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Utapata vidokezo na mbinu za kutumia GitHub Copilot kwa Sayansi ya Data.  # Kujifunza Mashine kwa Waanzilishi - Mtaala > 🌍 Safiri kote duniani tunapochunguza Kujifunza Mashine kwa njia ya tamaduni za dunia 🌍 Wahamasishaji wa Cloud katika Microsoft wanafurahia kutoa mtaala wa wiki 12, masomo 26 yote kuhusu **Kujifunza Mashine**. Katika mtaala huu, utajifunza kuhusu kile kinachoitwa mara nyingine **kujifunza mashine cha kawaida**, ukitumia hasa maktaba ya Scikit-learn na kuepuka kujifunza kwa kina, ambacho kinashughulikiwa katika [mtaala wetu wa AI kwa Waanzilishi](https://aka.ms/ai4beginners). Pia weka masomo haya pamoja na mtaala wetu wa ['Sayansi ya Data kwa Waanzilishi'](https://aka.ms/ds4beginners). Safiri nasi kote duniani tunapotumia mbinu hizi za kawaida kwa data kutoka sehemu nyingi za dunia. Kila somo linajumuisha maswali ya kabla na baada ya somo, maelekezo yaliyoandikwa ya kukamilisha somo, suluhisho, kazi, na zaidi. Mbinu yetu ya kujifunza kwa miradi inakuwezesha kujifunza huku ukijenga, njia iliyothibitishwa ya kufanya ujuzi mpya 'kubaki'. **✍️ Shukrani za dhati kwa waandishi wetu** Jen Looper, Stephen Howell, Francesca Lazzeri, Tomomi Imura, Cassie Breviu, Dmitry Soshnikov, Chris Noring, Anirban Mukherjee, Ornella Altunyan, Ruth Yakubu na Amy Boyd **🎨 Pia shukrani kwa wachoraji wetu** Tomomi Imura, Dasani Madipalli, na Jen Looper **🙏 Shukrani maalum 🙏 kwa waandishi, wakaguzi, na wachangiaji wa maudhui wa Microsoft Student Ambassador**, hasa Rishit Dagli, Muhammad Sakib Khan Inan, Rohan Raj, Alexandru Petrescu, Abhishek Jaiswal, Nawrin Tabassum, Ioan Samuila, na Snigdha Agarwal **🤩 Shukrani za ziada kwa Microsoft Student Ambassadors Eric Wanjau, Jasleen Sondhi, na Vidushi Gupta kwa masomo yetu ya R!** # Kuanzia Fuata hatua hizi: 1. **Fanya Fork ya Hifadhi**: Bonyeza kitufe cha "Fork" kilicho kona ya juu kulia ya ukurasa huu. 2. **Nakili Hifadhi**: `git clone https://github.com/microsoft/ML-For-Beginners.git` > [pata rasilimali zote za ziada kwa kozi hii katika mkusanyiko wetu wa Microsoft Learn](https://learn.microsoft.com/en-us/collections/qrqzamz1nn2wx3?WT.mc_id=academic-77952-bethanycheum) > 🔧 **Unahitaji msaada?** Angalia [Mwongozo wa Kutatua Matatizo](TROUBLESHOOTING.md) kwa suluhisho za matatizo ya kawaida ya usakinishaji, usanidi, na kuendesha masomo. **[Wanafunzi](https://aka.ms/student-page)**, ili kutumia mtaala huu, fanya fork ya hifadhi nzima kwenye akaunti yako ya GitHub na kamilisha mazoezi peke yako au na kikundi: - Anza na mtihani wa kabla ya somo. - Soma somo na kamilisha shughuli, simama na fikiria kila ukaguzi wa maarifa. - Jaribu kuunda miradi kwa kuelewa masomo badala ya kuendesha msimbo wa suluhisho; hata hivyo msimbo huo upo katika folda za `/solution` katika kila somo linalolenga mradi. - Fanya mtihani wa baada ya somo. - Kamilisha changamoto. - Kamilisha kazi. - Baada ya kumaliza kundi la masomo, tembelea [Bodi ya Majadiliano](https://github.com/microsoft/ML-For-Beginners/discussions) na "jifunze kwa sauti" kwa kujaza rubriki ya PAT inayofaa. 'PAT' ni Chombo cha Tathmini ya Maendeleo ambacho ni rubriki unayojaza ili kuendeleza kujifunza kwako. Unaweza pia kutoa maoni kwa PAT nyingine ili tujifunze pamoja. > Kwa masomo zaidi, tunapendekeza kufuata moduli na njia za kujifunza za [Microsoft Learn](https://docs.microsoft.com/en-us/users/jenlooper-2911/collections/k7o7tg1gp306q4?WT.mc_id=academic-77952-leestott). **Walimu**, tumetoa [mapendekezo kadhaa](for-teachers.md) juu ya jinsi ya kutumia mtaala huu. --- ## Video za maelekezo Baadhi ya masomo yanapatikana kama video fupi. Unaweza kuyapata yote haya ndani ya masomo, au kwenye [orodha ya video za ML kwa Waanzilishi kwenye kituo cha Microsoft Developer YouTube](https://aka.ms/ml-beginners-videos) kwa kubonyeza picha hapa chini. [](https://aka.ms/ml-beginners-videos) --- ## Kutana na Timu [](https://youtu.be/Tj1XWrDSYJU) **Gif na** [Mohit Jaisal](https://linkedin.com/in/mohitjaisal) > 🎥 Bonyeza picha hapo juu kwa video kuhusu mradi na watu waliouunda! --- ## Mbinu ya Kufundisha Tumechagua kanuni mbili za kufundisha wakati wa kujenga mtaala huu: kuhakikisha kuwa ni **mradi-msingi** na kwamba inajumuisha **maswali ya mara kwa mara**. Zaidi ya hayo, mtaala huu una **kauli mbiu** ya pamoja ili kuupa mshikamano. Kwa kuhakikisha maudhui yanaendana na miradi, mchakato unakuwa wa kuvutia zaidi kwa wanafunzi na kumbukumbu ya dhana itaimarishwa. Zaidi ya hayo, mtihani wa chini wa hatari kabla ya darasa huweka nia ya mwanafunzi kuelekea kujifunza mada, wakati mtihani wa pili baada ya darasa unahakikisha kumbukumbu zaidi. Mtaala huu umeundwa kuwa rahisi na wa kufurahisha na unaweza kuchukuliwa kwa jumla au sehemu. Miradi huanza ndogo na kuwa ngumu zaidi mwishoni mwa mzunguko wa wiki 12. Mtaala huu pia unajumuisha maelezo ya matumizi halisi ya ML, ambayo yanaweza kutumika kama mkopo wa ziada au kama msingi wa majadiliano. > Pata [Kanuni zetu za Maadili](CODE_OF_CONDUCT.md), [Kushiriki](CONTRIBUTING.md), [Tafsiri](TRANSLATIONS.md), na [Kutatua Matatizo](TROUBLESHOOTING.md). Tunakaribisha maoni yako ya kujenga! ## Kila somo linajumuisha - sketchnote hiari - video ya ziada hiari - video ya maelekezo (masomo mengine tu) - [mtihani wa kuamsha kabla ya somo](https://ff-quizzes.netlify.app/en/ml/) - somo lililoandikwa - kwa masomo ya mradi, mwongozo wa hatua kwa hatua wa jinsi ya kujenga mradi - ukaguzi wa maarifa - changamoto - usomaji wa ziada - kazi - [mtihani wa baada ya somo](https://ff-quizzes.netlify.app/en/ml/) > **Kumbuka kuhusu lugha**: Masomo haya yameandikwa hasa kwa Python, lakini mengi pia yanapatikana kwa R. Ili kumaliza somo la R, nenda kwenye folda ya `/solution` na tafuta masomo ya R. Yanajumuisha kiambatisho cha .rmd kinachoonyesha faili ya **R Markdown** ambayo inaweza kufafanuliwa kama kuingiza `vipande vya msimbo` (vya R au lugha nyingine) na `kichwa cha YAML` (kinachoongoza jinsi ya kuunda matokeo kama PDF) katika `nyaraka za Markdown`. Hivyo, hutumika kama mfumo bora wa uandishi kwa sayansi ya data kwa kuwa inakuwezesha kuunganisha msimbo wako, matokeo yake, na mawazo yako kwa kuandika kwa Markdown. Zaidi ya hayo, nyaraka za R Markdown zinaweza kutengenezwa kuwa aina za matokeo kama PDF, HTML, au Word. > **Kumbuka kuhusu mitihani**: Mitihani yote iko katika [folda ya Quiz App](../../quiz-app), kwa jumla ya mitihani 52 yenye maswali matatu kila moja. Imeunganishwa kutoka ndani ya masomo lakini app ya mtihani inaweza kuendeshwa kwa ndani; fuata maelekezo katika folda ya `quiz-app` ili kuendesha kwa ndani au kuweka kwenye Azure. | Nambari ya Somo | Mada | Kundi la Somo | Malengo ya Kujifunza | Somo Linalounganishwa | Mwandishi | | :-----------: | :------------------------------------------------------------: | :-------------------------------------------------: | ------------------------------------------------------------------------------------------------------------------------------- | :--------------------------------------------------------------------------------------------------------------------------------------: | :--------------------------------------------------: | | 01 | Utangulizi wa kujifunza mashine | [Introduction](1-Introduction/README.md) | Jifunze dhana za msingi nyuma ya kujifunza mashine | [Lesson](1-Introduction/1-intro-to-ML/README.md) | Muhammad | | 02 | Historia ya kujifunza mashine | [Introduction](1-Introduction/README.md) | Jifunze historia inayosababisha uwanja huu | [Lesson](1-Introduction/2-history-of-ML/README.md) | Jen and Amy | | 03 | Usawa na kujifunza mashine | [Introduction](1-Introduction/README.md) | Ni masuala gani muhimu ya kifalsafa kuhusu usawa ambayo wanafunzi wanapaswa kuyazingatia wanapojenga na kutumia mifano ya ML? | [Lesson](1-Introduction/3-fairness/README.md) | Tomomi | | 04 | Mbinu za kujifunza mashine | [Introduction](1-Introduction/README.md) | Ni mbinu gani watafiti wa ML hutumia kujenga mifano ya ML? | [Lesson](1-Introduction/4-techniques-of-ML/README.md) | Chris and Jen | | 05 | Utangulizi wa regression | [Regression](2-Regression/README.md) | Anza na Python na Scikit-learn kwa mifano ya regression | [Python](2-Regression/1-Tools/README.md) • [R](../../2-Regression/1-Tools/solution/R/lesson_1.html) | Jen • Eric Wanjau | | 06 | Bei za malenge ya Amerika Kaskazini 🎃 | [Regression](2-Regression/README.md) | Onyesha na safisha data kwa ajili ya ML | [Python](2-Regression/2-Data/README.md) • [R](../../2-Regression/2-Data/solution/R/lesson_2.html) | Jen • Eric Wanjau | | 07 | Bei za malenge ya Amerika Kaskazini 🎃 | [Regression](2-Regression/README.md) | Jenga mifano ya regression ya mstari na polynomial | [Python](2-Regression/3-Linear/README.md) • [R](../../2-Regression/3-Linear/solution/R/lesson_3.html) | Jen and Dmitry • Eric Wanjau | | 08 | Bei za malenge ya Amerika Kaskazini 🎃 | [Regression](2-Regression/README.md) | Jenga mfano wa regression ya logistic | [Python](2-Regression/4-Logistic/README.md) • [R](../../2-Regression/4-Logistic/solution/R/lesson_4.html) | Jen • Eric Wanjau | | 09 | Programu ya Wavuti 🔌 | [Web App](3-Web-App/README.md) | Jenga programu ya wavuti kutumia mfano wako uliopata mafunzo | [Python](3-Web-App/1-Web-App/README.md) | Jen | | 10 | Utangulizi wa uainishaji | [Classification](4-Classification/README.md) | Safisha, andaa, na onyesha data yako; utangulizi wa uainishaji | [Python](4-Classification/1-Introduction/README.md) • [R](../../4-Classification/1-Introduction/solution/R/lesson_10.html) | Jen and Cassie • Eric Wanjau | | 11 | Vyakula vitamu vya Asia na India 🍜 | [Classification](4-Classification/README.md) | Utangulizi wa waainishaji | [Python](4-Classification/2-Classifiers-1/README.md) • [R](../../4-Classification/2-Classifiers-1/solution/R/lesson_11.html) | Jen and Cassie • Eric Wanjau | | 12 | Vyakula vitamu vya Asia na India 🍜 | [Classification](4-Classification/README.md) | Waainishaji zaidi | [Python](4-Classification/3-Classifiers-2/README.md) • [R](../../4-Classification/3-Classifiers-2/solution/R/lesson_12.html) | Jen and Cassie • Eric Wanjau | | 13 | Vyakula vitamu vya Asia na India 🍜 | [Classification](4-Classification/README.md) | Jenga programu