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@ -46,7 +46,9 @@ Travel with us around the world as we apply these classic techniques to data fro
## Meet the Team
[![Promo video](ml-for-beginners.png)](https://youtu.be/Tj1XWrDSYJU "Promo video")
[![Promo video](ml.gif)](https://youtu.be/Tj1XWrDSYJU "Promo video")
**Gif by** [Mohit Jaisal](https://linkedin.com/in/mohitjaisal)
> 🎥 Click the image above for a video about the project and the folks who created it!
@ -77,34 +79,34 @@ By ensuring that the content aligns with projects, the process is made more enga
> **A note about quizzes**: All quizzes are contained [in this app](https://white-water-09ec41f0f.azurestaticapps.net/), 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.
| Lesson Number | Topic | Lesson Grouping | Learning Objectives | Linked Lesson | Author |
|:-------------:|:----------------------------------------------------------:|:---------------------------------------------------:|---------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------:|:--------------:|
| 01 | Introduction to machine learning | [Introduction](1-Introduction/README.md) | Learn the basic concepts behind machine learning | [Lesson](1-Introduction/1-intro-to-ML/README.md) | Muhammad |
| 02 | The History of machine learning | [Introduction](1-Introduction/README.md) | Learn the history underlying this field | [Lesson](1-Introduction/2-history-of-ML/README.md) | Jen and Amy |
| 03 | Fairness and machine learning | [Introduction](1-Introduction/README.md) | What are the important philosophical issues around fairness that students should consider when building and applying ML models? | [Lesson](1-Introduction/3-fairness/README.md) | Tomomi |
| 04 | Techniques for machine learning | [Introduction](1-Introduction/README.md) | What techniques do ML researchers use to build ML models? | [Lesson](1-Introduction/4-techniques-of-ML/README.md) | Chris and Jen |
| 05 | Introduction to regression | [Regression](2-Regression/README.md) | Get started with Python and Scikit-learn for regression models | <ul><li>[Python](2-Regression/1-Tools/README.md)</li><li>[R](2-Regression/1-Tools/solution/R/lesson_1-R.ipynb)</li></ul> | <ul><li>Jen</li><li>Eric Wanjau</li></ul> |
| 06 | North American pumpkin prices 🎃 | [Regression](2-Regression/README.md) | Visualize and clean data in preparation for ML | <ul><li>[Python](2-Regression/2-Data/README.md)</li><li>[R](2-Regression/2-Data/solution/R/lesson_2-R.ipynb)</li></ul> | <ul><li>Jen</li><li>Eric Wanjau</li></ul> |
| 07 | North American pumpkin prices 🎃 | [Regression](2-Regression/README.md) | Build linear and polynomial regression models | <ul><li>[Python](2-Regression/3-Linear/README.md)</li><li>[R](2-Regression/3-Linear/solution/R/lesson_3-R.ipynb)</li></ul> | <ul><li>Jen</li><li>Eric Wanjau</li></ul> |
| 08 | North American pumpkin prices 🎃 | [Regression](2-Regression/README.md) | Build a logistic regression model | <ul><li>[Python](2-Regression/4-Logistic/README.md) </li><li>[R](2-Regression/4-Logistic/solution/R/lesson_4-R.ipynb)</li></ul> | <ul><li>Jen</li><li>Eric Wanjau</li></ul> |
| 09 | A Web App 🔌 | [Web App](3-Web-App/README.md) | Build a web app to use your trained model | [Python](3-Web-App/1-Web-App/README.md) | Jen |
| 10 | Introduction to classification | [Classification](4-Classification/README.md) | Clean, prep, and visualize your data; introduction to classification |<ul><li> [Python](4-Classification/1-Introduction/README.md) </li><li>[R](4-Classification/1-Introduction/solution/R/lesson_10-R.ipynb) | <ul><li>Jen and Cassie</li><li>Eric Wanjau</li></ul> |
