|Project Name||Stars||Downloads||Repos Using This||Packages Using This||Most Recent Commit||Total Releases||Latest Release||Open Issues||License||Language|
|Keras||59,447||578||6 hours ago||80||June 27, 2023||100||apache-2.0||Python|
|Deep Learning for humans|
|Scikit Learn||55,996||18,944||9,755||5 hours ago||71||June 30, 2023||2,260||bsd-3-clause||Python|
|scikit-learn: machine learning in Python|
|Ml For Beginners||53,637||7 days ago||7||mit||HTML|
|12 weeks, 26 lessons, 52 quizzes, classic Machine Learning for all|
|Made With Ml||34,217||4 days ago||5||May 15, 2019||4||mit||Jupyter Notebook|
|Learn how to design, develop, deploy and iterate on production-grade ML applications.|
|Ray||27,966||80||298||8 hours ago||87||July 24, 2023||3,451||apache-2.0||Python|
|Ray is a unified framework for scaling AI and Python applications. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.|
|Streamlit||27,572||17||898||6 hours ago||204||July 20, 2023||671||apache-2.0||Python|
|Streamlit — A faster way to build and share data apps.|
|Spacy||27,244||1,533||1,198||7 hours ago||222||July 07, 2023||94||mit||Python|
|💫 Industrial-strength Natural Language Processing (NLP) in Python|
|Data Science Ipython Notebooks||25,242||3 months ago||34||other||Python|
|Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines.|
|Lightning||24,740||7||620||6 hours ago||253||July 25, 2023||688||apache-2.0||Python|
|Deep learning framework to train, deploy, and ship AI products Lightning fast.|
|Applied Ml||24,714||23 days ago||3||mit|
|📚 Papers & tech blogs by companies sharing their work on data science & machine learning in production.|
Travel around the world as we explore Machine Learning by means of world cultures
Azure Cloud Advocates at Microsoft are pleased to offer a 12-week, 26-lesson curriculum all about Machine Learning. In this curriculum, you will learn about what is sometimes called classic machine learning, using primarily Scikit-learn as a library and avoiding deep learning, which is covered in our forthcoming 'AI for Beginners' curriculum. Pair these lessons with our 'Data Science for Beginners' curriculum, as well!
Travel with us around the world as we apply these classic techniques to data from many areas of the world. Each lesson includes pre- and post-lesson quizzes, written instructions to complete the lesson, a solution, an assignment, and more. Our project-based pedagogy allows you to learn while building, a proven way for new skills to 'stick'.
** Hearty thanks to our authors** Jen Looper, Stephen Howell, Francesca Lazzeri, Tomomi Imura, Cassie Breviu, Dmitry Soshnikov, Chris Noring, Anirban Mukherjee, Ornella Altunyan, Ruth Yakubu and Amy Boyd
** Thanks as well to our illustrators** Tomomi Imura, Dasani Madipalli, and Jen Looper
** Special thanks to our Microsoft Student Ambassador authors, reviewers, and content contributors**, notably Rishit Dagli, Muhammad Sakib Khan Inan, Rohan Raj, Alexandru Petrescu, Abhishek Jaiswal, Nawrin Tabassum, Ioan Samuila, and Snigdha Agarwal
** Extra gratitude to Microsoft Student Ambassadors Eric Wanjau, Jasleen Sondhi, and Vidushi Gupta for our R lessons!**
Students, to use this curriculum, fork the entire repo to your own GitHub account and complete the exercises on your own or with a group:
/solutionfolders in each project-oriented lesson.
For further study, we recommend following these Microsoft Learn modules and learning paths.
Teachers, we have included some suggestions on how to use this curriculum.
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 by clicking the image below.
Gif by Mohit Jaisal
Click the image above for a video about the project and the folks who created it!
We have chosen two pedagogical tenets while building this curriculum: ensuring that it is hands-on project-based and that it includes frequent quizzes. In addition, this curriculum has a common theme to give it cohesion.
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.
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
/solutionfolder 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 this 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
|Lesson Number||Topic||Lesson Grouping||Learning Objectives||Linked Lesson||Author|
|01||Introduction to machine learning||Introduction||Learn the basic concepts behind machine learning||Lesson||Muhammad|
|02||The History of machine learning||Introduction||Learn the history underlying this field||Lesson||Jen and Amy|
|03||Fairness and machine learning||Introduction||What are the important philosophical issues around fairness that students should consider when building and applying ML models?||Lesson||Tomomi|
|04||Techniques for machine learning||Introduction||What techniques do ML researchers use to build ML models?||Lesson||Chris and Jen|
|05||Introduction to regression||Regression||Get started with Python and Scikit-learn for regression models||
|06||North American pumpkin prices||Regression||Visualize and clean data in preparation for ML||
|07||North American pumpkin prices||Regression||Build linear and polynomial regression models||
|08||North American pumpkin prices||Regression||Build a logistic regression model||
|09||A Web App||Web App||Build a web app to use your trained model||Python||Jen|
|10||Introduction to classification||Classification||Clean, prep, and visualize your data; introduction to classification||
|11||Delicious Asian and Indian cuisines||Classification||Introduction to classifiers||
|12||Delicious Asian and Indian cuisines||Classification||More classifiers||
|13||Delicious Asian and Indian cuisines||Classification||Build a recommender web app using your model||Python||Jen|
|14||Introduction to clustering||Clustering||Clean, prep, and visualize your data; Introduction to clustering||
|15||Exploring Nigerian Musical Tastes||Clustering||Explore the K-Means clustering method||
|16||Introduction to natural language processing||Natural language processing||Learn the basics about NLP by building a simple bot||Python||Stephen|
|17||Common NLP Tasks||Natural language processing||Deepen your NLP knowledge by understanding common tasks required when dealing with language structures||Python||Stephen|
|18||Translation and sentiment analysis||Natural language processing||Translation and sentiment analysis with Jane Austen||Python||Stephen|
|19||Romantic hotels of Europe||Natural language processing||Sentiment analysis with hotel reviews 1||Python||Stephen|
|20||Romantic hotels of Europe||Natural language processing||Sentiment analysis with hotel reviews 2||Python||Stephen|
|21||Introduction to time series forecasting||Time series||Introduction to time series forecasting||Python||Francesca|
|22||World Power Usage - time series forecasting with ARIMA||Time series||Time series forecasting with ARIMA||Python||Francesca|
|23||World Power Usage - time series forecasting with SVR||Time series||Time series forecasting with Support Vector Regressor||Python||Anirban|
|24||Introduction to reinforcement learning||Reinforcement learning||Introduction to reinforcement learning with Q-Learning||Python||Dmitry|
|25||Help Peter avoid the wolf!||Reinforcement learning||Reinforcement learning Gym||Python||Dmitry|
|Postscript||Real-World ML scenarios and applications||ML in the Wild||Interesting and revealing real-world applications of classical ML||Lesson||Team|
|Postscript||Model Debugging in ML using RAI dashboard||ML in the Wild||Model Debugging in Machine Learning using Responsible AI dashboard components||Lesson||Ruth Yakubu|
You can run this documentation offline by using Docsify. Fork this repo, install Docsify 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:
Find a pdf of the curriculum with links here.
Our team produces other curricula! Check out: