Introduction to Machine Learning

Learn the math behind the basic machine learning algorithms and code them yourself in Python.

Goals of this course

1. Have fun
It may come as a surprise, but this is the most important. Beyond the stressful deadlines and exams, it’s essential to approach the material with a playful mindset. Make it a game! The more fun you have, the more you keep at it and the better you become.

2. Learn the math behind Machine Learning’s basic algorithms
Build a solid foundation by learning the key mathematics of ML’s core algorithms.

3. Learn how to write beautiful Python code
In the tutorials, you will (re)implement the algorithms yourself, from scratch. No fancy ML library: it’s about translating the equations and algorithmic steps into clear, self-explanatory code. This is the ultimate hands-on passage to reach mastery. Then the backstage of machine learning algorithms will have no secrets for you!

4. Learn how to ask for help
Requesting technical support is more than a skill: it’s an art! Experts can only help if they understand exactly where you’re stuck. You’ll learn how to narrow down an issue, keep the relevant context while stripping away unnecessary details, provide a minimal working example, and formulate your questions effectively.

5. Learn how to learn (on your own)
How do you become an independent learner? By asking yourself the right questions! This course ends with a STEP (Study Through Exploration and Presentation): a mini-project focusing on an aspect of Machine Learning you want to really master. Get your curiosity to guide you, enjoy the deep dive, and share your findings with crystal-clear explanations. The best way to learn is to teach! On the last day of the course, you’ll present your study and code to your peers, who will learn from it, challenge your understanding with their questions, and help you learn even more.