| 11 | Delicious Asian and Indian cuisines 🍜 | [Classification](4-Classification/README.md) | Introduction to classifiers |<ul><li> [Python](4-Classification/2-Classifiers-1/README.md)</li><li>[R](4-Classification/2-Classifiers-1/solution/R/lesson_11-R.ipynb) | <ul><li>Jen and Cassie</li><li>Eric Wanjau</li></ul> |
| 12 | Delicious Asian and Indian cuisines 🍜 | [Classification](4-Classification/README.md) | More classifiers |<ul><li> [Python](4-Classification/3-Classifiers-2/README.md)</li><li>[R](4-Classification/3-Classifiers-2/solution/R/lesson_12-R.ipynb) | <ul><li>Jen and Cassie</li><li>Eric Wanjau</li></ul> |
| 13 | Delicious Asian and Indian cuisines 🍜 | [Classification](4-Classification/README.md) | Build a recommender web app using your model | [Python](4-Classification/4-Applied/README.md) | Jen |
| 14 | Introduction to clustering | [Clustering](5-Clustering/README.md) | Clean, prep, and visualize your data; Introduction to clustering | <ul><li> [Python](5-Clustering/1-Visualize/README.md)</li><li>[R](5-Clustering/1-Visualize/solution/R/lesson_14-R.ipynb) | <ul><li>Jen</li><li>Eric Wanjau</li></ul> |
| 15 | Exploring Nigerian Musical Tastes 🎧 | [Clustering](5-Clustering/README.md) | Explore the K-Means clustering method | <ul><li> [Python](5-Clustering/2-K-Means/README.md)</li><li>[R](5-Clustering/2-K-Means/solution/R/lesson_15-R.ipynb) | <ul><li>Jen</li><li>Eric Wanjau</li></ul> |
| 16 | Introduction to natural language processing ☕️ | [Natural language processing](6-NLP/README.md) | Learn the basics about NLP by building a simple bot | [Python](6-NLP/1-Introduction-to-NLP/README.md) | Stephen |
| 17 | Common NLP Tasks ☕️ | [Natural language processing](6-NLP/README.md) | Deepen your NLP knowledge by understanding common tasks required when dealing with language structures | [Python](6-NLP/2-Tasks/README.md) | Stephen |
| 18 | Translation and sentiment analysis ♥️ | [Natural language processing](6-NLP/README.md) | Translation and sentiment analysis with Jane Austen | [Python](6-NLP/3-Translation-Sentiment/README.md) | Stephen |
| 19 | Romantic hotels of Europe ♥️ | [Natural language processing](6-NLP/README.md) | Sentiment analysis with hotel reviews 1 | [Python](6-NLP/4-Hotel-Reviews-1/README.md) | Stephen |
| 20 | Romantic hotels of Europe ♥️ | [Natural language processing](6-NLP/README.md) | Sentiment analysis with hotel reviews 2 | [Python](6-NLP/5-Hotel-Reviews-2/README.md) | Stephen |
| 21 | Introduction to time series forecasting | [Time series](7-TimeSeries/README.md) | Introduction to time series forecasting | [Python](7-TimeSeries/1-Introduction/README.md) | Francesca |
| 22 | ⚡️ World Power Usage ⚡️ - time series forecasting with ARIMA | [Time series](7-TimeSeries/README.md) | Time series forecasting with ARIMA | [Python](7-TimeSeries/2-ARIMA/README.md) | Francesca |
| 23 | ⚡️ World Power Usage ⚡️ - time series forecasting with SVR | [Time series](7-TimeSeries/README.md) | Time series forecasting with Support Vector Regressor | [Python](7-TimeSeries/3-SVR/README.md) | Anirban |
| 24 | Introduction to reinforcement learning | [Reinforcement learning](8-Reinforcement/README.md) | Introduction to reinforcement learning with Q-Learning | [Python](8-Reinforcement/1-QLearning/README.md) | Dmitry |
| 25 | Help Peter avoid the wolf! 🐺 | [Reinforcement learning](8-Reinforcement/README.md) | Reinforcement learning Gym | [Python](8-Reinforcement/2-Gym/README.md) | Dmitry |
