The best AI courses to start with in 2026 range from no-code introductions such as Elements of AI to implementation-heavy classes such as Stanford’s CS336. This list covers ten courses, each described the way its official page presents it on October 26, 2026: provider, level, price, length and what you learn. Where a page does not state a price or duration, we say so rather than guess.
How to choose
Start from what you can already do. If you have no programming background, begin with a conceptual course. If you can write Python, pick a hands-on course that has you train models. If you want to build products on language models, go straight to the LLM-focused entries. Courses are ordered roughly from easiest entry point to most demanding.
Pricing and catalogs change, so confirm cost and requirements on the official page before you commit time. For a staged plan that links several resources together, see our free learning path to understand AI.
1. Elements of AI
Created by MinnaLearn and the University of Helsinki, Elements of AI is a free online course in two parts: Introduction to AI and Building AI. The first part explains what AI is, what it can and cannot do and how it affects daily life, and needs no advanced math or programming. The second part covers the algorithms used to build AI methods and recommends basic Python. The page gives no total duration; you work at your own pace. It mentions a separate 60-minute “Get AI Ready” program, but does not state whether the main courses award a certificate.
2. Machine Learning Crash Course (Google)
Google’s Machine Learning Crash Course combines animated videos, interactive visualizations and hands-on exercises that run in the browser using Colaboratory, with no setup. Topics include linear and logistic regression, classification, numerical and categorical data, overfitting, neural networks, embeddings, an introduction to large language models, production ML systems, AutoML and fairness. The prerequisites page expects good programming skills, ideally Python, and comfort with basic statistics and algebra. It lists per-module time estimates that add up to about 13.5 hours by our sum. The pages we read do not mention a cost.
3. Generative AI for Beginners (Microsoft)
Microsoft Cloud Advocates publish this 21-lesson course as an MIT-licensed GitHub repository. Lessons are labeled “Learn” for concepts or “Build” for concepts plus code, and cover prompt engineering, text and image generation apps, embeddings and vector search, function calling, RAG, agents and fine-tuning. Code samples use Python and TypeScript, and the written material has translations in more than 50 languages. You need a GitHub account and access to a model service such as the OpenAI API or Foundry Local; any usage charges depend on the service you pick, and the page lists no course price.
4. CS50’s Introduction to Artificial Intelligence with Python (Harvard)
Taught by Brian Yu and David J. Malan, this course runs seven weeks of lectures and projects: search, knowledge, uncertainty, optimization, learning, neural networks and language. Applications include game-playing engines, handwriting recognition and machine translation. The prerequisite is CS50x or at least one year of Python. You can take it free through the OpenCourseWare materials; the page also points to edX verified and professional certificates and Harvard Extension or Summer School credit, without giving their costs.
5. Practical Deep Learning for Coders (fast.ai)
Jeremy Howard’s free course, shown on the page as the 2022 Part 1 edition, has nine lessons of roughly 90 minutes each. You build models for computer vision, NLP, tabular data and collaborative filtering, deploy them as web apps, and implement stochastic gradient descent and a full training loop from scratch, using PyTorch, fastai and Hugging Face Transformers. The page says about a year of coding experience, preferably Python, plus high school math is enough, and no special hardware is needed.
6. Neural Networks: Zero to Hero (Andrej Karpathy)
This video series teaches you to build neural networks in code, starting with backpropagation and ending with GPT-style language models. The eight listed lectures run from about 56 minutes to over two hours each: micrograd, several makemore parts, “Let’s build GPT” and “Let’s build the GPT Tokenizer”. The page asks for solid Python and introductory math such as derivatives, notes a community Discord, and does not state a price. It calls the course ongoing.
7. The Hugging Face LLM Course
Hugging Face describes its course as completely free and without ads. It covers LLMs and NLP with the Transformers, Datasets, Tokenizers and Accelerate libraries and the Hub. Chapters 1 to 4 introduce Transformers and fine-tuning, chapters 5 to 8 cover datasets, tokenizers and classic tasks, chapter 9 is about building demos, and chapters 10 to 12 cover advanced topics including fine-tuning, dataset curation and reasoning models. The page estimates about one week and 6 to 8 hours per chapter, asks for good Python and suggests an intro deep learning course first, and says there is currently no certificate.
8. CS336: Language Modeling from Scratch (Stanford)
Stanford’s Spring 2026 class teaches the full pipeline of building a language model: data collection and cleaning, Transformer construction, training and evaluation. Topics include resource accounting, mixture of experts, GPUs and Triton kernels, parallelism, scaling laws, data filtering and post-training with SFT, RLHF and RLVR. It is a 5-unit, implementation-heavy class that expects strong Python, PyTorch experience, college calculus and linear algebra, and ML fundamentals. The page indicates lecture recordings, materials, assignments and code are publicly accessible. It is the hardest course on this list.
9. DeepLearning.AI short courses
DeepLearning.AI’s catalog lists 107 short courses next to 14 longer courses and 11 professional certificates. Short courses run from about 36 minutes to about 3 hours, are tagged Beginner or Intermediate, and cover topics such as prompt engineering, multi-agent systems, attention in transformers, structured outputs and vector databases, often with partners including Meta, OpenAI, Anthropic and LangChain. The catalog page mentions membership plans but shows no prices, so check the individual course page for current access terms.
10. Anthropic Courses
Anthropic hosts a course catalog on Skilljar that includes Claude 101, Building with the Claude API, Introduction to Model Context Protocol, Claude Code in Action and a series of AI Fluency courses for educators, students, nonprofits and small businesses. The page does not label levels or state whether courses are free; it says a Skilljar account, not an Anthropic account, is required and mentions completion certificates without describing requirements. It is best for people who plan to use Claude in work or build on its API.
What to watch next
- Updates to fast.ai, Hugging Face and Microsoft courses as new model techniques become standard.
- New terms of Stanford’s language modeling and vision classes, which are re-run each year.
- Additional DeepLearning.AI short courses built with model providers.
- Changes in whether and how courses issue certificates.
Key takeaways
- Match the course to your current skills: conceptual first, then Python-based, then LLM-specific.
- Elements of AI, fast.ai, Hugging Face and CS50 AI are described as free on their official pages.
- Several pages, including Google’s, Karpathy’s and Anthropic’s, do not state a price, so check before enrolling.
- CS336 is the most demanding entry and assumes prior deep learning experience.
- Combining a conceptual course with a build-it-yourself one gives broader coverage; our developer learning path shows one way.
Frequently asked questions
What is the best free AI course for a complete beginner?
Elements of AI is the most beginner-friendly entry here: its first part needs no advanced math or programming, and the course is described as free. After it, Google’s Machine Learning Crash Course is a natural next step if you can program.
Do I need to know Python to learn AI?
For most courses on this list, yes. Elements of AI Part 1 does not require programming, but fast.ai, CS50 AI, Karpathy’s series and the Hugging Face course all list Python as a prerequisite.
Can I learn from videos alone?
Videos such as curated YouTube channels help with intuition, but courses with exercises and projects give you the practice that makes the ideas stick.
