7 free Machine Learning courses: where to study in 2025

In this review, we have compiled a selection of the best machine learning courses in 2025 to help you dive into the world of artificial intelligence.

Machine Learning from Stanford University
A legendary course by a Stanford University professor, which introduced millions of students around the world to AI. The program covers the fundamentals of learning algorithms, linear and logistic regression, neural networks, and SVMs. Duration: two months.

Applied Machine Learning from Cornell University
An open online course covering key machine learning algorithms, including linear regression, neural networks, and kernel-based methods, as well as their mathematical foundations and implementation. The course includes 23 lectures with detailed notes, over 30 hours of video materials, and practical implementations of more than 20 algorithms in Python.

IBM Professional Certificate in Machine Learning
An online program consisting of six courses that prepares specialists for work in the field of machine learning. Over three months, participants will master key algorithms, including regression, classification, and clustering, learn to develop recommendation systems in Python, work with KNN and PCA, and apply deep learning to real-world tasks. The course includes practical labs, a final project, and provides an IBM certificate that can be added to your LinkedIn profile.

Machine Learning for Everybody from freeCodeCamp
An accessible introduction to machine learning for beginners. It explains key ML concepts such as classification, regression, neural networks, and clustering, and demonstrates working with TensorFlow for practical application. The course includes 27 exercises, 12 theoretical modules, and real-world examples, helping you master basic algorithms step by step. 

Google's Machine Learning Crash Course
A practical introduction to ML, including 12 modules with video lectures, interactive visualizations, and real-world examples. It covers key algorithms, data handling, neural network models, and principles of deploying ML systems. The course is aimed at both beginners and those who want to study specific topics. The updated version places greater emphasis on interactive learning and the latest achievements in artificial intelligence.

"Introduction to Data Science and Machine Learning" on Stepik
The course is designed for beginners who want to master the basic concepts of machine learning. Participants will become familiar with key algorithms such as decision trees and neural networks, as well as learn to work with popular data analysis tools—Pandas and Scikit-learn in Python. The course includes lectures, practical assignments, and discussions of current topics such as the future of artificial intelligence and careers in Data Science. A Stepik certificate is issued upon completion.

An Introduction to Applied Machine Learning in Python from CodeFatherTech
An overview guide to applied machine learning, explaining how to use ML algorithms to solve real-world problems. The course covers key concepts such as classification, regression, neural networks, and data processing, and demonstrates examples of ML application in voice assistants, recommendation systems, and user behavior analysis.