| Postscript | Real-World ML scenarios and applications | [ML in the Wild](9-Real-World/README.md) | Interesting and revealing real-world applications of classical ML | [Lesson](9-Real-World/1-Applications/README.md) | Team |
| Lesson Number | Topic | Lesson Grouping | Learning Objectives | Linked Lesson | Author |
| :-----------: | :------------------------------------------------------------: | :-------------------------------------------------: | ------------------------------------------------------------------------------------------------------------------------------- | :--------------------------------------------------------------------------------------------------------------------------------------: | :--------------------------------------------------: |
| 01 | Introduction to machine learning | [Introduction](1-Introduction/README.md) | Learn the basic concepts behind machine learning | [Lesson](1-Introduction/1-intro-to-ML/README.md) | Muhammad |
| 02 | The History of machine learning | [Introduction](1-Introduction/README.md) | Learn the history underlying this field | [Lesson](1-Introduction/2-history-of-ML/README.md) | Jen and Amy |
| 03 | Fairness and machine learning | [Introduction](1-Introduction/README.md) | What are the important philosophical issues around fairness that students should consider when building and applying ML models? | [Lesson](1-Introduction/3-fairness/README.md) | Tomomi |
| 04 | Techniques for machine learning | [Introduction](1-Introduction/README.md) | What techniques do ML researchers use to build ML models? | [Lesson](1-Introduction/4-techniques-of-ML/README.md) | Chris and Jen |
| 05 | Introduction to regression | [Regression](2-Regression/README.md) | Get started with Python and Scikit-learn for regression models | <ul><li>[Python](2-Regression/1-Tools/README.md)</li><li>[R](2-Regression/1-Tools/solution/R/lesson_1-R.ipynb)</li></ul> | <ul><li>Jen</li><li>Eric Wanjau</li></ul> |
| 06 | North American pumpkin prices 🎃 | [Regression](2-Regression/README.md) | Visualize and clean data in preparation for ML | <ul><li>[Python](2-Regression/2-Data/README.md)</li><li>[R](2-Regression/2-Data/solution/R/lesson_2-R.ipynb)</li></ul> | <ul><li>Jen</li><li>Eric Wanjau</li></ul> |
| 07 | North American pumpkin prices 🎃 | [Regression](2-Regression/README.md) | Build linear and polynomial regression models | <ul><li>[Python](2-Regression/3-Linear/README.md)</li><li>[R](2-Regression/3-Linear/solution/R/lesson_3-R.ipynb)</li></ul> | <ul><li>Jen</li><li>Eric Wanjau</li></ul> |
| 08 | North American pumpkin prices 🎃 | [Regression](2-Regression/README.md) | Build a logistic regression model | <ul><li>[Python](2-Regression/4-Logistic/README.md) </li><li>[R](2-Regression/4-Logistic/solution/R/lesson_4-R.ipynb)</li></ul> | <ul><li>Jen</li><li>Eric Wanjau</li></ul> |
| 09 | A Web App 🔌 | [Web App](3-Web-App/README.md) | Build a web app to use your trained model | [Python](3-Web-App/1-Web-App/README.md) | Jen |
| 10 | Introduction to classification | [Classification](4-Classification/README.md) | Clean, prep, and visualize your data; introduction to classification | <ul><li> [Python](4-Classification/1-Introduction/README.md) </li><li>[R](4-Classification/1-Introduction/solution/R/lesson_10-R.ipynb) | <ul><li>Jen and Cassie</li><li>Eric Wanjau</li></ul> |
| 11 | Delicious Asian and Indian cuisines 🍜 | [Classification](4-Classification/README.md) | Introduction to classifiers | <ul><li> [Python](4-Classification/2-Classifiers-1/README.md)</li><li>[R](4-Classification/2-Classifiers-1/solution/R/lesson_11-R.ipynb) | <ul><li>Jen and Cassie</li><li>Eric Wanjau</li></ul> |
