5 Free AI Courses From the Best Universities in t…
These are the five free courses I'd actually use to learn AI from the ground up, all from schools like Stanford, Harvard, MIT and DeepMind, and every one is genuinely free. They go in a rough order, from your first ML course to computer vision and reinforcement learning.
Link: https://www.youtube.com/playlist?list=PLoROMvodv4rMiGQp3WXShtMGgzqpfVfbU (Andrew Ng's full CS229 lectures; notes and problem sets at cs229.stanford.edu)
Description: The machine learning course, taught at Stanford and originally by Andrew Ng. You go from linear and logistic regression up to SVMs, neural networks and learning theory, and you derive the math yourself instead of just importing a library. It is heavier than most intro courses and that is exactly why it pays off. Lectures are free on YouTube and all notes and problem sets are on the course site.
Link: https://pll.harvard.edu/course/cs50s-introduction-artificial-intelligence-python
Description: The friendliest way into real AI. Harvard's CS50 AI covers the algorithms behind game engines, search, optimization and early machine learning, all in Python. You actually build things like a minimax game agent and a maze solver rather than just reading about them. The course is completely free, only the optional certificate costs money.
Link: https://introtodeeplearning.com/
Description: MIT's fast, modern crash course in deep learning, re-recorded every year so it never goes stale. It takes you from a single neuron to CNNs, transformers and generative AI in about a week of lectures, with hands-on labs in Colab. A great second step once you have the basics. Everything is free on the site and YouTube.
Link: https://cs231n.stanford.edu/
Description: The legendary computer vision course, originally built by Fei-Fei Li and Andrej Karpathy. You build convolutional networks from scratch and understand every layer instead of just calling model.fit. It is best saved for once you are comfortable with the basics of machine learning. The notes are free at cs231n.github.io and the lectures are on YouTube.
Link: https://www.youtube.com/playlist?list=PLqYmG7hTraZBKeNJ-JE_eyJHZ7XgBoAyb
Description: The reinforcement learning course, taught by David Silver, the researcher behind AlphaGo. This DeepMind and UCL series takes you from Markov decision processes to Q-learning and policy gradients, the ideas behind agents that learn by trial and error. RL feels like a locked door most people never open, and this is the key. The full lecture series is free on YouTube.
My advice: pick the one that matches where you want to go and actually finish it. One of these done properly beats ten started.
10 AI & ML BOOKS You Should Read in 2026
Author: Sebastian Raschka
What You’ll Learn: Learn how large language models work by building one from the ground up. The book covers tokenization, attention mechanisms, transformer architecture, model training, fine-tuning, and text generation.
Author: Chip Huyen
What You’ll Learn: Learn how to build real-world AI applications using foundation models. It covers model selection, evaluation, prompt engineering, RAG, fine-tuning, and deploying reliable AI systems.
Authors: Jay Alammar & Maarten Grootendorst
What You’ll Learn: Understand the core concepts behind modern LLMs through practical examples and visual explanations. Explore transformers, embeddings, text generation, semantic search, and building LLM-powered applications.
Author: Aurélien Géron
What You’ll Learn: Learn how to build machine learning and deep learning models with Python. The book covers regression, classification, neural networks, transformers, model training, and practical ML workflows using popular frameworks.
Author: Chip Huyen
What You’ll Learn: Learn how to turn machine learning models into reliable production systems. Explore data pipelines, model deployment, monitoring, scalability, distribution shifts, and maintaining ML systems in the real world.
Author: Andriy Burkov
What You’ll Learn: Get a concise overview of essential machine learning concepts, including supervised and unsupervised learning, popular algorithms, model evaluation, and practical techniques for solving ML problems.
Authors: Peter Bruce, Andrew Bruce & Peter Gedeck
What You’ll Learn: Build the statistical foundation needed for data science and machine learning. Explore probability, sampling, regression, classification, statistical testing, and techniques for analyzing real-world data.
Author: Hala Nelson
What You’ll Learn: Understand the mathematics that powers modern AI systems. Learn key concepts from linear algebra, calculus, probability, statistics, and optimization and see how they connect to machine learning.
Authors: Paul Iusztin & Maxime Labonne
What You’ll Learn: Learn how to take large language models from development to production. Explore fine-tuning, evaluation, RAG, deployment, monitoring, and practical workflows for building reliable LLM applications.
Author: David Foster
What You’ll Learn: Discover how generative AI models create new content. Explore techniques behind image generation, text generation, transformers, VAEs, GANs, and other deep learning approaches used in generative AI.