Machine Learning for Beginners - A Curriculum

🌍 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, 24-lesson curriculum all about traditional Machine Learning. In this lesson group, you will learn about what is sometimes called 'classic' ML, using primarily Scikit-Learn as a library and avoiding deep learning, which is covered in our forthcoming 'AI for Beginners' curriculum.

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 Soshkinov, Amy Boyd, Ornella Altunyan

🙏Special thanks🙏 to our Microsoft Student Ambassador reviewers and content contributors, notably Rishit Dagli, Rohan Raj, Muhammad Sakib Khan Inan, Alexandru Petrescu, Abhishek Jaiswal, Nawrin Tabassum, Ioan Samuila, and Snigdha Agarwal

Getting Started

Students, to use this curriculum, fork the entire repo to your own GitHub account and complete the exercises on your own:

• Start with a pre-lecture quiz
• Read the lecture and complete the activities, pausing and reflecting at each knowledge check.
• Try to create the projects by comprehending the lessons rather than running the solution code; however that code is available in the /solution folders in each project-oriented lesson.
• Take the post-lecture quiz
• Complete the challenge
• Complete the assignment
• Consider forming a study group with friends to go through the content together.
• For further study, we recommend following Microsoft Learn modules and learning paths.

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Teachers, we have included some suggestions on how to use this curriculum.

Future space for Promo Video

🎥 Click the image above for a video about the project and the folks who created it!

Pedagogy

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.

Find our Code of Conduct, Contributing, and Translation guidelines. We welcome your constructive feedback!

Each lesson includes:

• optional sketchnote
• optional supplemental video
• pre-lecture warmup quiz
• written lesson
• for project-based lessons, step-by-step guides on how to build the project
• knowledge checks
• a challenge
• supplemental reading
• assignment
• post-lecture quiz

A note about quizzes: All quizzes are contained in this app, for 48 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	Section	Concepts Taught	Learning Objectives	Linked Lesson	Author
01	Introduction	Introduction to Machine Learning	Learn the basic concepts behind Machine Learning	lesson	Team
02	Introduction	The History of Machine Learning	Learn the history underlying this field	lesson	Jen and Amy
03	Introduction	Fairness and Machine Learning	What are the important philosophical issues around fairness that students should consider when building and applying ML models?	lesson	Tomomi
04	Introduction to Regression	Regression	Get started with Python and Scikit-Learn for Regression models	lesson	Jen
05	North American Pumpkin Prices 🎃	Regression	Visualize and clean data in preparation for ML	lesson	Jen
06	North American Pumpkin Prices 🎃	Regression	Build Linear and Polynomial Regression models	lesson	Jen
07	North American Pumpkin Prices 🎃	Regression	Build a Logistic Regression model	lesson	Jen
08	A Web App 🔌	Web App	Build a Web app to use your trained model	lesson	Jen
09	Introduction to Classification	Classification	Clean, Prep, and Visualize your Data; Introduction to Classification	lesson	Cassie
10	Delicious Asian and Indian Recipes 🍜	Classification	Build a Discriminative Model	lesson	Cassie
11	Delicious Asian and Indian Recipes 🍜	Classification	Build a Generative Model	lesson	Cassie
12	Delicious Asian and Indian Recipes 🍜	Classification	Build a Web App using your Model	lesson	Jen
13	Introduction to Clustering	Clustering	Clean, Prep, and Visualize your Data; Introduction to Clustering	lesson	Jen
14	Exploring Nigerian Musical Tastes 🎧	Clustering	Explore the K-Means Clustering Method	lesson	Jen
15	Introduction to Natural Language Processing ☕️	Natural Language Processing	Learn the basics about NLP by building a simple bot	lesson	Stephen
16	Common NLP Tasks ☕️	Natural Language Processing	Deepen your NLP knowledge by understanding common tasks required when dealing with language structures	lesson	Stephen
17	Translation and Sentiment Analysis ❤️	Natural Language Processing	Translation and Sentiment analysis with Jane Austen	lesson	Stephen
18	Romantic Hotels of Europe ♥️	Natural Language Processing	Sentiment analysis, continued	lesson	Stephen
19	Romantic Hotels of Europe ♥️	Natural Language Processing	Sentiment analysis, continued	lesson	Stephen
20	Introduction to Time Series Forecasting	Time Series	Introduction to Time Series Forecasting	lesson	Francesca
21	⚡️ World Power Usage ⚡️ Time Series Forecasting with ARIMA ⚡️	Time Series	Time Series Forecasting with ARIMA	lesson	Francesca
22	Introduction to Reinforcement Learning	Reinforcement Learning	tbd	lesson	Dmitry
23	Help Peter avoid the Wolf! 🐺	Reinforcement Learning	tbd	lesson	Dmitry
24	Real-World ML Scenarios and Applications	ML in the Wild	Interesting and Revealing real-world applications of classical ML	lesson	Team

Offline access

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: localhost:3000.