Academy
Curated AI & ML learning resources — from deep technical to domain-specific I want to learn something now
Practical Deep Learning for Coders
Top-down, hands-on approach to deep learning. Covers training models from day one, then digs into the theory.
From Deep Learning Foundations to Stable Diffusion
30+ hours implementing Stable Diffusion from scratch. Build MLPs, ResNets, UNets, and diffusion pipelines with DDPM, DDIM, textual inversion, and Dreambooth.
Machine Learning Specialization
Andrew Ng's updated ML course. Covers regression, classification, neural networks, and recommender systems with Python.
Deep Learning Specialization
Five-course series covering neural network foundations through sequence models and attention mechanisms.
Generative AI for Everyone
Non-technical overview of generative AI. How LLMs work, what they can and can't do, and how to think about AI strategy.
ChatGPT Prompt Engineering for Developers
Prompt engineering best practices for application development using the OpenAI API. Covers summarization, inference, transformation, and expansion.
Building and Evaluating Advanced RAG Applications
Build advanced RAG pipelines with sentence-window and auto-merging retrieval, then evaluate with the RAG triad of metrics.
AI Agents in LangGraph
Build agentic AI workflows with LangGraph. Covers tool use, planning, reflection, and a research agent using Tavily search.
Reinforcement Learning from Human Feedback
Understand how RLHF is used to tune and evaluate LLMs. Covers reward models, PPO, and evaluation of tuned models.
Multi AI Agent Systems with CrewAI
Design effective AI agents and organize multi-agent teams for complex tasks. Taught by CrewAI founder Joao Moura.
Building Agentic RAG with LlamaIndex
Build agentic RAG systems that reason over your data with tool use and decision-making. Taught by LlamaIndex co-founder Jerry Liu.
Multimodal RAG: Chat with Videos
Build multimodal RAG pipelines that extract and reason over video content, combining vision and language models.
Building AI Browser Agents
Build AI agents that navigate and interact with web browsers autonomously, performing multi-step tasks on real websites.
Building Code Agents with Hugging Face smolagents
Build code agents that write and execute Python to solve tasks using Hugging Face's smolagents library for lightweight agent development.
PyTorch for Deep Learning
DeepLearning.AI's PyTorch-focused certificate. Build and train neural networks from scratch using PyTorch's dynamic computation graphs.
Hugging Face NLP Course
Hands-on NLP with the Transformers library. Tokenizers, fine-tuning, and building real pipelines.
Hugging Face Deep RL Course
From Q-learning basics to PPO and RLHF. Train agents in virtual environments with hands-on exercises.
Hugging Face Audio Course
End-to-end audio ML with transformers. Covers ASR, text-to-speech, and real-world applications like speech-to-speech translation.
Hugging Face Computer Vision Course
Community-driven course covering CV fundamentals through state-of-the-art models including vision transformers and multimodal vision-language models.
Hugging Face Diffusion Models Course
Diffusion model fundamentals, fine-tuning, guidance, Stable Diffusion internals, and advanced generation techniques.
Hugging Face AI Agents Course
Six-week course on building AI agents with smolagents, LlamaIndex, and LangGraph. Covers code agents, tool-calling agents, and retrieval agents.
Hugging Face LLM Course
Comprehensive LLM course covering pre-training, fine-tuning, RLHF with GRPO, RAG, evaluation, and deployment with TRL.
Hugging Face smol-course — Fine-tuning Small LLMs
Fast-paced course on fine-tuning small language models using TRL and Transformers. Covers SFT, preference alignment, evaluation, and VLMs — all runnable on local hardware.
Stanford CS229: Machine Learning
The rigorous, math-heavy Stanford ML course. Covers supervised learning, unsupervised learning, and learning theory.
Stanford CS224N: NLP with Deep Learning
Deep dive into modern NLP. Word vectors, transformers, pretraining, and generation from Chris Manning's group.
Stanford CS231N: Deep Learning for Computer Vision
Stanford's flagship computer vision course. Covers CNNs, vision transformers, diffusion models, and vision-language models.
Stanford CS234: Reinforcement Learning
Rigorous treatment of reinforcement learning. MDPs, dynamic programming, Monte Carlo methods, temporal difference, and policy gradient methods.
Stanford CS224W: Machine Learning with Graphs
Representation learning on graphs — GNNs, knowledge graph reasoning, influence maximization, and network analysis. Taught by Jure Leskovec.
MIT 6.S191: Introduction to Deep Learning
Fast-paced MIT course covering deep learning foundations with hands-on TensorFlow labs. Updated annually.
