026528aef33c37b0d8.png)\n", + "> ![](../../../../../../translated_images/zh-MO/calculation.989aa7822020d9d0ba9fc781f1ab5192f3421be86ebb88026528aef33c37b0d8.png)\n", " 圖解由 Jen Looper 提供\n", "> \n", "> 計算 Y 的值。如果你支付大約 \\$4，那一定是四月！\n", @@ -804,7 +804,7 @@ "
圖表由 Dasani Madipalli 製作
\n", "\n", "\n", - "\n" + "\n" ], "metadata": { "id": "HOCqJXLTwtWI" diff --git a/translations/mo/2-Regression/4-Logistic/solution/R/lesson_4-R.ipynb b/translations/mo/2-Regression/4-Logistic/solution/R/lesson_4-R.ipynb index 466e905e6..83db4ea74 100644 --- a/translations/mo/2-Regression/4-Logistic/solution/R/lesson_4-R.ipynb +++ b/translations/mo/2-Regression/4-Logistic/solution/R/lesson_4-R.ipynb @@ -6,7 +6,7 @@ "source": [ "## 建立邏輯迴歸模型 - 第四課\n", "\n", - "![邏輯迴歸與線性迴歸資訊圖表](../../../../../../translated_images/mo/linear-vs-logistic.ba180bf95e7ee667.webp)\n", + "![邏輯迴歸與線性迴歸資訊圖表](../../../../../../translated_images/zh-MO/linear-vs-logistic.ba180bf95e7ee667.webp)\n", "\n", "#### **[課前測驗](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/15/)**\n", "\n", @@ -78,7 +78,7 @@ "\n", "邏輯回歸不提供與線性回歸相同的功能。前者提供對「二元類別」（例如「橙色或非橙色」）的預測，而後者則能夠預測「連續值」，例如根據南瓜的來源和收穫時間，*價格會上漲多少*。\n", "\n", - "![Dasani Madipalli 的資訊圖表](../../../../../../translated_images/mo/pumpkin-classifier.562771f104ad5436.webp)\n", + "![Dasani Madipalli 的資訊圖表](../../../../../../translated_images/zh-MO/pumpkin-classifier.562771f104ad5436.webp)\n", "\n", "### 其他分類方式\n", "\n", @@ -88,7 +88,7 @@ "\n", "- **序列型**，涉及有序的類別，適合我們希望按邏輯順序排列結果的情境，例如南瓜按有限的尺寸（迷你、小、中、大、特大、超大）排序。\n", "\n", - "![多項式 vs 序列型回歸](../../../../../../translated_images/mo/multinomial-vs-ordinal.36701b4850e37d86.webp)\n", + "![多項式 vs 序列型回歸](../../../../../../translated_images/zh-MO/multinomial-vs-ordinal.36701b4850e37d86.webp)\n", "\n", "#### **變數不需要相關**\n", "\n", diff --git a/translations/mo/2-Regression/README.md b/translations/mo/2-Regression/README.md index 692c60558..7b63ce443 100644 --- a/translations/mo/2-Regression/README.md +++ b/translations/mo/2-Regression/README.md @@ -12,7 +12,7 @@ CO_OP_TRANSLATOR_METADATA: 在北美，南瓜常被雕刻成恐怖的臉孔以慶祝萬聖節。讓我們一起探索這些迷人的蔬菜吧！ -![jack-o-lanterns](../../../translated_images/mo/jack-o-lanterns.181c661a9212457d.webp) +![jack-o-lanterns](../../../translated_images/zh-MO/jack-o-lanterns.181c661a9212457d.webp) > 照片由 Beth Teutschmann 提供，來自 Unsplash ## 你將學到什麼 diff --git a/translations/mo/3-Web-App/README.md b/translations/mo/3-Web-App/README.md index f35203ec9..eb6d9ae56 100644 --- a/translations/mo/3-Web-App/README.md +++ b/translations/mo/3-Web-App/README.md @@ -11,7 +11,7 @@ CO_OP_TRANSLATOR_METADATA: 在本課程的這一部分，您將學習一個應用機器學習的主題：如何將您的 Scikit-learn 模型保存為一個文件，並在網頁應用程式中使用它進行預測。一旦模型保存完成，您將學習如何在使用 Flask 建立的網頁應用程式中使用它。首先，您將使用一些關於 UFO 目擊事件的數據來建立模型！接著，您將建立一個網頁應用程式，允許您輸入秒數、緯度和經度值，來預測哪個國家報告了看到 UFO。 -![UFO 停車場](../../../translated_images/mo/ufo.9e787f5161da9d4d.webp) +![UFO 停車場](../../../translated_images/zh-MO/ufo.9e787f5161da9d4d.webp) 照片由 Michael Herren 提供，來自 Unsplash diff --git a/translations/mo/4-Classification/README.md b/translations/mo/4-Classification/README.md index c517a1ec4..5be8d01c4 100644 --- a/translations/mo/4-Classification/README.md +++ b/translations/mo/4-Classification/README.md @@ -13,7 +13,7 @@ CO_OP_TRANSLATOR_METADATA: 在亞洲和印度，飲食文化非常多樣化，而且非常美味！讓我們來看看有關區域料理的數據，試著了解它們的食材。 -![泰國食物攤販](../../../translated_images/mo/thai-food.c47a7a7f9f05c218.webp) +![泰國食物攤販](../../../translated_images/zh-MO/thai-food.c47a7a7f9f05c218.webp) > 照片由 Lisheng Chang 提供，來自 Unsplash ## 你將學到什麼 diff --git a/translations/mo/5-Clustering/README.md b/translations/mo/5-Clustering/README.md index b583ab2d2..07e3adfc6 100644 --- a/translations/mo/5-Clustering/README.md +++ b/translations/mo/5-Clustering/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: 尼日利亞多元化的觀眾擁有多樣的音樂品味。利用從 Spotify 擷取的數據（靈感來自[這篇文章](https://towardsdatascience.com/country-wise-visual-analysis-of-music-taste-using-spotify-api-seaborn-in-python-77f5b749b421)），讓我們來看看尼日利亞流行的音樂。這個數據集包含了各種歌曲的「舞蹈性」分數、「聲學性」、音量、「語音性」、流行度和能量等數據。探索這些數據中的模式將會非常有趣！ -![一台唱盤](../../../translated_images/mo/turntable.f2b86b13c53302dc.webp) +![一台唱盤](../../../translated_images/zh-MO/turntable.f2b86b13c53302dc.webp) > 照片由 Marcela Laskoski 提供，來自 Unsplash diff --git a/translations/mo/6-NLP/README.md b/translations/mo/6-NLP/README.md index 08544046e..46bb2fec6 100644 --- a/translations/mo/6-NLP/README.md +++ b/translations/mo/6-NLP/README.md @@ -17,7 +17,7 @@ CO_OP_TRANSLATOR_METADATA: 在這些課程中，我們將通過建立小型對話機器人來學習自然語言處理的基礎，了解機器學習如何幫助使這些對話變得越來越「智能」。你將穿越時光，與珍·奧斯汀1813年出版的經典小說《傲慢與偏見》中的伊麗莎白·班內特和達西先生進行交流。接著，你將進一步學習如何通過歐洲酒店評論進行情感分析。 -![傲慢與偏見書籍與茶](../../../translated_images/mo/p&p.279f1c49ecd88941.webp) +![傲慢與偏見書籍與茶](../../../translated_images/zh-MO/p&p.279f1c49ecd88941.webp) > 照片由 Elaine Howlin 提供，來自 Unsplash ## 課程 diff --git a/translations/mo/7-TimeSeries/README.md b/translations/mo/7-TimeSeries/README.md index a0a6be7dc..4d5e41843 100644 --- a/translations/mo/7-TimeSeries/README.md +++ b/translations/mo/7-TimeSeries/README.md @@ -17,7 +17,7 @@ CO_OP_TRANSLATOR_METADATA: 我們的區域重點是全球的電力使用情況，這是一個有趣的數據集，可以用來學習如何根據過去的負載模式預測未來的電力使用情況。您可以看到，這種預測在商業環境中是多麼有幫助。 -![電網](../../../translated_images/mo/electric-grid.0c21d5214db09ffa.webp) +![電網](../../../translated_images/zh-MO/electric-grid.0c21d5214db09ffa.webp) 照片由 [Peddi Sai hrithik](https://unsplash.com/@shutter_log?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText) 拍攝，展示了拉賈斯坦邦道路上的電塔，來自 [Unsplash](https://unsplash.com/s/photos/electric-india?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText) diff --git a/translations/mo/8-Reinforcement/README.md b/translations/mo/8-Reinforcement/README.md index ab1fe652c..f00b392f2 100644 --- a/translations/mo/8-Reinforcement/README.md +++ b/translations/mo/8-Reinforcement/README.md @@ -13,7 +13,7 @@ CO_OP_TRANSLATOR_METADATA: 想像一下你有一個模擬環境，例如股市。如果你施加某項規定，會發生什麼？它會產生正面還是負面的影響？如果發生負面影響，你需要接受這種_負面強化_，從中學習並改變方向。如果結果是正面的，你需要基於這種_正面強化_繼續努力。 -![彼得與狼](../../../translated_images/mo/peter.779730f9ba3a8a8d.webp) +![彼得與狼](../../../translated_images/zh-MO/peter.779730f9ba3a8a8d.webp) > 彼得和他的朋友們需要逃離飢餓的狼！圖片來源：[Jen Looper](https://twitter.com/jenlooper) diff --git a/translations/mo/9-Real-World/README.md b/translations/mo/9-Real-World/README.md index 5a0b46943..20796c6cf 100644 --- a/translations/mo/9-Real-World/README.md +++ b/translations/mo/9-Real-World/README.md @@ -11,7 +11,7 @@ CO_OP_TRANSLATOR_METADATA: 在本課程的這一部分，你將了解經典機器學習在現實世界中的一些應用。我們在網路上搜尋了許多白皮書和文章，介紹了使用這些策略的應用，並儘量避免涉及神經網路、深度學習和人工智慧。學習機器學習如何應用於商業系統、生態應用、金融、藝術與文化等領域。 -![chess](../../../translated_images/mo/chess.e704a268781bdad8.webp) +![chess](../../../translated_images/zh-MO/chess.e704a268781bdad8.webp) > 圖片由 Alexis Fauvet 提供，來源於 Unsplash diff --git a/translations/mo/README.md b/translations/mo/README.md index 5b1efe48b..5ad65655c 100644 --- a/translations/mo/README.md +++ b/translations/mo/README.md @@ -41,7 +41,7 @@ CO_OP_TRANSLATOR_METADATA: 我們目前舉辦 Discord AI 學習系列，歡迎於 2025 年 9 月 18 日至 30 日加入並了解詳情：[Learn with AI Series](https://aka.ms/learnwithai/discord)。您將獲得使用 GitHub Copilot 助力資料科學的技巧與秘訣。 -![Learn with AI series](../../../../translated_images/mo/3.9b58fd8d6c373c20.webp) +![Learn with AI series](../../../../translated_images/zh-MO/3.9b58fd8d6c373c20.webp) # 初學者的機器學習課程 @@ -89,7 +89,7 @@ CO_OP_TRANSLATOR_METADATA: 部分課堂提供短影片，您可在課程中直接觀看，也可點擊下方圖片至[微軟開發者 YouTube 頻道 的 ML for Beginners 播放清單](https://aka.ms/ml-beginners-videos)收看。 -[![ML for beginners banner](../../../../translated_images/mo/ml-for-beginners-video-banner.63f694a100034bc6.webp)](https://aka.ms/ml-beginners-videos) +[![ML for beginners banner](../../../../translated_images/zh-MO/ml-for-beginners-video-banner.63f694a100034bc6.webp)](https://aka.ms/ml-beginners-videos) --- diff --git a/translations/pt/1-Introduction/README.md b/translations/pt/1-Introduction/README.md index d749b6097..4d3a422de 100644 --- a/translations/pt/1-Introduction/README.md +++ b/translations/pt/1-Introduction/README.md @@ -11,7 +11,7 @@ CO_OP_TRANSLATOR_METADATA: Nesta seção do currículo, será apresentada uma introdução aos conceitos básicos que fundamentam o campo do aprendizado de máquina, o que ele é, além de aprender sobre sua história e as técnicas que os pesquisadores utilizam para trabalhar com ele. Vamos explorar juntos este novo mundo do aprendizado de máquina! -![globo](../../../translated_images/pt/globe.59f26379ceb40428.webp) +![globo](../../../translated_images/pt-PT/globe.59f26379ceb40428.webp) > Foto por Bill Oxford no Unsplash ### Aulas diff --git a/translations/pt/2-Regression/1-Tools/solution/R/lesson_1-R.ipynb b/translations/pt/2-Regression/1-Tools/solution/R/lesson_1-R.ipynb index dc2a07a0a..a397363e4 100644 --- a/translations/pt/2-Regression/1-Tools/solution/R/lesson_1-R.ipynb +++ b/translations/pt/2-Regression/1-Tools/solution/R/lesson_1-R.ipynb @@ -48,7 +48,7 @@ " width=\"630\"/>\n", "
Arte por @allison_horst
\n", "\n", - "\n" + "\n" ], "metadata": { "id": "LWNNzfqd6feZ" diff --git a/translations/pt/2-Regression/2-Data/solution/R/lesson_2-R.ipynb b/translations/pt/2-Regression/2-Data/solution/R/lesson_2-R.ipynb index 52cbdd064..51c7168e7 100644 --- a/translations/pt/2-Regression/2-Data/solution/R/lesson_2-R.ipynb +++ b/translations/pt/2-Regression/2-Data/solution/R/lesson_2-R.ipynb @@ -227,7 +227,7 @@ "
Ilustração por @allison_horst
\n", "\n", "\n", - "\n" + "\n" ], "metadata": { "id": "o4jLY5-VZO2C" @@ -529,7 +529,7 @@ "
Infografia por Dasani Madipalli
\n", "\n", "\n", - "\n", + "\n", "\n", "Há um ditado *sábio* que diz o seguinte:\n", "\n", diff --git a/translations/pt/2-Regression/3-Linear/solution/R/lesson_3-R.ipynb b/translations/pt/2-Regression/3-Linear/solution/R/lesson_3-R.ipynb index 28b731d6e..e4bd71ed7 100644 --- a/translations/pt/2-Regression/3-Linear/solution/R/lesson_3-R.ipynb +++ b/translations/pt/2-Regression/3-Linear/solution/R/lesson_3-R.ipynb @@ -130,7 +130,7 @@ ">\n", "> Em outras palavras, e referindo-nos à pergunta original sobre os dados das abóboras: \"prever o preço de uma abóbora por alqueire ao longo dos meses\", `X` referiria-se ao preço e `Y` ao mês de venda.\n", ">\n", - "> ![](../../../../../../translated_images/pt/calculation.989aa7822020d9d0ba9fc781f1ab5192f3421be86ebb88026528aef33c37b0d8.png)\n", + "> ![](../../../../../../translated_images/pt-PT/calculation.989aa7822020d9d0ba9fc781f1ab5192f3421be86ebb88026528aef33c37b0d8.png)\n", " Infográfico por Jen Looper\n", "> \n", "> Calcula o valor de Y. Se estás a pagar cerca de \\$4, deve ser abril!\n", @@ -162,7 +162,7 @@ "
Ilustração por @allison_horst
\n", "\n", "\n", - "\n" + "\n" ], "metadata": { "id": "WdUKXk7Bs8-V" @@ -456,7 +456,7 @@ "
Arte por @allison_horst
\n", "\n", "\n", - "\n" + "\n" ], "metadata": { "id": "KEiO0v7kuC9O" @@ -570,7 +570,7 @@ "
Infográfico por Dasani Madipalli
\n", "\n", "\n", - "\n" + "\n" ], "metadata": { "id": "YqXjLuWavNxW" @@ -811,7 +811,7 @@ "
Infográfico por Dasani Madipalli
\n", "\n", "\n", - "\n" + "\n" ], "metadata": { "id": "HOCqJXLTwtWI" diff --git a/translations/pt/2-Regression/4-Logistic/solution/R/lesson_4-R.ipynb b/translations/pt/2-Regression/4-Logistic/solution/R/lesson_4-R.ipynb index d092b78dd..9d6e3139d 100644 --- a/translations/pt/2-Regression/4-Logistic/solution/R/lesson_4-R.ipynb +++ b/translations/pt/2-Regression/4-Logistic/solution/R/lesson_4-R.ipynb @@ -6,7 +6,7 @@ "source": [ "## Construir um modelo de regressão logística - Aula 4\n", "\n", - "![Infográfico de regressão logística vs. regressão linear](../../../../../../translated_images/pt/linear-vs-logistic.ba180bf95e7ee667.webp)\n", + "![Infográfico de regressão logística vs. regressão linear](../../../../../../translated_images/pt-PT/linear-vs-logistic.ba180bf95e7ee667.webp)\n", "\n", "#### **[Questionário pré-aula](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/15/)**\n", "\n", @@ -78,7 +78,7 @@ "\n", "A regressão logística não oferece as mesmas funcionalidades que a regressão linear. A primeira fornece uma previsão sobre uma `categoria binária` (\"laranja ou não laranja\"), enquanto a segunda é capaz de prever `valores contínuos`, por exemplo, dado a origem de uma abóbora e o momento da colheita, *quanto o seu preço irá aumentar*.\n", "\n", - "![Infográfico por Dasani Madipalli](../../../../../../translated_images/pt/pumpkin-classifier.562771f104ad5436.webp)\n", + "![Infográfico por Dasani Madipalli](../../../../../../translated_images/pt-PT/pumpkin-classifier.562771f104ad5436.webp)\n", "\n", "### Outras classificações\n", "\n", @@ -88,7 +88,7 @@ "\n", "- **Ordinal**, que envolve categorias ordenadas, útil se quisermos organizar os resultados logicamente, como as nossas abóboras que são ordenadas por um número finito de tamanhos (mini,pequeno,médio,grande,xl,xxl).\n", "\n", - "![Regressão multinomial vs ordinal](../../../../../../translated_images/pt/multinomial-vs-ordinal.36701b4850e37d86.webp)\n", + "![Regressão multinomial vs ordinal](../../../../../../translated_images/pt-PT/multinomial-vs-ordinal.36701b4850e37d86.webp)\n", "\n", "#### **As variáveis NÃO precisam estar correlacionadas**\n", "\n", diff --git a/translations/pt/2-Regression/README.md b/translations/pt/2-Regression/README.md index 001adf7cf..54c03f332 100644 --- a/translations/pt/2-Regression/README.md +++ b/translations/pt/2-Regression/README.md @@ -12,7 +12,7 @@ CO_OP_TRANSLATOR_METADATA: Na América do Norte, as abóboras são frequentemente esculpidas em rostos assustadores para o Halloween. Vamos descobrir mais sobre estes vegetais fascinantes! -![jack-o-lanterns](../../../translated_images/pt/jack-o-lanterns.181c661a9212457d.webp) +![jack-o-lanterns](../../../translated_images/pt-PT/jack-o-lanterns.181c661a9212457d.webp) > Foto por Beth Teutschmann no Unsplash ## O que irá aprender diff --git a/translations/pt/3-Web-App/README.md b/translations/pt/3-Web-App/README.md index 37befff5a..9447a11ae 100644 --- a/translations/pt/3-Web-App/README.md +++ b/translations/pt/3-Web-App/README.md @@ -11,7 +11,7 @@ CO_OP_TRANSLATOR_METADATA: Nesta secção do currículo, será introduzido a um tópico aplicado de ML: como guardar o seu modelo Scikit-learn como um ficheiro que pode ser utilizado para fazer previsões dentro de uma aplicação web. Depois de guardar o modelo, aprenderá como utilizá-lo numa aplicação web construída em Flask. Primeiro, irá criar um modelo utilizando alguns dados relacionados com avistamentos de OVNIs! Em seguida, irá construir uma aplicação web que permitirá introduzir um número de segundos juntamente com valores de latitude e longitude para prever qual país relatou ter visto um OVNI. -![Estacionamento de OVNIs](../../../translated_images/pt/ufo.9e787f5161da9d4d.webp) +![Estacionamento de OVNIs](../../../translated_images/pt-PT/ufo.9e787f5161da9d4d.webp) Foto por Michael Herren em Unsplash diff --git a/translations/pt/4-Classification/1-Introduction/solution/R/lesson_10-R.ipynb b/translations/pt/4-Classification/1-Introduction/solution/R/lesson_10-R.ipynb index ee8dcbd09..05d570ffe 100644 --- a/translations/pt/4-Classification/1-Introduction/solution/R/lesson_10-R.ipynb +++ b/translations/pt/4-Classification/1-Introduction/solution/R/lesson_10-R.ipynb @@ -44,7 +44,7 @@ "
Celebre as culinárias pan-asiáticas nestas lições! Imagem por Jen Looper
\n", "\n", "\n", - "\n", + "\n", "\n", "Classificação é uma forma de [aprendizagem supervisionada](https://wikipedia.org/wiki/Supervised_learning) que tem muito em comum com técnicas de regressão. Na classificação, treina-se um modelo para prever a que `categoria` um item pertence. Se o machine learning trata de prever valores ou nomes para coisas usando conjuntos de dados, então a classificação geralmente se divide em dois grupos: *classificação binária* e *classificação multicategorias*.