Learn AI
From zero to hero — free courses, tutorials, and resources from the world's best institutions. Curated learning paths to guide your journey.
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AI Fundamentals
11 resources
Start here. Core concepts in machine learning, deep learning, and artificial intelligence from top universities.
Machine Learning Specialization
Andrew Ng's legendary ML course, updated for 2024. Covers supervised & unsupervised learning, recommender systems, and reinforcement learning with Python.
Introduction to Generative AI
A concise introductory course explaining what generative AI is, how it works, and how to use it. Great for absolute beginners.
CS50's Introduction to AI with Python
Explore the concepts and algorithms behind modern AI. Covers graph search, optimization, machine learning, neural networks, and NLP using Python.
Practical Deep Learning for Coders
Top-down, hands-on approach to deep learning. Build state-of-the-art models from day one. Covers vision, NLP, tabular data, and collaborative filtering.
Elements of AI
A free online introduction to AI designed for non-experts. Covers what AI is, philosophy, machine learning, neural networks, and societal implications.
Intro to Machine Learning
Gentle introduction to machine learning concepts as part of AP Computer Science Principles. Great for high school and early college students.
MIT 6.S191 — Introduction to Deep Learning
MIT's official introductory deep learning course. Covers dense networks, CNNs, RNNs, generative models, reinforcement learning, and more with TensorFlow labs.
Stanford CS229 — Machine Learning
The full Stanford ML course. Rigorous mathematical treatment of supervised learning, unsupervised learning, learning theory, and reinforcement learning.
Intro to Machine Learning
Learn the core ideas in machine learning and build your first models with scikit-learn. Hands-on micro-course with immediate feedback.
Machine Learning Crash Course
Google's fast-paced practical introduction to ML. Covers linear regression, classification, neural networks, embeddings, and ML engineering best practices.
AI for Beginners
A 12-week, 24-lesson curriculum covering symbolic AI, neural networks, computer vision, NLP, and other AI topics with hands-on projects.
Large Language Models & NLP
12 resources
Master prompt engineering, build with LLMs, and understand the architecture behind large language models.
Prompt Engineering Guide
Official Anthropic guide to writing effective prompts for Claude. Covers techniques like chain-of-thought, few-shot learning, and system prompts.
OpenAI Cookbook
Collection of practical examples and best practices for using the OpenAI API. Covers embeddings, fine-tuning, function calling, and RAG patterns.
ChatGPT Prompt Engineering for Developers
Short course by Andrew Ng & Isa Fulford on using LLM APIs effectively. Learn to summarize, infer, transform, and expand text programmatically.
CS224N — NLP with Deep Learning
Stanford's premier NLP course. Covers word vectors, neural networks for NLP, attention, transformers, pretraining, and large language models.
NLP Course
Learn how to use Transformers, Datasets, and Tokenizers libraries. Covers fine-tuning, building demos, and sharing models on the Hub.
The Illustrated Transformer
The most cited visual explanation of the Transformer architecture. Step-by-step illustrations of self-attention, multi-head attention, and positional encoding.
Neural Networks: Zero to Hero
Build neural networks from scratch in code. Covers backpropagation, language modeling, GPT architecture, and tokenization with pure Python and PyTorch.
LangChain for LLM Application Development
Learn to build LLM-powered apps with LangChain. Covers memory, chains, agents, and evaluation for production applications.
LLM University
Comprehensive curriculum on large language models. Covers text representation, generation, embeddings, semantic search, and RAG from the ground up.
Anthropic Courses
Official educational courses from Anthropic covering Claude API usage, prompt engineering patterns, tool use, and building production applications.
Claude API Documentation & Tutorials
Official Anthropic docs covering the Claude API: authentication, messages API, vision, tool use, streaming, and best practices for building production apps.
Constitutional AI — Harmlessness from AI Feedback
Anthropic's landmark paper on Constitutional AI (CAI) — how Claude is trained to be helpful, harmless, and honest using RLHF with AI-generated feedback.
Computer Vision
5 resources
Learn to build systems that see and understand images, from CNNs to modern vision transformers.
CS231N — Deep Learning for Computer Vision
The definitive computer vision course. Covers image classification, CNNs, object detection, segmentation, generative models, and vision transformers.
PyImageSearch Free Tutorials
Hundreds of free hands-on tutorials on OpenCV, deep learning for vision, face recognition, object detection, and image processing with Python.
Computer Vision Course
Learn modern computer vision with Transformers. Covers image classification, object detection, segmentation, and multimodal models using HF libraries.
Computer Vision Micro-Course
Hands-on Kaggle micro-course on building convolutional neural networks. Learn to build image classifiers with data augmentation and transfer learning.
First Principles of Computer Vision
Columbia professor Shree Nayar explains computer vision from first principles. Covers cameras, image formation, features, 3D reconstruction, and recognition.
Deep Learning & Neural Networks
7 resources
Dive deep into neural network architectures, training techniques, and the math behind modern AI.
Neural Networks Series
Beautiful visual explanations of how neural networks learn. Covers gradient descent, backpropagation, and what neurons actually compute. Best visual intro available.
Deep Learning Specialization
Andrew Ng's comprehensive 5-course deep learning specialization. Covers neural networks, optimization, CNNs, sequence models, and practical methodology.
Deep Learning with Yann LeCun
Yann LeCun's NYU course. Covers energy-based models, self-supervised learning, graph neural networks, and cutting-edge deep learning research.
Dive into Deep Learning
Interactive deep learning textbook with code, math, and discussions. Used at 500+ universities. Covers everything from linear models to transformers and GANs.
Neural Network Playground
Tinker with a real neural network in your browser. Visualize how different architectures, activations, and learning rates affect training in real-time.
Deep Learning (Goodfellow, Bengio, Courville)
The definitive deep learning textbook, freely available online. Covers linear algebra, probability, optimization, CNNs, RNNs, autoencoders, and generative models.
Full Stack Deep Learning
Learn to build and deploy production ML systems. Covers project planning, data management, training, deployment, monitoring, and team workflows.
AI Tools & Frameworks
8 resources
Master the tools that power modern AI development — PyTorch, TensorFlow, Hugging Face, and more.
Official PyTorch Tutorials
Comprehensive tutorials from the PyTorch team. Covers tensors, autograd, neural networks, data loading, distributed training, and deployment.
TensorFlow Tutorials
Step-by-step tutorials covering TensorFlow basics, Keras, image classification, text processing, data pipelines, and model deployment.
Transformers Course
Master the Transformers library. Learn to fine-tune pretrained models, build pipelines, and deploy models using the Hugging Face ecosystem.
W&B Courses
Free courses on experiment tracking, hyperparameter tuning, model evaluation, CI/CD for ML, and building LLM applications with W&B.
LlamaIndex Documentation & Tutorials
Learn to build RAG applications, data agents, and structured data extraction pipelines with LlamaIndex. Includes starter tutorials and advanced guides.
JAX Quickstart
Get started with JAX, Google's high-performance numerical computing library. Learn automatic differentiation, JIT compilation, and vectorization for ML research.
MLflow Tutorials
Learn MLflow for experiment tracking, model packaging, and deployment. Essential MLOps tool used across the industry for reproducible ML workflows.
Scikit-learn Tutorials
Official tutorials for the most popular classical ML library. Covers classification, regression, clustering, preprocessing, model selection, and pipelines.
AI Ethics & Safety
5 resources
Understand the critical challenges of AI alignment, safety, fairness, and responsible development.
Anthropic Research
Cutting-edge research on AI safety, constitutional AI, interpretability, and alignment. Includes papers, blog posts, and technical reports.
AI Safety Fundamentals
Structured course on AI alignment and governance. Covers technical alignment, AI governance, and career paths in AI safety. Used by 10,000+ participants.
Center for AI Safety Resources
Research and resources from CAIS on reducing societal-scale risks from AI. Includes the widely-signed statement on AI risk and educational materials.
Ethics of AI
Free online course on the ethical dimensions of AI. Covers fairness, transparency, accountability, privacy, and the societal impact of automated systems.
