These five no-cost courses make a useful learning path, but none alone can guarantee mastery. Start with Google’s short orientation, build fundamentals with Google and Kaggle, then choose a deeper applied course when you are ready. “Free” here refers to access to the reviewed course materials; it does not establish that a free certificate is available.
Which free machine-learning course should you take first?
For a complete beginner, follow Google’s foundational sequence into guided practice: take Introduction to Machine Learning, then Google’s Machine Learning Crash Course, followed by Kaggle’s introductory machine-learning lessons. Once you understand basic modeling, move on to Kaggle’s deep-learning introduction. If you already know how to code, you can progress from fundamentals to fast.ai’s broader, project-oriented course.
The courses serve different purposes and are not interchangeable. Google emphasizes sequenced concepts and interactive exercises; Kaggle offers concise, practical lessons; fast.ai expects coding experience and applies deep-learning techniques to projects.
Compare the five courses
| Course | Starting skill | Time commitment | Learning mode and scope |
|---|---|---|---|
| Google: Introduction to Machine Learning | Beginner-friendly orientation | Brief; a specific duration is not stated on the cited page | Entry point in Google’s foundational sequence; introductory rather than comprehensive. |
| Google: Machine Learning Crash Course | Newcomers should follow the modules in order; experienced learners can skip to self-contained modules | A specific duration is not stated on the cited page | Videos, interactive visualizations, and exercises across core ML concepts and selected current applications. |
| Kaggle Learn: Intro to Machine Learning | Those seeking initial modeling familiarity | A specific duration is not stated on the cited catalog page | Concise lessons and practical exercises; not a comprehensive theory course. |
| Kaggle Learn: Intro to Deep Learning | Learners ready to begin neural networks | Kaggle estimates four hours | Short TensorFlow and Keras course with practical exercises and neural-network fundamentals. |
| fast.ai: Practical Deep Learning for Coders | People with coding experience, preferably Python, and at least high-school mathematics | Nine lessons, each around 90 minutes according to fast.ai | Applied, project-oriented deep learning across computer vision, NLP, tabular data, collaborative filtering, random forests, regression, and deployment. |
1. Google: Introduction to Machine Learning
Google’s foundational course sequence begins with this brief introduction. It is a good first stop if machine learning is new to you and you want an orientation before tackling a longer course. Google recommends taking its foundational offerings in order, so this course leads naturally into the Machine Learning Crash Course.
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Use it to get started, not as a complete curriculum: its role in the path is to prepare you for more substantial study.
2. Google: Machine Learning Crash Course
Google’s Machine Learning Crash Course (MLCC) is the more structured fundamentals course in this set. Its materials include videos, interactive visualizations, and exercises. Topics span regression and classification, data representation, overfitting, neural networks, embeddings, introductory large language model concepts, production machine learning, AutoML, and fairness.
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Google advises newcomers to work through the modules in order. If you already have relevant experience, you can instead select individual self-contained modules. The course’s breadth makes it a stronger fundamentals step than a short orientation, though a list of topics is not the same as mastery of each one.
3. Kaggle Learn: Intro to Machine Learning
Kaggle’s Learn catalog lists its Intro to Machine Learning among its no-cost courses. This is a practical choice for building familiarity with modeling through guided lessons and exercises. Treat it as concise practice alongside a broader fundamentals course, not as a replacement for deeper conceptual study.
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4. Kaggle Learn: Intro to Deep Learning
Kaggle’s Intro to Deep Learning is a short next step once you are ready to work with neural networks. It uses TensorFlow and Keras and introduces neurons and deeper networks, stochastic gradient descent, overfitting, dropout, batch normalization, and binary classification. Kaggle estimates that the course takes four hours.
That estimate makes it a manageable introduction, not a claim that four hours is enough to become proficient in deep learning. Continue by implementing models, studying their behavior, and taking on projects beyond the guided lessons.
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5. fast.ai: Practical Deep Learning for Coders
fast.ai’s Practical Deep Learning for Coders is the deeper applied option for learners who already know how to code. fast.ai describes nine lessons of around 90 minutes each. The course uses projects to cover computer vision, natural-language processing, tabular work, collaborative filtering, random forests, regression, and model deployment.
It expects coding experience, preferably in Python, and at least high-school mathematics. fast.ai says it teaches the calculus and linear algebra needed along the way. It also describes free computing options and says special hardware is unnecessary.
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fast.ai reproduces a testimonial about its companion book from Google Director of Research Peter Norvig, opening with the phrase “Deep Learning is for everyone.” That is a testimonial about the book, not an independent assessment or a promise that every learner can take the course without preparation. The companion book, Deep Learning for Coders with fastai and PyTorch, is optional; fast.ai says it is freely available online, and purchasing a copy is not necessary to take the course.
A practical order for learning
- Get oriented: Take Google’s Introduction to Machine Learning.
- Build a foundation: Work through Google’s MLCC modules in order if you are new to the subject.
- Practice modeling: Add Kaggle’s Intro to Machine Learning for concise, guided exercises.
- Try neural networks: Take Kaggle’s Intro to Deep Learning when you are ready for TensorFlow and Keras fundamentals.
- Go deeper through projects: Choose fast.ai if you can code and want an applied course spanning several kinds of machine-learning work.
The two Kaggle courses are intentionally short introductions. Use them to practice and orient yourself, then build further through projects and study rather than treating completion as a finish line.
Where Stanford CS229 fits—and where it does not
Stanford’s Summer 2026 CS229 course page is a useful comparison for readers seeking a more mathematically demanding university course, but it is not one of the five open recommendations. The course covers supervised and unsupervised learning, learning theory, and reinforcement learning. Its stated preparation includes Python/NumPy programming, probability, multivariable calculus, and linear algebra at specified university-course equivalents. The current page says course documents are shared only with Stanford affiliates, so it should not be presented as a set of freely available materials for everyone.
What “free” means for these courses
The cited course pages and Kaggle catalog support no-cost access to the reviewed learning materials, but they do not establish the availability or terms of free certificates or credentials. Check each provider’s current terms if a credential matters to you.
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For additional context, Google’s MLCC page says “millions” have relied on it since 2018 but gives no exact count or publication year for that claim. It does not provide a precise, independently useful measure of learner outcomes, so course popularity should not be read as evidence of completion, performance, or job readiness.
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