\n", "\n", diff --git a/translations/pt/4-Classification/README.md b/translations/pt/4-Classification/README.md index 3cae9920c..41dcf68ef 100644 --- a/translations/pt/4-Classification/README.md +++ b/translations/pt/4-Classification/README.md @@ -13,7 +13,7 @@ CO_OP_TRANSLATOR_METADATA: Na Ásia e na Índia, as tradições culinárias são extremamente diversas e muito deliciosas! Vamos analisar dados sobre culinárias regionais para tentar entender os seus ingredientes. -![Vendedor de comida tailandesa](../../../translated_images/pt/thai-food.c47a7a7f9f05c218.webp) +![Vendedor de comida tailandesa](../../../translated_images/pt-PT/thai-food.c47a7a7f9f05c218.webp) > Foto por Lisheng Chang no Unsplash ## O que irá aprender diff --git a/translations/pt/5-Clustering/README.md b/translations/pt/5-Clustering/README.md index 351faa8fa..ff8c57393 100644 --- a/translations/pt/5-Clustering/README.md +++ b/translations/pt/5-Clustering/README.md @@ -15,7 +15,7 @@ Clustering é uma tarefa de aprendizagem automática que procura encontrar objet O público diversificado da Nigéria tem gostos musicais igualmente variados. Usando dados extraídos do Spotify (inspirado por [este artigo](https://towardsdatascience.com/country-wise-visual-analysis-of-music-taste-using-spotify-api-seaborn-in-python-77f5b749b421)), vamos analisar algumas músicas populares na Nigéria. Este conjunto de dados inclui informações sobre o 'danceability', 'acousticness', volume, 'speechiness', popularidade e energia de várias músicas. Será interessante descobrir padrões nesses dados! -![Um gira-discos](../../../translated_images/pt/turntable.f2b86b13c53302dc.webp) +![Um gira-discos](../../../translated_images/pt-PT/turntable.f2b86b13c53302dc.webp) > Foto de Marcela Laskoski no Unsplash diff --git a/translations/pt/6-NLP/README.md b/translations/pt/6-NLP/README.md index 3f116b32a..b2b2e9961 100644 --- a/translations/pt/6-NLP/README.md +++ b/translations/pt/6-NLP/README.md @@ -17,7 +17,7 @@ Nesta seção do currículo, será introduzido um dos usos mais difundidos do ap Nestes módulos, aprenderemos os fundamentos do PLN construindo pequenos bots conversacionais para entender como o aprendizado de máquina ajuda a tornar essas conversas cada vez mais 'inteligentes'. Você viajará no tempo, conversando com Elizabeth Bennett e Mr. Darcy do clássico romance de Jane Austen, **Orgulho e Preconceito**, publicado em 1813. Depois, aprofundará seus conhecimentos aprendendo sobre análise de sentimentos através de avaliações de hotéis na Europa. -![Livro Orgulho e Preconceito e chá](../../../translated_images/pt/p&p.279f1c49ecd88941.webp) +![Livro Orgulho e Preconceito e chá](../../../translated_images/pt-PT/p&p.279f1c49ecd88941.webp) > Foto por Elaine Howlin no Unsplash ## Aulas diff --git a/translations/pt/7-TimeSeries/README.md b/translations/pt/7-TimeSeries/README.md index bc22c92a0..0d1720df9 100644 --- a/translations/pt/7-TimeSeries/README.md +++ b/translations/pt/7-TimeSeries/README.md @@ -17,7 +17,7 @@ Nestes dois módulos, será introduzido o conceito de previsão de séries tempo O nosso foco regional é o consumo de eletricidade no mundo, um conjunto de dados interessante para aprender a prever o consumo futuro de energia com base nos padrões de carga do passado. É possível perceber como este tipo de previsão pode ser extremamente útil em ambientes empresariais. -![rede elétrica](../../../translated_images/pt/electric-grid.0c21d5214db09ffa.webp) +![rede elétrica](../../../translated_images/pt-PT/electric-grid.0c21d5214db09ffa.webp) Foto de [Peddi Sai hrithik](https://unsplash.com/@shutter_log?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText) de torres elétricas numa estrada em Rajasthan no [Unsplash](https://unsplash.com/s/photos/electric-india?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText) diff --git a/translations/pt/8-Reinforcement/README.md b/translations/pt/8-Reinforcement/README.md index c98e74646..05ade32a0 100644 --- a/translations/pt/8-Reinforcement/README.md +++ b/translations/pt/8-Reinforcement/README.md @@ -13,7 +13,7 @@ O aprendizado por reforço, RL, é considerado um dos paradigmas básicos de apr Imagine que você tem um ambiente simulado, como o mercado de ações. O que acontece se você impuser uma determinada regulamentação? Isso terá um efeito positivo ou negativo? Se algo negativo acontecer, você precisa aceitar esse _reforço negativo_, aprender com ele e mudar de rumo. Se o resultado for positivo, você deve construir sobre esse _reforço positivo_. -![peter e o lobo](../../../translated_images/pt/peter.779730f9ba3a8a8d.webp) +![peter e o lobo](../../../translated_images/pt-PT/peter.779730f9ba3a8a8d.webp) > Peter e seus amigos precisam escapar do lobo faminto! Imagem por [Jen Looper](https://twitter.com/jenlooper) diff --git a/translations/pt/9-Real-World/README.md b/translations/pt/9-Real-World/README.md index 95c6abc58..053bf157e 100644 --- a/translations/pt/9-Real-World/README.md +++ b/translations/pt/9-Real-World/README.md @@ -11,7 +11,7 @@ CO_OP_TRANSLATOR_METADATA: Nesta secção do currículo, será apresentado a algumas aplicações reais de aprendizagem automática clássica. Pesquisámos na internet para encontrar artigos e documentos técnicos sobre aplicações que utilizaram estas estratégias, evitando redes neuronais, aprendizagem profunda e IA tanto quanto possível. Descubra como a aprendizagem automática é utilizada em sistemas empresariais, aplicações ecológicas, finanças, artes e cultura, entre outros. -![chess](../../../translated_images/pt/chess.e704a268781bdad8.webp) +![chess](../../../translated_images/pt-PT/chess.e704a268781bdad8.webp) > Foto por Alexis Fauvet em Unsplash diff --git a/translations/pt/README.md b/translations/pt/README.md index 4fb8891c9..acd498084 100644 --- a/translations/pt/README.md +++ b/translations/pt/README.md @@ -41,7 +41,7 @@ CO_OP_TRANSLATOR_METADATA: Temos uma série de aprendizagem com IA no Discord, saiba mais e junte-se a nós em [Learn with AI Series](https://aka.ms/learnwithai/discord) de 18 a 30 de setembro de 2025. Irá receber dicas e truques sobre como usar o GitHub Copilot para Ciência de Dados. -![Série Learn with AI](../../../../translated_images/pt/3.9b58fd8d6c373c20.webp) +![Série Learn with AI](../../../../translated_images/pt-PT/3.9b58fd8d6c373c20.webp) # Aprendizagem Automática para Iniciantes - Um Currículo @@ -90,7 +90,7 @@ Siga estes passos: Algumas das lições estão disponíveis em formato de vídeo curto. Pode encontrá-los integrados nas lições, ou na [playlist ML for Beginners do canal Microsoft Developer no YouTube](https://aka.ms/ml-beginners-videos) clicando na imagem abaixo. -[![Banner ML for beginners](../../../../translated_images/pt/ml-for-beginners-video-banner.63f694a100034bc6.webp)](https://aka.ms/ml-beginners-videos) +[![Banner ML for beginners](../../../../translated_images/pt-PT/ml-for-beginners-video-banner.63f694a100034bc6.webp)](https://aka.ms/ml-beginners-videos) --- diff --git a/translations/tw/1-Introduction/README.md b/translations/tw/1-Introduction/README.md index bc7ae3af0..645cf757a 100644 --- a/translations/tw/1-Introduction/README.md +++ b/translations/tw/1-Introduction/README.md @@ -11,7 +11,7 @@ CO_OP_TRANSLATOR_METADATA: 在本課程的這一部分中，您將了解機器學習領域的基本概念、它的定義，並學習它的歷史以及研究人員使用的相關技術。讓我們一起探索這個嶄新的機器學習世界吧！ -![globe](../../../translated_images/tw/globe.59f26379ceb40428.webp) +![globe](../../../translated_images/zh-TW/globe.59f26379ceb40428.webp) > 照片由 Bill Oxford 提供，來自 Unsplash ### 課程 diff --git a/translations/tw/2-Regression/1-Tools/solution/R/lesson_1-R.ipynb b/translations/tw/2-Regression/1-Tools/solution/R/lesson_1-R.ipynb index dd33fa62d..2fecd44a3 100644 --- a/translations/tw/2-Regression/1-Tools/solution/R/lesson_1-R.ipynb +++ b/translations/tw/2-Regression/1-Tools/solution/R/lesson_1-R.ipynb @@ -48,7 +48,7 @@ " width=\"630\"/>\n", "
插畫作者：@allison_horst
\n", "\n", - "\n" + "\n" ], "metadata": { "id": "LWNNzfqd6feZ" diff --git a/translations/tw/2-Regression/2-Data/solution/R/lesson_2-R.ipynb b/translations/tw/2-Regression/2-Data/solution/R/lesson_2-R.ipynb index 88b5bf4d0..4968537ff 100644 --- a/translations/tw/2-Regression/2-Data/solution/R/lesson_2-R.ipynb +++ b/translations/tw/2-Regression/2-Data/solution/R/lesson_2-R.ipynb @@ -49,7 +49,7 @@ "
插圖由 @allison_horst 提供
\n", "\n", "\n", - "\n" + "\n" ], "metadata": { "id": "Pg5aexcOPqAZ" @@ -230,7 +230,7 @@ "
插圖由 @allison_horst 提供
\n", "\n", "\n", - "\n" + "\n" ], "metadata": { "id": "o4jLY5-VZO2C" @@ -533,7 +533,7 @@ "
資訊圖表由 Dasani Madipalli 製作
\n", "\n", "\n", - "\n", + "\n", "\n", "有一句*智慧*的名言是這樣說的：\n", "\n", diff --git a/translations/tw/2-Regression/3-Linear/solution/R/lesson_3-R.ipynb b/translations/tw/2-Regression/3-Linear/solution/R/lesson_3-R.ipynb index e85c027f6..6e11bb633 100644 --- a/translations/tw/2-Regression/3-Linear/solution/R/lesson_3-R.ipynb +++ b/translations/tw/2-Regression/3-Linear/solution/R/lesson_3-R.ipynb @@ -40,7 +40,7 @@ "
Dasani Madipalli 的資訊圖表
\n", "\n", "\n", - "\n", + "\n", "\n", "#### 簡介\n", "\n", @@ -132,7 +132,7 @@ ">\n", "> 換句話說，參考我們南瓜數據的原始問題：「根據月份預測每蒲式耳南瓜的價格」，`X` 代表價格，`Y` 代表銷售月份。\n", ">\n", - "> ![](../../../../../../translated_images/tw/calculation.989aa7822020d9d0ba9fc781f1ab5192f3421be86ebb88026528aef33c37b0d8.png)\n", + "> ![](../../../../../../translated_images/zh-TW/calculation.989aa7822020d9d0ba9fc781f1ab5192f3421be86ebb88026528aef33c37b0d8.png)\n", " 圖解由 Jen Looper 提供\n", "> \n", "> 計算 Y 的值。如果你支付大約 \\$4，那一定是四月！\n", @@ -164,7 +164,7 @@ "
插圖作者：@allison_horst
\n", "\n", "\n", - "\n" + "\n" ], "metadata": { "id": "WdUKXk7Bs8-V" @@ -458,7 +458,7 @@ "
插圖由 @allison_horst 提供
\n", "\n", "\n", - "\n" + "\n" ], "metadata": { "id": "KEiO0v7kuC9O" @@ -572,7 +572,7 @@ "
由 Dasani Madipalli 製作的資訊圖表
\n", "\n", "\n", - "\n" + "\n" ], "metadata": { "id": "YqXjLuWavNxW" @@ -813,7 +813,7 @@ "
資訊圖表由 Dasani Madipalli 提供
\n", "\n", "\n", - "\n" + "\n" ], "metadata": { "id": "HOCqJXLTwtWI" diff --git a/translations/tw/2-Regression/4-Logistic/solution/R/lesson_4-R.ipynb b/translations/tw/2-Regression/4-Logistic/solution/R/lesson_4-R.ipynb index 3f9080907..50829db73 100644 --- a/translations/tw/2-Regression/4-Logistic/solution/R/lesson_4-R.ipynb +++ b/translations/tw/2-Regression/4-Logistic/solution/R/lesson_4-R.ipynb @@ -6,7 +6,7 @@ "source": [ "## 建立邏輯迴歸模型 - 第四課\n", "\n", - "![邏輯迴歸與線性迴歸資訊圖](../../../../../../translated_images/tw/linear-vs-logistic.ba180bf95e7ee667.webp)\n", + "![邏輯迴歸與線性迴歸資訊圖](../../../../../../translated_images/zh-TW/linear-vs-logistic.ba180bf95e7ee667.webp)\n", "\n", "#### **[課前測驗](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/15/)**\n", "\n", @@ -78,7 +78,7 @@ "\n", "邏輯回歸不提供與線性回歸相同的功能。前者提供對「二元類別」（例如「橙色或非橙色」）的預測，而後者則能預測「連續值」，例如根據南瓜的來源和收穫時間，*價格將上漲多少*。\n", "\n", - "![Dasani Madipalli 的資訊圖表](../../../../../../translated_images/tw/pumpkin-classifier.562771f104ad5436.webp)\n", + "![Dasani Madipalli 的資訊圖表](../../../../../../translated_images/zh-TW/pumpkin-classifier.562771f104ad5436.webp)\n", "\n", "### 其他分類方式\n", "\n", @@ -88,7 +88,7 @@ "\n", "- **序列式**，涉及有序的類別，適合我們希望按邏輯順序排列結果的情況，例如南瓜按有限的尺寸（迷你、小、中、大、特大、超大）排序。