7 free online courses for learning machine learning in 2025

Machine Learning Crash Course by Google
A free course from Google that combines theory with interactive exercises. It explains the basics of linear and logistic regression, classification, and overfitting through simple examples. A great starting point for those who want to quickly dive into ML and immediately see the results of their work.

Machine Learning Specialization by DeepLearning.AI
An updated version of Andrew Ng's classic course. It explains basic algorithms such as regression and decision trees and helps apply them to real-world problems. Suitable for those who want to confidently master practical ML from the very beginning.

Data Science: Building Machine Learning Models by Harvard University
This course introduces ML in the context of data analysis. You will learn how to build and evaluate models, work with large datasets, and apply machine learning methods in real projects. Everything is explained clearly and without excessive theory.

Machine Learning Courses by DataCamp
DataCamp offers dozens of short ML courses, ranging from introductory topics to advanced models. The materials are interactive: you write code directly in the browser and immediately see the results. The "learn by doing" format is ideal for those who dislike dry theory.

Machine Learning by edX
edX offers courses from MIT, Microsoft, and other leading universities. You can choose your focus—theory, Python practice, or specialized models. A great option if you want to learn from the best and earn internationally recognized certificates.

Intro to Machine Learning by Kaggle
A fast-paced and practical course that introduces you to the basics of ML in just a few hours. There is no complex theory here—only interactive exercises and clear examples directly in the browser. A great way to try out machine learning in practice without unnecessary stress.

Introduction to Computational Thinking by OpenLearn
This course teaches you to think like a developer: breaking down problems, identifying patterns, and building algorithms. In the section on machine learning, it explains how computers learn without explicit instructions. Suitable for those who want to understand the logic of ML before studying the models themselves.