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@ -2,7 +2,7 @@
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@ -37,13 +37,13 @@ Travel with us around the world as we apply these classic techniques to data fro
> For further study, we recommend following these [Microsoft Learn](https://docs.microsoft.com/en-us/users/jenlooper-2911/collections/k7o7tg1gp306q4?WT.mc_id=academic-15963-cxa) modules and learning paths.
**Teachers**, we have [included some suggestions](for-teachers.md) on how to use this curriculum.
**Teachers**, we have [included some suggestions](../for-teachers.md) on how to use this curriculum.
---
## Meet the Team
[![Promo video](ml-for-beginners.png)](https://youtu.be/Tj1XWrDSYJU "Promo video")
[![Promo video](../ml-for-beginners.png)](https://youtu.be/Tj1XWrDSYJU "Promo video")
> 🎥 Click the image above for a video about the project and the folks who created it!
@ -54,7 +54,7 @@ We have chosen two pedagogical tenets while building this curriculum: ensuring t
By ensuring that the content aligns with projects, the process is made more engaging for students and retention of concepts will be augmented. In addition, a low-stakes quiz before a class sets the intention of the student towards learning a topic, while a second quiz after class ensures further retention. This curriculum was designed to be flexible and fun and can be taken in whole or in part. The projects start small and become increasingly complex by the end of the 12 week cycle. This curriculum also includes a postscript on real-world applications of ML, which can be used as extra credit or as a basis for discussion.
> Find our [Code of Conduct](CODE_OF_CONDUCT.md), [Contributing](CONTRIBUTING.md), and [Translation](TRANSLATIONS.md) guidelines. We welcome your constructive feedback!
> Find our [Code of Conduct](../CODE_OF_CONDUCT.md), [Contributing](../CONTRIBUTING.md), and [Translation](../TRANSLATIONS.md) guidelines. We welcome your constructive feedback!
## Each lesson includes:
- optional sketchnote
@ -73,42 +73,42 @@ By ensuring that the content aligns with projects, the process is made more enga
| 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 | [lesson](2-Regression/1-Tools/README.md) | Jen |
| 06 | North American pumpkin prices 🎃 | [Regression](2-Regression/README.md) | Visualize and clean data in preparation for ML | [lesson](2-Regression/2-Data/README.md) | Jen |
| 07 | North American pumpkin prices 🎃 | [Regression](2-Regression/README.md) | Build linear and polynomial regression models | [lesson](2-Regression/3-Linear/README.md) | Jen |
| 08 | North American pumpkin prices 🎃 | [Regression](2-Regression/README.md) | Build a logistic regression model | [lesson](2-Regression/4-Logistic/README.md) | Jen |
| 09 | A Web App 🔌 | [Web App](3-Web-App/README.md) | Build a web app to use your trained model | [lesson](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 | [lesson](4-Classification/1-Introduction/README.md) | Jen and Cassie |
| 11 | Delicious Asian and Indian cuisines 🍜 | [Classification](4-Classification/README.md) | Introduction to classifiers | [lesson](4-Classification/2-Classifiers-1/README.md) | Jen and Cassie |
| 12 | Delicious Asian and Indian cuisines 🍜 | [Classification](4-Classification/README.md) | More classifiers | [lesson](4-Classification/3-Classifiers-2/README.md) | Jen and Cassie |
| 13 | Delicious Asian and Indian cuisines 🍜 | [Classification](4-Classification/README.md) | Build a recommender web app using your model | [lesson](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 | [lesson](5-Clustering/1-Visualize/README.md) | Jen |
| 15 | Exploring Nigerian Musical Tastes 🎧 | [Clustering](5-Clustering/README.md) | Explore the K-Means clustering method | [lesson](5-Clustering/2-K-Means/README.md) | Jen |
| 16 | Introduction to natural language processing ☕️ | [Natural language processing](6-NLP/README.md) | Learn the basics about NLP by building a simple bot | [lesson](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 | [lesson](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 | [lesson](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 | [lesson](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 | [lesson](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 | [lesson](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 | [lesson](7-TimeSeries/2-ARIMA/README.md) | Francesca |