Responsible AI Resources
Microsoft's framework for responsible AI development. Covers fairness, reliability, privacy, inclusiveness, transparency, and accountability principles.
YouTube Channels
10 resources
The best YouTube channels for learning AI, staying current, and understanding research papers.
3Blue1Brown
Grant Sanderson's stunning visual math explanations. Essential for understanding linear algebra, calculus, and neural networks intuitively.
Andrej Karpathy
Former Tesla AI Director and OpenAI founding member. Deep, code-along tutorials building neural networks from scratch. Legendary teaching quality.
StatQuest with Josh Starmer
Crystal-clear explanations of statistics and machine learning. Each concept broken down with memorable songs and simple diagrams. BAM!
Two Minute Papers
Quick, exciting summaries of cutting-edge AI research papers. Great for staying up to date with the latest breakthroughs in AI and graphics.
Sentdex
Python programming, machine learning, and neural networks tutorials. Known for building complete projects from scratch with clear explanations.
AI Explained
In-depth analysis of AI developments, model comparisons, and implications. Balanced, well-researched takes on the latest AI news.
Yannic Kilcher
Detailed paper walkthroughs and ML news. Dives deep into the math and methodology of important research papers. Great for understanding SOTA techniques.
Machine Learning Street Talk
Long-form interviews with top AI researchers. Technical discussions on consciousness, reasoning, scaling laws, and the future of AI.
DeepLearning.AI
Andrew Ng's channel with short courses, event talks, and The Batch newsletter videos. Covers practical AI applications and career advice.
Fireship
Fast-paced, witty tech content including AI explainers. Famous for '100 seconds' format and keeping developers informed on AI trends.
Interactive Playgrounds
7 resources
Hands-on environments to experiment with AI models, build projects, and test ideas instantly.
Google AI Studio
Free web-based IDE for prototyping with Google's Gemini models. Test prompts, tune models, and build with the Gemini API interactively.
OpenAI Playground
Interactive environment to test OpenAI models. Experiment with different parameters, system prompts, function calling, and assistants.
Anthropic Console
Test Claude models, build prompts in the Workbench, manage API keys, and explore Claude's capabilities with the official Anthropic developer console.
Hugging Face Spaces
Discover and run thousands of ML demos built by the community. From image generation to text analysis — try models without any setup.
Replicate
Run open-source ML models in the cloud with one click. Browse thousands of models for image generation, video, audio, and language tasks.
TensorFlow Playground
Visualize neural network training in real-time. Adjust layers, neurons, learning rate, and activation functions to see how networks learn patterns.
Kaggle
The world's largest data science platform. Free GPU notebooks, competitions, datasets, and a massive community. Essential for any ML practitioner.
Reading & Research
8 resources
Stay at the frontier. Top blogs, newsletters, and research aggregators for the latest in AI.
Papers With Code
The ultimate ML research aggregator. Links papers to their official code implementations, datasets, and state-of-the-art benchmarks. Updated daily.
Distill.pub
Machine learning research journal with interactive, beautifully designed articles. Each piece is a masterclass in scientific communication and visualization.
Lil'Log
OpenAI researcher Lilian Weng's blog. Incredibly thorough technical deep dives into transformers, diffusion models, RLHF, agents, and more.
The Batch Newsletter
Andrew Ng's weekly AI newsletter. Curated AI news, research highlights, and industry trends delivered to your inbox. Read by 500,000+ subscribers.
Ahead of AI
Sebastian Raschka's newsletter on cutting-edge ML research. Deep technical analysis of new papers, model architectures, and training techniques.
arxiv-sanity
Built by Karpathy, this tool helps you navigate the flood of arXiv papers. Search, filter, sort, and get recommendations for ML research papers.
Colah's Blog
Anthropic researcher Chris Olah's legendary blog. Visual, intuitive explanations of LSTMs, attention, neural network representations, and information theory.
Machine Learning Mastery
Thousands of free tutorials making ML accessible. Practical, code-first guides on deep learning, NLP, time series, computer vision, and more.
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