The Best Machine Learning Courses to Take in 2026

2. Create Machine Learning Models — Microsoft Learn

Microsoft Learn's Create Machine Learning Models path is a strong free vendor option for learners who want a modular, hands-on introduction tied to the Azure ML ecosystem.

• Level: Beginner
• Time: ~10 hours across the path
• Cost: Completely free
• Best for: Engineers in Microsoft-aligned organizations or learners targeting Azure AI Engineer or Data Scientist Associate certifications

The path covers exploring and analyzing data with Python, training and evaluating regression and classification models, training clustering models, and refining and testing deep learning models. Lessons are delivered as short modules with interactive sandboxes — no Azure subscription required to get started. Less language-agnostic than Coursera or Kaggle (the curriculum nudges toward Azure ML SDK by the later modules), but a natural choice for learners who want both an ML grounding and a credential aligned with the Microsoft cloud stack.

3. Intro to Machine Learning — Kaggle Learn

Kaggle's Intro to Machine Learning is a strong free option for learners who want short, project-anchored lessons that lead directly into competition work.

• Level: Beginner
• Time: ~3 hours
• Cost: Completely free
• Best for: Learners who want a fast on-ramp to ML and access to a competition platform to practice on real datasets afterward

The course covers the standard ML workflow in seven concise lessons: how models work, basic data exploration, building your first model with decision trees, model validation, underfitting and overfitting, random forests, and (of course) submitting to a Kaggle competition. Each lesson pairs a short tutorial with a hands-on exercise in a Kaggle Notebook. 

4. Practical Deep Learning for Coders — fast.ai

fast.ai's Practical Deep Learning for Coders is a strong project-first option for learners who want to build working models before working through the theory.

• Level: Intermediate (one year of coding experience required)
• Time: ~20 hours of video across 7 lessons; substantially more for the project work
• Cost: Completely free
• Best for: Developers who already code and want to ship a working deep learning model in their first week, then learn what's underneath

Taught by Jeremy Howard. The course inverts the standard pedagogy: lesson 1 has learners training a state-of-the-art image classifier on their own data before any explanation of what a neural network is. Subsequent lessons gradually peel back layers. The current version uses PyTorch, fastai, Hugging Face Transformers, and Gradio for deployment.

5. Machine Learning Crash Course — Google

Google's Machine Learning Crash Course is a strong free option for learners who want a polished, vendor-authored introduction with interactive widgets that build intuition for how models actually behave.

• Level: Beginner to Intermediate
• Time: ~15 hours across the foundational modules; longer with the advanced and LLM modules
• Cost: Completely free
• Best for: Engineers at any level who want a current, well-paced ML overview, with bonus depth on LLMs and production ML systems

Originally written for Google engineers and now public. The 2024 overhaul significantly expanded the curriculum: the foundational ML modules (linear regression, logistic regression, classification, neural networks, embeddings) now sit alongside advanced modules on production ML systems, generative AI, large language models, and fairness in ML. Each lesson includes interactive Colab notebooks and short visualization widgets that let learners manipulate model behavior.

6. CS229 Machine Learning — Stanford Online

Stanford's CS229 is a strong option for learners who want the full mathematical depth of a graduate machine learning course.

• Level: Advanced (linear algebra, multivariable calculus, probability, and Python required)
• Time: ~20 lectures of ~80 minutes each, plus problem sets
• Cost: Lecture videos free on YouTube; the professional certificate version through Stanford Online costs more
• Best for: Engineers, researchers, and grad students who want rigorous derivations rather than intuition-first explanations

CS229 covers supervised learning (linear models, GLMs, SVMs, kernel methods), unsupervised learning (k-means, EM, PCA, ICA), deep learning, and reinforcement learning, with proofs and derivations throughout. This is a good course for someone who wants to understand why the algorithms work, not just how to use them.

7. 6.036 Introduction to Machine Learning — MIT OpenCourseWare

MIT's 6.036 is a strong free university option for learners who want an undergraduate-rigor introduction to ML with a focus on the algorithms and mathematics behind them.

• Level: Intermediate to Advanced (Python, linear algebra, and basic probability required)
• Time: ~24 lectures plus 12 problem sets and labs
• Cost: Completely free
• Best for: Self-directed learners who want MIT-grade rigor and prefer working through proofs and problem sets rather than building applied projects

The course covers linear classifiers, perceptrons and the perceptron convergence theorem, logistic regression and gradient descent, feature representations and regularization, neural networks and backpropagation, convolutional and recurrent networks, state machines and Markov decision processes, and reinforcement learning. The lectures and problem sets are the actual on-campus MIT course materials. Less polished than commercial offerings.

8. Machine Learning A–Z: Udemy

Kirill Eremenko and Hadelin de Ponteves' Machine Learning A–Z is a strong project-based option on Udemy, with over a million students enrolled.

• Level: Beginner to Intermediate
• Time: ~44 hours of video, plus templates and exercises
• Cost: $15–$85 on Udemy sale
• Best for: Learners who want a single instructor walking them through a wide catalog of ML algorithms with reusable code templates for each

The course covers regression, classification, clustering, association rule learning, reinforcement learning, NLP, deep learning, dimensionality reduction, and model selection — each with a Python and an R implementation, plus a downloadable code template. The breadth means individual topics get less depth than in a focused academic course, but the catalog format is useful for learners who want a "what algorithm do I reach for here" reference. The course is updated periodically.

9. CS50's Introduction to AI with Python — Harvard (edX)

Harvard's CS50AI on edX is a strong option for learners who want machine learning placed in the broader context of artificial intelligence — search, knowledge representation, and reasoning under uncertainty alongside the ML.

• Level: Intermediate (CS50P or equivalent Python experience required)
• Time: ~7 weeks at 10–30 hours per week
• Cost: Free to audit; $239 for an edX verified certificate
• Best for: Learners who want a broader AI grounding — not just ML — from a recognized university

Taught by Brian Yu and David J. Malan. The course covers search algorithms (BFS, DFS, A*, minimax), knowledge representation and propositional logic, probability and Bayesian networks, optimization, machine learning (supervised and reinforcement), neural networks, and natural language processing. Each module is paired with a substantial Python project — a tic-tac-toe AI, a PageRank implementation, a handwriting recognizer, a question-answering system.

Best Machine Learning Courses Comparison Table