ya wavuti ya kupendekeza kwa kutumia mfano wako | [Python](4-Classification/4-Applied/README.md) | Jen | | 14 | Utangulizi wa ugawaji kundi | [Clustering](5-Clustering/README.md) | Safisha, andaa, na onyesha data yako; Utangulizi wa ugawaji kundi | [Python](5-Clustering/1-Visualize/README.md) • [R](../../5-Clustering/1-Visualize/solution/R/lesson_14.html) | Jen • Eric Wanjau | | 15 | Kuchunguza Ladha za Muziki za Nigeria 🎧 | [Clustering](5-Clustering/README.md) | Chunguza mbinu ya ugawaji kundi ya 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 | Utangulizi wa usindikaji wa lugha asilia ☕️ | [Natural language processing](6-NLP/README.md) | Jifunze misingi ya NLP kwa kujenga bot rahisi | [Python](6-NLP/1-Introduction-to-NLP/README.md) | Stephen | | 17 | Kazi za kawaida za NLP ☕️ | [Natural language processing](6-NLP/README.md) | Zidi maarifa yako ya NLP kwa kuelewa kazi za kawaida zinazohitajika katika kushughulikia miundo ya lugha | [Python](6-NLP/2-Tasks/README.md) | Stephen | | 18 | Tafsiri na uchambuzi wa hisia ♥️ | [Natural language processing](6-NLP/README.md) | Tafsiri na uchambuzi wa hisia na Jane Austen | [Python](6-NLP/3-Translation-Sentiment/README.md) | Stephen | | 19 | Hoteli za kimapenzi za Ulaya ♥️ | [Natural language processing](6-NLP/README.md) | Uchambuzi wa hisia kwa mapitio ya hoteli 1 | [Python](6-NLP/4-Hotel-Reviews-1/README.md) | Stephen | | 20 | Hoteli za kimapenzi za Ulaya ♥️ | [Natural language processing](6-NLP/README.md) | Uchambuzi wa hisia kwa mapitio ya hoteli 2 | [Python](6-NLP/5-Hotel-Reviews-2/README.md) | Stephen | | 21 | Utangulizi wa utabiri wa mfululizo wa wakati | [Time series](7-TimeSeries/README.md) | Utangulizi wa utabiri wa mfululizo wa wakati | [Python](7-TimeSeries/1-Introduction/README.md) | Francesca | | 22 | ⚡️ Matumizi ya Nguvu Duniani ⚡️ - utabiri wa mfululizo wa wakati na ARIMA | [Time series](7-TimeSeries/README.md) | Utabiri wa mfululizo wa wakati kwa kutumia ARIMA | [Python](7-TimeSeries/2-ARIMA/README.md) | Francesca | | 23 | ⚡️ Matumizi ya Nguvu Duniani ⚡️ - utabiri wa mfululizo wa wakati na SVR | [Time series](7-TimeSeries/README.md) | Utabiri wa mfululizo wa wakati kwa kutumia Support Vector Regressor | [Python](7-TimeSeries/3-SVR/README.md) | Anirban | | 24 | Utangulizi wa kujifunza kwa kuimarisha | [Reinforcement learning](8-Reinforcement/README.md) | Utangulizi wa kujifunza kwa kuimarisha kwa kutumia Q-Learning | [Python](8-Reinforcement/1-QLearning/README.md) | Dmitry | | 25 | Msaada kwa Peter kuepuka mbwa mwitu! 🐺 | [Reinforcement learning](8-Reinforcement/README.md) | Gym ya kujifunza kwa kuimarisha | [Python](8-Reinforcement/2-Gym/README.md) | Dmitry | | Postscript | Hali halisi za ML na matumizi yake | [ML in the Wild](9-Real-World/README.md) | Matumizi ya kuvutia na ya kufichua ya ML ya kawaida | [Lesson](9-Real-World/1-Applications/README.md) | Team | | Postscript | Urekebishaji wa Mifano katika ML kwa kutumia dashibodi ya RAI | [ML in the Wild](9-Real-World/README.md) | Urekebishaji wa Mifano katika Kujifunza Mashine kwa kutumia vipengele vya dashibodi ya Responsible AI | [Lesson](9-Real-World/2-Debugging-ML-Models/README.md) | Ruth Yakubu | > [pata rasilimali zote za ziada kwa kozi hii katika mkusanyiko wetu wa Microsoft Learn](https://learn.microsoft.com/en-us/collections/qrqzamz1nn2wx3?WT.mc_id=academic-77952-bethanycheum) ## Ufikiaji wa nje ya mtandao