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.gitignore vendored
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@ -33,6 +33,8 @@ bld/
# Visual Studio 2015/2017 cache/options directory
.vs/
# Visual Studio Code cache/options directory
.vscode/
# Uncomment if you have tasks that create the project's static files in wwwroot
#wwwroot/

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1-Introduction/1-intro-to-ML/translations/README.fr.md
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@ -4,7 +4,7 @@
> 🎥 Cliquer sur l'image ci-dessus afin de regarder une vidéo expliquant la différence entre machine learning, AI et deep learning.
## [Quiz préalable](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/1/)
## [Quiz préalable](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/1?loc=fr)
### Introduction
@ -98,7 +98,7 @@ Dans un avenir proche, comprendre les bases du machine learning sera indispensab
Esquisser, sur papier ou à l'aide d'une application en ligne comme [Excalidraw](https://excalidraw.com/), votre compréhension des différences entre l'IA, le ML, le deep learning et la data science. Ajouter quelques idées de problèmes que chacune de ces techniques est bonne à résoudre.
## [Quiz de validation des connaissances](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/2/)
## [Quiz de validation des connaissances](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/2?loc=fr)
## Révision et auto-apprentissage

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@ -98,8 +98,8 @@ AI、ML、深層学習、データサイエンスの違いについて理解し
## 振り返りと自習
クラウド上でMLアルゴリズムをどのように扱うことができるかについては、この[ラーニングパス](https://docs.microsoft.com/learn/paths/create-no-code-predictive-models-azure-machine-learning/?WT.mc_id=academic-15963-cxa)に従ってください。.
クラウド上でMLアルゴリズムをどのように扱うことができるかについては、この[ラーニングパス](https://docs.microsoft.com/learn/paths/create-no-code-predictive-models-azure-machine-learning/?WT.mc_id=academic-15963-cxa)に従ってください。
## 課題
[起動し、実行してください。](assignment.md)
[稼働させる](assignment.ja.md)

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# 稼働させる
## 指示
この評価のない課題では、Pythonについて復習し、環境を稼働させてノートブックを実行できるようにする必要があります。
この[Pythonラーニングパス](https://docs.microsoft.com/learn/paths/python-language/?WT.mc_id=academic-15963-cxa)を受講し、次の入門用ビデオに従ってシステムをセットアップしてください。
https://www.youtube.com/playlist?list=PLlrxD0HtieHhS8VzuMCfQD4uJ9yne1mE6

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1-Introduction/2-history-of-ML/translations/README.fr.md
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![Résumé de l'histoire du machine learning dans un sketchnote](../../../sketchnotes/ml-history.png)
> Sketchnote de [Tomomi Imura](https://www.twitter.com/girlie_mac)
## [Quizz préalable](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/3/)
## [Quizz préalable](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/3?loc=fr)
Dans cette leçon, nous allons parcourir les principales étapes de l'histoire du machine learning et de l'intelligence artificielle.
@ -102,7 +102,7 @@ Reste à savoir ce que l'avenir nous réserve, mais il est important de comprend
Plongez dans l'un de ces moments historiques et apprenez-en plus sur les personnes derrière ceux-ci. Il y a des personnalités fascinantes, et aucune découverte scientifique n'a jamais été créée avec un vide culturel. Que découvrez-vous ?
## [Quiz de validation des connaissances](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/4/)
## [Quiz de validation des connaissances](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/4?loc=fr)
## Révision et auto-apprentissage

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1-Introduction/translations/README.fr.md
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@ -7,10 +7,10 @@ Dans cette section du programme, vous découvrirez les concepts de base sous-jac
### Leçons
1. [Introduction au machine learning](1-intro-to-ML/README.md)
1. [L’histoire du machine learning et de l’IA](2-history-of-ML/README.md)
1. [Équité et machine learning](3-équité/README.md)
1. [Techniques de machine learning](4-techniques-of-ML/README.md)
1. [Introduction au machine learning](../1-intro-to-ML/translations/README.fr.md)
1. [L’histoire du machine learning et de l’IA](../2-history-of-ML/translations/README.fr.md)
1. [Équité et machine learning](../3-fairness/translations/README.fr.md)
1. [Techniques de machine learning](../4-techniques-of-ML/translations/README.fr.md)
### Crédits
"Introduction au machine learning" a été écrit avec ♥️ par une équipe de personnes comprenant [Muhammad Sakib Khan Inan](https://twitter.com/Sakibinan), [Ornella Altunyan](https://twitter.com/ornelladotcom) et [Jen Looper](https://twitter.com/jenlooper)

