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 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 forthcoming '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, Ornella Altunyan, 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

Getting Started

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:

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
After completing a lesson group, visit the Discussion board and "learn out loud" by filling out the appropriate PAT rubric. A 'PAT' is a Progress Assessment Tool that is a rubric you fill out to further your learning. You can also react to other PATs so we can learn together.

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.

Meet the Team

🎥 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. 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, 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 50 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	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	lesson	Jen
06	North American pumpkin prices 🎃	Regression	Visualize and clean data in preparation for ML	lesson	Jen
07	North American pumpkin prices 🎃	Regression	Build linear and polynomial regression models	lesson	Jen
08	North American pumpkin prices 🎃	Regression	Build a logistic regression model	lesson	Jen
09	A Web App 🔌	Web App	Build a web app to use your trained model	lesson	Jen
10	Introduction to classification	Classification	Clean, prep, and visualize your data; introduction to classification	lesson	Jen and Cassie
11	Delicious Asian and Indian cuisines 🍜	Classification	Introduction to classifiers	lesson	Jen and Cassie
12	Delicious Asian and Indian cuisines 🍜	Classification	More classifiers	lesson	Jen and Cassie
13	Delicious Asian and Indian cuisines 🍜	Classification	Build a recommender web app using your model	lesson	Jen
14	Introduction to clustering	Clustering	Clean, prep, and visualize your data; Introduction to clustering	lesson	Jen
15	Exploring Nigerian Musical Tastes 🎧	Clustering	Explore the K-Means clustering method	lesson	Jen
16	Introduction to natural language processing ☕️	Natural language processing	Learn the basics about NLP by building a simple bot	lesson	Stephen
17	Common NLP Tasks ☕️	Natural language processing	Deepen your NLP knowledge by understanding common tasks required when dealing with language structures	lesson	Stephen
18	Translation and sentiment analysis ♥️	Natural language processing	Translation and sentiment analysis with Jane Austen	lesson	Stephen
19	Romantic hotels of Europe ♥️	Natural language processing	Sentiment analysis with hotel reviews, 1	lesson	Stephen
20	Romantic hotels of Europe ♥️	Natural language processing	Sentiment analysis with hotel reviews 2	lesson	Stephen
21	Introduction to time series forecasting	Time series	Introduction to time series forecasting	lesson	Francesca
22	⚡️ World Power Usage ⚡️ - time series forecasting with ARIMA	Time series	Time series forecasting with ARIMA	lesson	Francesca
23	Introduction to reinforcement learning	Reinforcement learning	Introduction to reinforcement learning with Q-Learning	lesson	Dmitry
24	Help Peter avoid the wolf! 🐺	Reinforcement learning	Reinforcement learning Gym	lesson	Dmitry
Postscript	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.

PDFs

Find a pdf of the curriculum with links here

Help Wanted!

Would you like to contribute a translation? Please read our translation guidelines and add input here

Other Curricula

Our team produces other curricula! Check out:

Web Dev for Beginners

Azure Cloud Advocates at Microsoft are pleased to offer a 12-week, 24-lesson curriculum all about Machine Learning

Related tags

Overview

Machine Learning for Beginners - A Curriculum

Getting Started

Meet the Team

Pedagogy

Each lesson includes:

Offline access

PDFs

Help Wanted!

Other Curricula

Owner

Microsoft

LinearRegression2 Tvads and CarSales

neurodsp is a collection of approaches for applying digital signal processing to neural time series

TIANCHI Purchase Redemption Forecast Challenge

2021 Machine Learning Security Evasion Competition

Gaussian Process Optimization using GPy

Book Recommender System Using Sci-kit learn N-neighbours

Project to deploy a machine learning model based on Titanic dataset from Kaggle

Implementation of different ML Algorithms from scratch, written in Python 3.x

Temporal Alignment Prediction for Supervised Representation Learning and Few-Shot Sequence Classification

Python package for stacking (machine learning technique)

Confidence intervals for scikit-learn forest algorithms

A Python implementation of FastDTW

A statistical library designed to fill the void in Python's time series analysis capabilities, including the equivalent of R's auto.arima function.

A pure-python implementation of the UpSet suite of visualisation methods by Lex, Gehlenborg et al.

[HELP REQUESTED] Generalized Additive Models in Python

Programming assignments and quizzes from all courses within the Machine Learning Engineering for Production (MLOps) specialization offered by deeplearning.ai

MachineLearningStocks is designed to be an intuitive and highly extensible template project applying machine learning to making stock predictions.

Time Series Prediction with tf.contrib.timeseries

Apple-voice-recognition - Machine Learning

XGBoost + Optuna