Unaweza kuendesha nyaraka hii nje ya mtandao kwa kutumia [Docsify](https://docsify.js.org/#/). Fanya nakala ya repo hii, [weka Docsify](https://docsify.js.org/#/quickstart) kwenye mashine yako ya ndani, kisha katika folda kuu ya repo hii, andika `docsify serve`. Tovuti itahudumiwa kwenye bandari 3000 kwenye localhost yako: `localhost:3000`. ## PDFs Pata pdf ya mtaala na viungo [hapa](https://microsoft.github.io/ML-For-Beginners/pdf/readme.pdf). ## 🎒 Kozi Nyingine Timu yetu hutengeneza kozi nyingine! Angalia: ### LangChain [](https://aka.ms/langchain4j-for-beginners) [](https://aka.ms/langchainjs-for-beginners?WT.mc_id=m365-94501-dwahlin) --- ### Azure / Edge / MCP / Agents [](https://github.com/microsoft/AZD-for-beginners?WT.mc_id=academic-105485-koreyst) [](https://github.com/microsoft/edgeai-for-beginners?WT.mc_id=academic-105485-koreyst) [](https://github.com/microsoft/mcp-for-beginners?WT.mc_id=academic-105485-koreyst) [](https://github.com/microsoft/ai-agents-for-beginners?WT.mc_id=academic-105485-koreyst) --- ### Mfululizo wa AI ya Kizazi [](https://github.com/microsoft/generative-ai-for-beginners?WT.mc_id=academic-105485-koreyst) [-9333EA?style=for-the-badge&labelColor=E5E7EB&color=9333EA)](https://github.com/microsoft/Generative-AI-for-beginners-dotnet?WT.mc_id=academic-105485-koreyst) [-C084FC?style=for-the-badge&labelColor=E5E7EB&color=C084FC)](https://github.com/microsoft/generative-ai-for-beginners-java?WT.mc_id=academic-105485-koreyst) [-E879F9?style=for-the-badge&labelColor=E5E7EB&color=E879F9)](https://github.com/microsoft/generative-ai-with-javascript?WT.mc_id=academic-105485-koreyst) --- ### Kujifunza Msingi [](https://aka.ms/ml-beginners?WT.mc_id=academic-105485-koreyst) [](https://aka.ms/datascience-beginners?WT.mc_id=academic-105485-koreyst) [](https://aka.ms/ai-beginners?WT.mc_id=academic-105485-koreyst) [](https://github.com/microsoft/Security-101?WT.mc_id=academic-96948-sayoung) [](https://aka.ms/webdev-beginners?WT.mc_id=academic-105485-koreyst) [](https://aka.ms/iot-beginners?WT.mc_id=academic-105485-koreyst) [](https://github.com/microsoft/xr-development-for-beginners?WT.mc_id=academic-105485-koreyst) --- ### Mfululizo wa Copilot [](https://aka.ms/GitHubCopilotAI?WT.mc_id=academic-105485-koreyst) [](https://github.com/microsoft/mastering-github-copilot-for-dotnet-csharp-developers?WT.mc_id=academic-105485-koreyst) [](https://github.com/microsoft/CopilotAdventures?WT.mc_id=academic-105485-koreyst) ## Kupata Msaada Ikiwa unakumbwa na shida au una maswali yoyote kuhusu kujenga programu za AI. Jiunge na wapenzi wa kujifunza na waendelezaji wenye uzoefu katika mijadala kuhusu MCP. Ni jamii yenye msaada ambapo maswali yanakaribishwa na maarifa yanashirikiwa kwa uhuru. [](https://discord.gg/nTYy5BXMWG) Ikiwa una maoni kuhusu bidhaa au makosa wakati wa kujenga tembelea: [](https://aka.ms/foundry/forum) --- **Kiarifa cha Kukataa**: Hati hii imetafsiriwa kwa kutumia huduma ya tafsiri ya AI [Co-op Translator](https://github.com/Azure/co-op-translator). Ingawa tunajitahidi kwa usahihi, tafadhali fahamu kuwa tafsiri za kiotomatiki zinaweza kuwa na makosa au upungufu wa usahihi. Hati ya asili katika lugha yake ya asili inapaswa kuchukuliwa kama chanzo cha mamlaka. Kwa taarifa muhimu, tafsiri ya kitaalamu ya binadamu inapendekezwa. Hatubebei dhamana kwa kutoelewana au tafsiri potofu zinazotokana na matumizi ya tafsiri hii.