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# 用 Scikit-learn 实现一次回归算法
## 说明
先看看 Scikit-learn 中的 [Linnerud 数据集](https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_linnerud.html#sklearn.datasets.load_linnerud)
这个数据集中有多个[目标变量（target）](https://scikit-learn.org/stable/datasets/toy_dataset.html#linnerrud-dataset)，其中包含了三种运动（训练数据）和三个生理指标（目标变量）组成，这些数据都是从一个健身俱乐部中的20名中年男子收集到的。
之后用自己的方式，创建一个可以描述腰围和完成仰卧起坐个数关系的回归模型。用同样的方式对这个数据集中的其它数据也建立一下模型探究一下其中的关系。
## 评判标准
| 标准 | 优秀 | 中规中矩 | 仍需努力 |
| ------------------------------ | ----------------------------------- | ----------------------------- | -------------------------- |
| 需要提交一段能描述数据集中关系的文字 | 很好的描述了数据集中的关系 | 只能描述少部分的关系 | 啥都没有提交 |

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# 探索数据可视化
有好几个库都可以进行数据可视化。用 matplotlib 和 seaborn 对本课中涉及的 Pumpkin 数据集创建一些数据可视化的图标。并思考哪个库更容易使用？
## 评判标准
| 标准 | 优秀 | 中规中矩 | 仍需努力 |
| -------- | --------- | -------- | ----------------- |
| | 提交了含有两种探索可视化方法的notebook工程文件 | 提交了只包含有一种探索可视化方法的notebook工程文件 | 没提交 notebook 工程文件 |

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# 创建自己的回归模型
## 说明
在这节课中你学到了如何用线性回归和多项式回归建立一个模型。利用这些只是，找到一个你感兴趣的数据集或者是 Scikit-learn 内置的数据集来建立一个全新的模型。用你的 notebook 来解释为什么用了这种技术来对这个数据集进行建模，并且证明出你的模型的准确度。如果它没你想象中准确，请思考一下并解释一下原因。
## 评判标准
| 标准 | 优秀 | 中规中矩 | 仍需努力 |
| -------- | ------------------------------------------------------------ | -------------------------- | ------------------------------- |
| | 提交了一个完整的 notebook 工程文件，其中包含了解集，并且可读性良好 | 不完整的解集 | 解集是有缺陷或者有错误的 |

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# 再探回归模型
## 说明
在这节课中，你使用了 pumpkin 数据集的子集。现在，让我们回到原始数据，并尝试使用所有数据。经过了数据清理和标准化，建立一个逻辑回归模型。
## 评判标准
| 标准 | 优秀 | 中规中矩 | 仍需努力 |
| -------- | ----------------------------------------------------------------------- | ------------------------------------------------------------ | ----------------------------------------------------------- |
| | 用notebook呈现了一个解释性和性能良好的模型 | 用notebook呈现了一个性能一般的模型 | 用notebook呈现了一个性能差的模型或根本没有模型 |