| 12 | Delicious Asian and Indian cuisines 🍜 | [Classification](4-Classification/README.md) | More classifiers | <ul><li> [Python](4-Classification/3-Classifiers-2/README.md)</li><li>[R](4-Classification/3-Classifiers-2/solution/R/lesson_12-R.ipynb) | <ul><li>Jen and Cassie</li><li>Eric Wanjau</li></ul> |
| 13 | Delicious Asian and Indian cuisines 🍜 | [Classification](4-Classification/README.md) | Build a recommender web app using your model | [Python](4-Classification/4-Applied/README.md) | Jen |
| 14 | Introduction to clustering | [Clustering](5-Clustering/README.md) | Clean, prep, and visualize your data; Introduction to clustering | <ul><li> [Python](5-Clustering/1-Visualize/README.md)</li><li>[R](5-Clustering/1-Visualize/solution/R/lesson_14-R.ipynb) | <ul><li>Jen</li><li>Eric Wanjau</li></ul> |
| 15 | Exploring Nigerian Musical Tastes 🎧 | [Clustering](5-Clustering/README.md) | Explore the K-Means clustering method | <ul><li> [Python](5-Clustering/2-K-Means/README.md)</li><li>[R](5-Clustering/2-K-Means/solution/R/lesson_15-R.ipynb) | <ul><li>Jen</li><li>Eric Wanjau</li></ul> |
| 16 | Introduction to natural language processing ☕️ | [Natural language processing](6-NLP/README.md) | Learn the basics about NLP by building a simple bot | [Python](6-NLP/1-Introduction-to-NLP/README.md) | Stephen |
| 17 | Common NLP Tasks ☕️ | [Natural language processing](6-NLP/README.md) | Deepen your NLP knowledge by understanding common tasks required when dealing with language structures | [Python](6-NLP/2-Tasks/README.md) | Stephen |
| 18 | Translation and sentiment analysis ♥️ | [Natural language processing](6-NLP/README.md) | Translation and sentiment analysis with Jane Austen | [Python](6-NLP/3-Translation-Sentiment/README.md) | Stephen |
| 19 | Romantic hotels of Europe ♥️ | [Natural language processing](6-NLP/README.md) | Sentiment analysis with hotel reviews 1 | [Python](6-NLP/4-Hotel-Reviews-1/README.md) | Stephen |
| 20 | Romantic hotels of Europe ♥️ | [Natural language processing](6-NLP/README.md) | Sentiment analysis with hotel reviews 2 | [Python](6-NLP/5-Hotel-Reviews-2/README.md) | Stephen |
| 21 | Introduction to time series forecasting | [Time series](7-TimeSeries/README.md) | Introduction to time series forecasting | [Python](7-TimeSeries/1-Introduction/README.md) | Francesca |
| 22 | ⚡️ World Power Usage ⚡️ - time series forecasting with ARIMA | [Time series](7-TimeSeries/README.md) | Time series forecasting with ARIMA | [Python](7-TimeSeries/2-ARIMA/README.md) | Francesca |
| 23 | ⚡️ World Power Usage ⚡️ - time series forecasting with SVR | [Time series](7-TimeSeries/README.md) | Time series forecasting with Support Vector Regressor | [Python](7-TimeSeries/3-SVR/README.md) | Anirban |
| 24 | Introduction to reinforcement learning | [Reinforcement learning](8-Reinforcement/README.md) | Introduction to reinforcement learning with Q-Learning | [Python](8-Reinforcement/1-QLearning/README.md) | Dmitry |
| 25 | Help Peter avoid the wolf! 🐺 | [Reinforcement learning](8-Reinforcement/README.md) | Reinforcement learning Gym | [Python](8-Reinforcement/2-Gym/README.md) | Dmitry |
| Postscript | Real-World ML scenarios and applications | [ML in the Wild](9-Real-World/README.md) | Interesting and revealing real-world applications of classical ML | [Lesson](9-Real-World/1-Applications/README.md) | Team |
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