| 23 | Introduction to reinforcement learning | [Reinforcement learning](8-Reinforcement/README.md) | Introduction to reinforcement learning with Q-Learning | [lesson](8-Reinforcement/1-QLearning/README.md) | Dmitry |
| 24 | Help Peter avoid the wolf! 🐺 | [Reinforcement learning](8-Reinforcement/README.md) | Reinforcement learning Gym | [lesson](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 |
| 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 | [lesson](../2-Regression/1-Tools/README.md) | Jen |
| 06 | North American pumpkin prices 🎃 | [Regression](../2-Regression/README.md) | Visualize and clean data in preparation for ML | [lesson](../2-Regression/2-Data/README.md) | Jen |
| 07 | North American pumpkin prices 🎃 | [Regression](../2-Regression/README.md) | Build linear and polynomial regression models | [lesson](../2-Regression/3-Linear/README.md) | Jen |
| 08 | North American pumpkin prices 🎃 | [Regression](../2-Regression/README.md) | Build a logistic regression model | [lesson](../2-Regression/4-Logistic/README.md) | Jen |
| 09 | A Web App 🔌 | [Web App](../3-Web-App/README.md) | Build a web app to use your trained model | [lesson](../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 | [lesson](../4-Classification/1-Introduction/README.md) | Jen and Cassie |
| 11 | Delicious Asian and Indian cuisines 🍜 | [Classification](../4-Classification/README.md) | Introduction to classifiers | [lesson](../4-Classification/2-Classifiers-1/README.md) | Jen and Cassie |
| 12 | Delicious Asian and Indian cuisines 🍜 | [Classification](../4-Classification/README.md) | More classifiers | [lesson](../4-Classification/3-Classifiers-2/README.md) | Jen and Cassie |
| 13 | Delicious Asian and Indian cuisines 🍜 | [Classification](../4-Classification/README.md) | Build a recommender web app using your model | [lesson](../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 | [lesson](../5-Clustering/1-Visualize/README.md) | Jen |
| 15 | Exploring Nigerian Musical Tastes 🎧 | [Clustering](../5-Clustering/README.md) | Explore the K-Means clustering method | [lesson](../5-Clustering/2-K-Means/README.md) | Jen |
| 16 | Introduction to natural language processing ☕️ | [Natural language processing](../6-NLP/README.md) | Learn the basics about NLP by building a simple bot | [lesson](../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 | [lesson](../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 | [lesson](../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 | [lesson](../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 | [lesson](../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 | [lesson](../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 | [lesson](../7-TimeSeries/2-ARIMA/README.md) | Francesca |
| 23 | Introduction to reinforcement learning | [Reinforcement learning](../8-Reinforcement/README.md) | Introduction to reinforcement learning with Q-Learning | [lesson](../8-Reinforcement/1-QLearning/README.md) | Dmitry |
| 24 | Help Peter avoid the wolf! 🐺 | [Reinforcement learning](../8-Reinforcement/README.md) | Reinforcement learning Gym | [lesson](../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 |
## Offline access
You can run this documentation offline by using [Docsify](https://docsify.js.org/#/). Fork this repo, [install Docsify](https://docsify.js.org/#/quickstart) on your local machine, and then in the root folder of this repo, type `docsify serve`. The website will be served on port 3000 on your localhost: `localhost:3000`.
## PDFs
Find a pdf of the curriculum with links [here](pdf/readme.pdf)
Find a pdf of the curriculum with links [here](../pdf/readme.pdf)
## Help Wanted!
Would you like to contribute a translation? Please read our [translation guidelines](TRANSLATIONS.md) and add input [here](https://github.com/microsoft/ML-For-Beginners/issues/71)
Would you like to contribute a translation? Please read our [translation guidelines](../TRANSLATIONS.md) and add input [here](https://github.com/microsoft/ML-For-Beginners/issues/71)
## Other Curricula