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4-Classification/1-Introduction/README.md
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@ -163,7 +163,7 @@ Now you can dig deeper into the data and learn what are the typical ingredients
def create_ingredient_df(df):
ingredient_df = df.T.drop(['cuisine','Unnamed: 0']).sum(axis=1).to_frame('value')
ingredient_df = ingredient_df[(ingredient_df.T != 0).any()]
ingredient_df = ingredient_df.sort_values(by='value', ascending=False
ingredient_df = ingredient_df.sort_values(by='value', ascending=False,
inplace=False)
return ingredient_df
```
@ -275,7 +275,7 @@ Now that you have cleaned the data, use [SMOTE](https://imbalanced-learn.org/dev
```python
transformed_df.head()
transformed_df.info()
transformed_df.to_csv("../data/cleaned_cuisine.csv")
transformed_df.to_csv("../data/cleaned_cuisines.csv")
```
This fresh CSV can now be found in the root data folder.

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@ -622,7 +622,7 @@
"metadata": {},
"outputs": [],
"source": [
"transformed_df.to_csv(\"../../data/cleaned_cuisine.csv\")"
"transformed_df.to_csv(\"../../data/cleaned_cuisines.csv\")"
]
},
{

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@ -275,7 +275,8 @@ Veriyi temizlediniz, şimdi [SMOTE](https://imbalanced-learn.org/dev/references/
```python
transformed_df.head()
transformed_df.info()
transformed_df.to_csv("../data/cleaned_cuisine.csv")
transformed_df.to_csv("../../data/cleaned_cuisines.csv")
```
Bu yeni CSV şimdi kök data (veri) klasöründe görülebilir.

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@ -269,7 +269,7 @@ Scikit-learn项目提供多种对数据进行分类的算法，你需要根据
```python
transformed_df.head()
transformed_df.info()
transformed_df.to_csv("../data/cleaned_cuisine.csv")
transformed_df.to_csv("../data/cleaned_cuisines.csv")
```
这个全新的CSV文件可以在数据根目录中被找到。

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@ -15,7 +15,7 @@ Assuming you completed [Lesson 1](../1-Introduction/README.md), make sure that a
```python
import pandas as pd
cuisines_df = pd.read_csv("../../data/cleaned_cuisine.csv")
cuisines_df = pd.read_csv("../../data/cleaned_cuisines.csv")
cuisines_df.head()
```
@ -67,13 +67,13 @@ Assuming you completed [Lesson 1](../1-Introduction/README.md), make sure that a
Your features look like this:
| almond | angelica | anise | anise_seed | apple | apple_brandy | apricot | armagnac | artemisia | artichoke | ... | whiskey | white_bread | white_wine | whole_grain_wheat_flour | wine | wood | yam | yeast | yogurt | zucchini |
| -----: | -------: | ----: | ---------: | ----: | -----------: | ------: | -------: | --------: | --------: | ---: | ------: | ----------: | ---------: | ----------------------: | ---: | ---: | ---: | ----: | -----: | -------: |
| 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
| | almond | angelica | anise | anise_seed | apple | apple_brandy | apricot | armagnac | artemisia | artichoke | ... | whiskey | white_bread | white_wine | whole_grain_wheat_flour | wine | wood | yam | yeast | yogurt | zucchini |
| -----: | -------: | ----: | ---------: | ----: | -----------: | ------: | -------: | --------: | --------: | ---: | ------: | ----------: | ---------: | ----------------------: | ---: | ---: | ---: | ----: | -----: | -------: | -----: |
| 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 |
Now you are ready to train your model!

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@ -47,7 +47,7 @@
],
"source": [
"import pandas as pd\n",
"cuisines_df = pd.read_csv(\"../../data/cleaned_cuisine.csv\")\n",
"cuisines_df = pd.read_csv(\"../../data/cleaned_cuisines.csv\")\n",
"cuisines_df.head()"
]
},

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4-Classification/3-Classifiers-2/README.md
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@ -6,7 +6,7 @@ In this second classification lesson, you will explore more ways to classify num
### Prerequisite
We assume that you have completed the previous lessons and have a cleaned dataset in your `data` folder called _cleaned_cuisine.csv_ in the root of this 4-lesson folder.
We assume that you have completed the previous lessons and have a cleaned dataset in your `data` folder called _cleaned_cuisines.csv_ in the root of this 4-lesson folder.
### Preparation

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@ -47,7 +47,7 @@
],
"source": [
"import pandas as pd\n",
"cuisines_df = pd.read_csv(\"../data/cleaned_cuisine.csv\")\n",
"cuisines_df = pd.read_csv(\"../data/cleaned_cuisines.csv\")\n",
"cuisines_df.head()"
]
},

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@ -47,7 +47,7 @@
],
"source": [
"import pandas as pd\n",
"cuisines_df = pd.read_csv(\"../../data/cleaned_cuisine.csv\")\n",
"cuisines_df = pd.read_csv(\"../../data/cleaned_cuisines.csv\")\n",
"cuisines_df.head()"
]
},

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4-Classification/4-Applied/README.md
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@ -40,7 +40,7 @@ First, train a classification model using the cleaned cuisines dataset we used.
1. Then, work with your data in the same way you did in previous lessons, by reading a CSV file using `read_csv()`:
```python
data = pd.read_csv('../data/cleaned_cuisine.csv')
data = pd.read_csv('../data/cleaned_cuisines.csv')
data.head()
```

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@ -115,7 +115,7 @@
}
],
"source": [
"data = pd.read_csv('../../data/cleaned_cuisine.csv')\n",
"data = pd.read_csv('../../data/cleaned_cuisines.csv')\n",
"data.head()"
]
},

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package.json
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@ -4,8 +4,8 @@
"description": "Machine Learning for Beginners - A Curriculum",
"main": "index.js",
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},
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},
"repository": {
"type": "git",
"url": "git+https://github.com/microsoft/ML-For-Beginners.git"

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@ -1087,16 +1087,6 @@
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@ -1746,6 +1746,53 @@
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@ -1762,6 +1809,16 @@
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@ -1778,6 +1835,18 @@
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"webpack-sources": "^1.4.3"
}
},
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@ -10953,87 +11022,6 @@
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1
quiz-app/src/App.vue
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@ -6,6 +6,7 @@
<select v-model="locale">
<option>en</option>
<option>tr</option>
<option>fr</option>
</select>
</nav>
<div id="app">

2811
quiz-app/src/assets/translations/fr.json
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2
quiz-app/src/assets/translations/index.js
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@ -1,12 +1,14 @@
// index.js
import en from './en.json';
import tr from './tr.json';
import fr from './fr.json';
//export const defaultLocale = 'en';
const messages = {
en: en[0],
tr: tr[0],
fr: fr[0]
};
export default messages;

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