\n", "\n", - "![多項式 vs 序列式回歸](../../../../../../translated_images/tw/multinomial-vs-ordinal.36701b4850e37d86.webp)\n", + "![多項式 vs 序列式回歸](../../../../../../translated_images/zh-TW/multinomial-vs-ordinal.36701b4850e37d86.webp)\n", "\n", "#### **變數不必相關**\n", "\n", diff --git a/translations/tw/2-Regression/README.md b/translations/tw/2-Regression/README.md index a5292f635..7f1c81cb9 100644 --- a/translations/tw/2-Regression/README.md +++ b/translations/tw/2-Regression/README.md @@ -12,7 +12,7 @@ CO_OP_TRANSLATOR_METADATA: 在北美，南瓜常被雕刻成恐怖的臉孔以慶祝萬聖節。讓我們一起探索這些迷人的蔬菜吧！ -![jack-o-lanterns](../../../translated_images/tw/jack-o-lanterns.181c661a9212457d.webp) +![jack-o-lanterns](../../../translated_images/zh-TW/jack-o-lanterns.181c661a9212457d.webp) > 照片由 Beth Teutschmann 提供，來自 Unsplash ## 你將學到什麼 diff --git a/translations/tw/3-Web-App/README.md b/translations/tw/3-Web-App/README.md index 2b4190f07..3b301ed09 100644 --- a/translations/tw/3-Web-App/README.md +++ b/translations/tw/3-Web-App/README.md @@ -11,7 +11,7 @@ CO_OP_TRANSLATOR_METADATA: 在本課程的這一部分，您將學習一個應用機器學習的主題：如何將您的 Scikit-learn 模型保存為一個文件，並在網頁應用程式中使用它進行預測。一旦模型保存完成，您將學習如何在使用 Flask 建立的網頁應用程式中使用它。首先，您將使用一些關於 UFO 目擊事件的數據來建立模型！接著，您將建立一個網頁應用程式，允許您輸入秒數、緯度和經度值，來預測哪個國家報告了看到 UFO。 -![UFO 停車場](../../../translated_images/tw/ufo.9e787f5161da9d4d.webp) +![UFO 停車場](../../../translated_images/zh-TW/ufo.9e787f5161da9d4d.webp) 照片由 Michael Herren 提供，來自 Unsplash diff --git a/translations/tw/4-Classification/README.md b/translations/tw/4-Classification/README.md index 4f96f7e29..4e24070d0 100644 --- a/translations/tw/4-Classification/README.md +++ b/translations/tw/4-Classification/README.md @@ -13,7 +13,7 @@ CO_OP_TRANSLATOR_METADATA: 在亞洲和印度，飲食文化非常多樣化，而且非常美味！讓我們來看看有關地區料理的數據，試著了解它們的食材。 -![泰國街頭小吃](../../../translated_images/tw/thai-food.c47a7a7f9f05c218.webp) +![泰國街頭小吃](../../../translated_images/zh-TW/thai-food.c47a7a7f9f05c218.webp) > 照片由 Lisheng Chang 提供，來自 Unsplash ## 你將學到什麼 diff --git a/translations/tw/5-Clustering/README.md b/translations/tw/5-Clustering/README.md index 24447dba6..097551a16 100644 --- a/translations/tw/5-Clustering/README.md +++ b/translations/tw/5-Clustering/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: 尼日利亞的多元化觀眾擁有多樣化的音樂品味。使用從 Spotify 擷取的數據（靈感來自[這篇文章](https://towardsdatascience.com/country-wise-visual-analysis-of-music-taste-using-spotify-api-seaborn-in-python-77f5b749b421)），讓我們來看看一些在尼日利亞流行的音樂。這個數據集包含了各種歌曲的「舞蹈性」分數、「聲學性」、音量、「語音性」、流行度和能量等數據。探索這些數據中的模式將會非常有趣！ -![唱盤](../../../translated_images/tw/turntable.f2b86b13c53302dc.webp) +![唱盤](../../../translated_images/zh-TW/turntable.f2b86b13c53302dc.webp) > 照片由 Marcela Laskoski 提供，來自 Unsplash diff --git a/translations/tw/6-NLP/README.md b/translations/tw/6-NLP/README.md index d64707e6b..6570606cf 100644 --- a/translations/tw/6-NLP/README.md +++ b/translations/tw/6-NLP/README.md @@ -17,7 +17,7 @@ CO_OP_TRANSLATOR_METADATA: 在這些課程中，我們將通過構建小型對話機器人來學習 NLP 的基礎知識，了解機器學習如何幫助使這些對話變得越來越「智能」。您將穿越時光，與珍·奧斯汀 1813 年出版的經典小說《傲慢與偏見》中的伊麗莎白·班內特和達西先生進行對話。接著，您將進一步學習如何通過分析歐洲酒店評論來了解情感分析。 -![傲慢與偏見書籍與茶](../../../translated_images/tw/p&p.279f1c49ecd88941.webp) +![傲慢與偏見書籍與茶](../../../translated_images/zh-TW/p&p.279f1c49ecd88941.webp) > 圖片由 Elaine Howlin 提供，來自 Unsplash ## 課程 diff --git a/translations/tw/7-TimeSeries/README.md b/translations/tw/7-TimeSeries/README.md index b994c549d..d10ec2e64 100644 --- a/translations/tw/7-TimeSeries/README.md +++ b/translations/tw/7-TimeSeries/README.md @@ -17,7 +17,7 @@ CO_OP_TRANSLATOR_METADATA: 我們的地區重點是全球的電力使用，這是一個有趣的數據集，可以用來學習如何根據過去的負載模式預測未來的電力使用。您可以看到這種預測在商業環境中是多麼有幫助。 -![電力網](../../../translated_images/tw/electric-grid.0c21d5214db09ffa.webp) +![電力網](../../../translated_images/zh-TW/electric-grid.0c21d5214db09ffa.webp) 照片由 [Peddi Sai hrithik](https://unsplash.com/@shutter_log?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText) 拍攝，展示了拉賈斯坦邦道路上的電力塔，來自 [Unsplash](https://unsplash.com/s/photos/electric-india?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText) diff --git a/translations/tw/8-Reinforcement/README.md b/translations/tw/8-Reinforcement/README.md index 2604512cd..1f4452cfc 100644 --- a/translations/tw/8-Reinforcement/README.md +++ b/translations/tw/8-Reinforcement/README.md @@ -13,7 +13,7 @@ CO_OP_TRANSLATOR_METADATA: 想像你有一個模擬環境，例如股票市場。如果你施加某項規定，會發生什麼？它會產生正面還是負面的影響？如果發生負面影響，你需要接受這種_負面強化_，從中學習並改變方向。如果是正面結果，你需要基於這種_正面強化_進一步發展。 -![彼得與狼](../../../translated_images/tw/peter.779730f9ba3a8a8d.webp) +![彼得與狼](../../../translated_images/zh-TW/peter.779730f9ba3a8a8d.webp) > 彼得和他的朋友們需要逃離飢餓的狼！圖片由 [Jen Looper](https://twitter.com/jenlooper) 提供 diff --git a/translations/tw/9-Real-World/README.md b/translations/tw/9-Real-World/README.md index 7dd3987cc..f63fd2b74 100644 --- a/translations/tw/9-Real-World/README.md +++ b/translations/tw/9-Real-World/README.md @@ -11,7 +11,7 @@ CO_OP_TRANSLATOR_METADATA: 在本課程的這一部分，您將了解一些經典機器學習在現實世界中的應用。我們在網路上搜尋了許多白皮書和文章，介紹使用這些策略的應用，並儘量避免涉及神經網絡、深度學習和人工智慧。了解機器學習如何應用於商業系統、生態環境、金融、藝術與文化等領域。 -![chess](../../../translated_images/tw/chess.e704a268781bdad8.webp) +![chess](../../../translated_images/zh-TW/chess.e704a268781bdad8.webp) > 照片由 Alexis Fauvet 提供，來源於 Unsplash diff --git a/translations/tw/README.md b/translations/tw/README.md index 75ea46451..5b23805a0 100644 --- a/translations/tw/README.md +++ b/translations/tw/README.md @@ -41,7 +41,7 @@ CO_OP_TRANSLATOR_METADATA: 我們持續推出 Discord AI 學習系列，詳情並加入請見 [Learn with AI Series](https://aka.ms/learnwithai/discord)，時間為 2025 年 9 月 18 日至 30 日。你會學到使用 GitHub Copilot 進行資料科學的小撇步與技巧。 -![Learn with AI series](../../../../translated_images/tw/3.9b58fd8d6c373c20.webp) +![Learn with AI series](../../../../translated_images/zh-TW/3.9b58fd8d6c373c20.webp) # 機器學習入門 - 課程綱要 @@ -89,7 +89,7 @@ CO_OP_TRANSLATOR_METADATA: 部分課程有短影片可觀看。可在課程內容中內嵌觀看，或到[Microsoft Developer YouTube 頻道的 ML for Beginners 播放清單](https://aka.ms/ml-beginners-videos)點擊以下圖片觀看。 -[![ML for beginners banner](../../../../translated_images/tw/ml-for-beginners-video-banner.63f694a100034bc6.webp)](https://aka.ms/ml-beginners-videos) +[![ML for beginners banner](../../../../translated_images/zh-TW/ml-for-beginners-video-banner.63f694a100034bc6.webp)](https://aka.ms/ml-beginners-videos) --- diff --git a/translations/zh/1-Introduction/README.md b/translations/zh/1-Introduction/README.md index e5e85b447..af8d92f01 100644 --- a/translations/zh/1-Introduction/README.md +++ b/translations/zh/1-Introduction/README.md @@ -11,7 +11,7 @@ CO_OP_TRANSLATOR_METADATA: 在本课程部分中，您将了解机器学习领域的基本概念、它的定义，并学习它的历史以及研究人员使用的相关技术。让我们一起探索这个机器学习的新世界吧！ -![globe](../../../translated_images/zh/globe.59f26379ceb40428.webp) +![globe](../../../translated_images/zh-CN/globe.59f26379ceb40428.webp) > 图片由 Bill Oxford 提供，来自 Unsplash ### 课程 diff --git a/translations/zh/2-Regression/1-Tools/solution/R/lesson_1-R.ipynb b/translations/zh/2-Regression/1-Tools/solution/R/lesson_1-R.ipynb index 2a387cb42..f7e1ee54c 100644 --- a/translations/zh/2-Regression/1-Tools/solution/R/lesson_1-R.ipynb +++ b/translations/zh/2-Regression/1-Tools/solution/R/lesson_1-R.ipynb @@ -48,7 +48,7 @@ " width=\"630\"/>\n", "
由 @allison_horst 创作的艺术作品
\n", "\n", - "\n" + "\n" ], "metadata": { "id": "LWNNzfqd6feZ" diff --git a/translations/zh/2-Regression/2-Data/solution/R/lesson_2-R.ipynb b/translations/zh/2-Regression/2-Data/solution/R/lesson_2-R.ipynb index 6a64f3b3a..9cb696e33 100644 --- a/translations/zh/2-Regression/2-Data/solution/R/lesson_2-R.ipynb +++ b/translations/zh/2-Regression/2-Data/solution/R/lesson_2-R.ipynb @@ -49,7 +49,7 @@ "
艺术作品由 @allison_horst 提供
\n", "\n", "\n", - "\n" + "\n" ], "metadata": { "id": "Pg5aexcOPqAZ" @@ -230,7 +230,7 @@ "
插图作者：@allison_horst
\n", "\n", "\n", - "\n" + "\n" ], "metadata": { "id": "o4jLY5-VZO2C" @@ -533,7 +533,7 @@ "
信息图表作者：Dasani Madipalli
\n", "\n", "\n", - "\n", + "\n", "\n", "有一句*智慧*的名言是这样说的：\n", "\n", diff --git a/translations/zh/2-Regression/3-Linear/solution/R/lesson_3-R.ipynb b/translations/zh/2-Regression/3-Linear/solution/R/lesson_3-R.ipynb index c4f53f278..57f669a57 100644 --- a/translations/zh/2-Regression/3-Linear/solution/R/lesson_3-R.ipynb +++ b/translations/zh/2-Regression/3-Linear/solution/R/lesson_3-R.ipynb @@ -42,7 +42,7 @@ "
信息图作者：Dasani Madipalli
\n", "\n", "\n", - "\n", + "\n", "\n", "#### 介绍\n", "\n", @@ -134,7 +134,7 @@ ">\n", "> 换句话说，参考我们的南瓜数据的原始问题：“按月份预测每蒲式耳南瓜的价格”，`X` 表示价格，`Y` 表示销售月份。\n", ">\n", - "> ![](../../../../../../translated_images/zh/calculation.989aa7822020d9d0ba9fc781f1ab5192f3421be86ebb88026528aef33c37b0d8.png)\n", + "> ![](../../../../../../translated_images/zh-CN/calculation.989aa7822020d9d0ba9fc781f1ab5192f3421be86ebb88026528aef33c37b0d8.png)\n", " 信息图由 Jen Looper 制作\n", "> \n", "> 计算 Y 的值。如果你支付大约 \\$4，那一定是四月！\n", @@ -166,7 +166,7 @@ "
插画作者：@allison_horst
\n", "\n", "\n", - "\n" + "\n" ], "metadata": { "id": "WdUKXk7Bs8-V" @@ -571,7 +571,7 @@ "
Dasani Madipalli 制作的信息图
\n", "\n", "\n", - "\n" + "\n" ], "metadata": { "id": "YqXjLuWavNxW" @@ -812,7 +812,7 @@ "
信息图由 Dasani Madipalli 制作
\n", "\n", "\n", - "\n" + "\n" ], "metadata": { "id": "HOCqJXLTwtWI" diff --git a/translations/zh/2-Regression/4-Logistic/solution/R/lesson_4-R.ipynb b/translations/zh/2-Regression/4-Logistic/solution/R/lesson_4-R.ipynb index ea68389e0..6a61a9f92 100644 --- a/translations/zh/2-Regression/4-Logistic/solution/R/lesson_4-R.ipynb +++ b/translations/zh/2-Regression/4-Logistic/solution/R/lesson_4-R.ipynb @@ -6,7 +6,7 @@ "source": [ "## 构建逻辑回归模型 - 第4课\n", "\n", - "![逻辑回归与线性回归信息图](../../../../../../translated_images/zh/linear-vs-logistic.ba180bf95e7ee667.webp)\n", + "![逻辑回归与线性回归信息图](../../../../../../translated_images/zh-CN/linear-vs-logistic.ba180bf95e7ee667.webp)\n", "\n", "#### **[课前测验](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/15/)**\n", "\n", @@ -78,7 +78,7 @@ "\n", "逻辑回归不具备线性回归的相同功能。前者提供关于`二元类别`（例如“橙色或非橙色”）的预测，而后者能够预测`连续值`，例如根据南瓜的产地和收获时间，*预测其价格将上涨多少*。\n", "\n", - "![Dasani Madipalli制作的信息图](../../../../../../translated_images/zh/pumpkin-classifier.562771f104ad5436.webp)\n", + "![Dasani Madipalli制作的信息图](../../../../../../translated_images/zh-CN/pumpkin-classifier.562771f104ad5436.webp)\n", "\n", "### 其他分类方式\n", "\n", @@ -88,7 +88,7 @@ "\n", "- **有序分类**，涉及有序的类别，这在我们需要逻辑地排列结果时很有用，例如按南瓜的有限尺寸（迷你、小、中、大、特大、超大）进行排序。\n", "\n", - "![多项式分类 vs 有序分类](../../../../../../translated_images/zh/multinomial-vs-ordinal.36701b4850e37d86.webp)\n", + "![多项式分类 vs 有序分类](../../../../../../translated_images/zh-CN/multinomial-vs-ordinal.36701b4850e37d86.webp)\n", "\n", "#### **变量不需要相关**\n", "\n", diff --git a/translations/zh/2-Regression/README.md b/translations/zh/2-Regression/README.md index 651999fb1..4168cc47d 100644 --- a/translations/zh/2-Regression/README.md +++ b/translations/zh/2-Regression/README.md @@ -12,7 +12,7 @@ CO_OP_TRANSLATOR_METADATA: 在北美，南瓜常被雕刻成恐怖的面孔用于庆祝万圣节。让我们一起来探索这些迷人的蔬菜吧！ -![jack-o-lanterns](../../../translated_images/zh/jack-o-lanterns.181c661a9212457d.webp) +![jack-o-lanterns](../../../translated_images/zh-CN/jack-o-lanterns.181c661a9212457d.webp) > 图片由 Beth Teutschmann 提供，来自 Unsplash ## 你将学到什么 diff --git a/translations/zh/3-Web-App/README.md b/translations/zh/3-Web-App/README.md index a0fa5bd9a..39a36157c 100644 --- a/translations/zh/3-Web-App/README.md +++ b/translations/zh/3-Web-App/README.md @@ -11,7 +11,7 @@ CO_OP_TRANSLATOR_METADATA: 在本课程的这一部分，您将学习一个应用型的机器学习主题：如何将您的 Scikit-learn 模型保存为一个文件，以便在网页应用中进行预测。一旦模型保存完成，您将学习如何在使用 Flask 构建的网页应用中使用它。您将首先使用一些关于 UFO 目击事件的数据创建一个模型！然后，您将构建一个网页应用，允许用户输入持续时间（秒数）、纬度和经度值，以预测哪个国家报告了看到 UFO。 -![UFO 停车场](../../../translated_images/zh/ufo.9e787f5161da9d4d.webp) +![UFO 停车场](../../../translated_images/zh-CN/ufo.9e787f5161da9d4d.webp) 照片由 Michael Herren 提供，来自 Unsplash diff --git a/translations/zh/4-Classification/README.md b/translations/zh/4-Classification/README.md index 5031588fa..d89ae7828 100644 --- a/translations/zh/4-Classification/README.md +++ b/translations/zh/4-Classification/README.md @@ -13,7 +13,7 @@ CO_OP_TRANSLATOR_METADATA: 在亚洲和印度，饮食文化极其多样化，而且非常美味！让我们来看看有关地区美食的数据，试着了解它们的食材。 -![泰国小吃摊](../../../translated_images/zh/thai-food.c47a7a7f9f05c218.webp) +![泰国小吃摊](../../../translated_images/zh-CN/thai-food.c47a7a7f9f05c218.webp) > 图片由 Lisheng Chang 提供，发布在 Unsplash ## 你将学到什么 diff --git a/translations/zh/5-Clustering/README.md b/translations/zh/5-Clustering/README.md index 55273c696..71583dd3c 100644 --- a/translations/zh/5-Clustering/README.md +++ b/translations/zh/5-Clustering/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: 尼日利亚的观众拥有多样化的音乐品味。通过从 Spotify 抓取的数据（灵感来源于[这篇文章](https://towardsdatascience.com/country-wise-visual-analysis-of-music-taste-using-spotify-api-seaborn-in-python-77f5b749b421)），让我们来看看尼日利亚流行的一些音乐。这份数据集包括关于各种歌曲的“舞蹈性”评分、“声学性”、响度、“语音性”、流行度和能量的相关数据。发现这些数据中的模式将会非常有趣！ -![唱盘](../../../translated_images/zh/turntable.f2b86b13c53302dc.webp) +![唱盘](../../../translated_images/zh-CN/turntable.f2b86b13c53302dc.webp) > 图片由 Marcela Laskoski 提供，来自 Unsplash diff --git a/translations/zh/6-NLP/README.md b/translations/zh/6-NLP/README.md index 2cda2973e..e2930c97f 100644 --- a/translations/zh/6-NLP/README.md +++ b/translations/zh/6-NLP/README.md @@ -17,7 +17,7 @@ CO_OP_TRANSLATOR_METADATA: 在这些课程中，我们将通过构建小型对话机器人来学习NLP的基础知识，了解机器学习如何帮助使这些对话变得越来越“智能”。您将穿越时光，与简·奥斯汀1813年出版的经典小说《傲慢与偏见》中的伊丽莎白·班内特和达西先生进行对话。随后，您将通过学习欧洲酒店评论中的情感分析进一步加深知识。 -![傲慢与偏见书籍与茶](../../../translated_images/zh/p&p.279f1c49ecd88941.webp) +![傲慢与偏见书籍与茶](../../../translated_images/zh-CN/p&p.279f1c49ecd88941.webp) > 图片由 Elaine Howlin 提供，来自 Unsplash ## 课程 diff --git a/translations/zh/7-TimeSeries/README.md b/translations/zh/7-TimeSeries/README.md index c3624d779..c07f1ed6b 100644 --- a/translations/zh/7-TimeSeries/README.md +++ b/translations/zh/7-TimeSeries/README.md @@ -17,7 +17,7 @@ CO_OP_TRANSLATOR_METADATA: 我们的区域重点是全球电力使用，这是一个有趣的数据集，可以用来学习如何根据过去的负载模式预测未来的电力使用情况。你会发现这种预测在商业环境中非常有帮助。 -![电网](../../../translated_images/zh/electric-grid.0c21d5214db09ffa.webp) +![电网](../../../translated_images/zh-CN/electric-grid.0c21d5214db09ffa.webp) 照片由 [Peddi Sai hrithik](https://unsplash.com/@shutter_log?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText) 在拉贾斯坦邦的道路上拍摄的电力塔，发布于 [Unsplash](https://unsplash.com/s/photos/electric-india?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText) diff --git a/translations/zh/8-Reinforcement/README.md b/translations/zh/8-Reinforcement/README.md index 65166ead3..8dbe9a4f8 100644 --- a/translations/zh/8-Reinforcement/README.md +++ b/translations/zh/8-Reinforcement/README.md @@ -13,7 +13,7 @@ CO_OP_TRANSLATOR_METADATA: 想象一下，你有一个模拟环境，比如股票市场。如果你实施某项规定，会发生什么？它会产生积极还是消极的影响？如果发生了消极的事情，你需要接受这种_负强化_，从中学习并调整方向。如果是积极的结果，你需要基于这种_正强化_继续发展。 -![彼得与狼](../../../translated_images/zh/peter.779730f9ba3a8a8d.webp) +![彼得与狼](../../../translated_images/zh-CN/peter.779730f9ba3a8a8d.webp) > 彼得和他的朋友们需要逃离饥饿的狼！图片由 [Jen Looper](https://twitter.com/jenlooper) 提供 diff --git a/translations/zh/9-Real-World/README.md b/translations/zh/9-Real-World/README.md index b9045a7ae..c2ee2de10 100644 --- a/translations/zh/9-Real-World/README.md +++ b/translations/zh/9-Real-World/README.md @@ -11,7 +11,7 @@ CO_OP_TRANSLATOR_METADATA: 在本课程的这一部分中，您将了解经典机器学习在现实世界中的一些应用。我们在互联网上搜集了关于这些策略应用的白皮书和文章，尽量避免涉及神经网络、深度学习和人工智能。了解机器学习如何应用于商业系统、生态应用、金融、艺术与文化等领域。 -![国际象棋](../../../translated_images/zh/chess.e704a268781bdad8.webp) +![国际象棋](../../../translated_images/zh-CN/chess.e704a268781bdad8.webp) > 图片由 Alexis Fauvet 提供，来源于 Unsplash diff --git a/translations/zh/README.md b/translations/zh/README.md index 7522cc908..a757da5db 100644 --- a/translations/zh/README.md +++ b/translations/zh/README.md @@ -41,7 +41,7 @@ CO_OP_TRANSLATOR_METADATA: 我们正在进行 Discord上的 AI 学习系列，了解更多并加入我们：[Learn with AI Series](https://aka.ms/learnwithai/discord)，活动时间为 2025 年 9 月 18 日至 30 日。您将获得使用 GitHub Copilot 进行数据科学的技巧和窍门。 -![Learn with AI series](../../../../translated_images/zh/3.9b58fd8d6c373c20.webp) +![Learn with AI series](../../../../translated_images/zh-CN/3.9b58fd8d6c373c20.webp) # 机器学习入门课程 @@ -89,7 +89,7 @@ CO_OP_TRANSLATOR_METADATA: 部分课程提供短视频示范。您可以在课程内找到这些视频，或访问 [微软开发者频道的 ML for Beginners 播放列表](https://aka.ms/ml-beginners-videos)，点击下图观看。 -[![ML for beginners banner](../../../../translated_images/zh/ml-for-beginners-video-banner.63f694a100034bc6.webp)](https://aka.ms/ml-beginners-videos) +[![ML for beginners banner](../../../../translated_images/zh-CN/ml-for-beginners-video-banner.63f694a100034bc6.webp)](https://aka.ms/ml-beginners-videos) ---