The best AI and machine learning courses across every platform and skill level — curated and constantly updated. Links may include affiliate referrals.
These courses are consistently rated the highest and are the best entry points for their respective levels.
Andrew Ng's legendary course — the best introduction to ML ever made. Covers supervised learning, unsupervised learning, and best practices. Updated for 2024 with Python and scikit-learn.
Google's official course on using AI tools in the workplace. No coding needed. Perfect for business professionals who want to harness AI in everyday work tasks. Comes with a certificate.
A free online course from the University of Helsinki and MinnaLearn. No math or programming required. Explains what AI is, how it's built, and how it's changing society. 500K+ completions.
Microsoft's open-source curriculum on GitHub — 24 lessons covering neural networks, computer vision, NLP, and more. Practical with Jupyter notebooks and code examples throughout.
Andrew Ng and Isa Fulford (OpenAI) teach you how to use the ChatGPT API effectively. Covers prompt engineering principles, summarizing, inferring, transforming, and expanding. 1.5 hours.
Andrew Ng's 5-course specialization covering neural networks, CNNs, sequence models, and optimization. The gold standard for learning deep learning fundamentals. 1M+ enrolled.
Learn to use the Transformers library for NLP. Build and deploy BERT, GPT-2, and other models. Covers fine-tuning, datasets, tokenizers, and the Hugging Face ecosystem. Hands-on throughout.
Build LLM-powered applications using LangChain. Covers chains, agents, memory, RAG, and evaluation. Taught by Harrison Chase (LangChain creator) and Andrew Ng. Essential for LLM developers.
Jeremy Howard's top-down approach — build real deep learning applications first, then understand the theory. Uses PyTorch and fastai. Highly practical, used by thousands of practitioners.
Complete preparation for the Google TensorFlow Developer Certificate. Covers CNNs, NLP, time series, and deployment. Zero to certified in one course. Taught by Daniel Bourke.
Google's own ML education platform. 25+ lessons covering linear regression, classification, neural networks, embeddings, and fairness. Interactive visualizations and coding exercises included.
Stanford's flagship ML course with full lecture videos on YouTube. Deep mathematical treatment of ML algorithms — linear algebra, probability, optimization, and theory. The academic gold standard.
Andrej Karpathy (former OpenAI, Tesla AI) builds neural networks from scratch in Python — micrograd, makemore, GPT-2. The best hands-on series for truly understanding transformers and LLMs.
Learn to deploy ML models at scale — data pipelines, model serving, monitoring, and infrastructure. Andrew Ng's specialization for taking ML from notebooks to production systems. 4-course series.
Learn to fine-tune LLMs with LoRA and QLoRA on custom datasets. Covers instruction tuning, data preparation, training, and evaluation. Taught by Sharon Zhou. Directly applicable skills.
Learn the RLHF pipeline that powers ChatGPT — reward modeling, PPO training, and Constitutional AI. Taught by Lamini's team. Covers the complete alignment training pipeline from theory to code.
Build systems that search and retrieve across text, images, audio, and video using multimodal embeddings and vector search. Uses Weaviate's multimodal models. Highly practical and cutting-edge.
Not sure which courses to take? Follow one of these curated sequences.
For business professionals, managers, and curious minds who want to understand AI without coding.
For software engineers who want to build AI-powered products and applications.
For those who want to push the frontier — understand model internals, training, and alignment.