MIT 6.S183: A Practical Introduction to Diffusion Models
Build diffusion models from the ground up. Covers algorithms, implementation, and using pretrained models for downstream tasks.
UC Berkeley CS188: Introduction to Artificial Intelligence
Berkeley's comprehensive intro to AI. Search, game trees, constraint satisfaction, Bayesian networks, ML, and reinforcement learning.
UC Berkeley CS285: Deep Reinforcement Learning
Sergey Levine's graduate course on deep RL. Policy gradients, actor-critic, model-based RL, exploration, and offline RL.
CS50's Introduction to Artificial Intelligence with Python
Harvard's AI course covering graph search, adversarial search, knowledge, uncertainty, optimization, machine learning, and neural networks in Python.
Tiny Machine Learning (TinyML) Professional Certificate
Deploy ML models on microcontrollers. Covers TensorFlow Lite for Microcontrollers, model optimization, and real-world embedded AI applications.
Georgia Tech CS 6601: Artificial Intelligence
Graduate-level AI covering search, game playing, Bayesian networks, machine learning, and logic. Part of the affordable OMSCS program.
Georgia Tech CS 7641: Machine Learning
Comprehensive ML course covering supervised, unsupervised, and reinforcement learning with theoretical depth. Part of the OMSCS program.
First Principles of Computer Vision
Five-course specialization from first principles — camera imaging, features, 3D reconstruction, and perception. All lectures free on YouTube.
Stanford CS237A: Principles of Robot Autonomy
Perception, localization, SLAM, motion planning, and decision-making for mobile robots. Extensive use of ROS for hands-on activities.
Prompt Engineering Guide
Comprehensive prompt engineering reference. Techniques, models, applications, and risks.
LangChain Academy
Build LLM-powered applications with LangChain and LangGraph. Covers chains, agents, RAG, and deployment.
Anthropic Prompt Engineering Tutorial
Anthropic's official guide to prompting Claude effectively. Covers techniques from basic to advanced.
Claude 101
Anthropic's foundational Claude course. Covers prompting, Projects, Artifacts, Skills, research mode, and role-specific use cases.
AI Fluency: Framework & Foundations
Learn to work with any AI effectively and responsibly. Covers prompt techniques, collaboration patterns, and ethical AI use.
Building with the Claude API
Deep dive into the Claude API. Covers API calls, tool use, streaming responses, error handling, and building AI applications.
Introduction to Model Context Protocol (MCP)
Build MCP servers and clients from scratch using Python. Connect Claude to external tools, data sources, and systems.
Claude Code in Action
Integrate Claude Code into your development workflow. Read files, run commands, edit code, manage context, and automate tasks.
Building with Anthropic Models on AWS Bedrock
Deploy and use Anthropic models through AWS Bedrock. Integration patterns, configuration, and building production applications.
Building with Anthropic Models on Google Vertex AI
Deploy and use Anthropic models through Google Vertex AI. Integration, configuration, and building production applications.
AI Fluency for Educators
Helps faculty and instructional designers apply AI fluency in teaching practice and institutional strategy.
OpenAI Cookbook
Official OpenAI recipes and guides for the GPT API. Covers embeddings, semantic search, fine-tuning, function calling, and agent architectures.
OpenAI Academy
OpenAI's learning platform covering API usage, prompt design, system messages, tokens, and integrating GPT models into applications.
Full Stack LLM Bootcamp
Practical bootcamp on building LLM apps. Prompt engineering, augmented language models, and launch strategies.
Neural Networks: Zero to Hero
Build a GPT from scratch. Karpathy walks through neural nets, backprop, and transformer architectures step by step.
LLM Course
Comprehensive roadmap to LLMs with Colab notebooks. Covers the LLM Scientist and LLM Engineer paths including training, datasets, evaluation, and quantization.
Build a Large Language Model (From Scratch)
Step-by-step guide to building a GPT-style LLM from scratch. Companion video course covers 15+ hours of implementation detail.
Deep Learning Fundamentals
Ten-unit course covering deep learning fundamentals with bite-sized videos and hands-on exercises using open-source tools.
AI Engineering
Comprehensive guide to building production AI systems. Covers the full stack from model selection to deployment, drawing from a decade of ML systems experience.
AI Evals For Engineers & PMs
Hands-on course on finding, diagnosing, and prioritizing AI errors. Over 2,000 PMs and engineers trained, including teams at OpenAI and Anthropic.
Lil'Log — ML Research Tutorials
Authoritative deep-dive blog posts covering cutting-edge ML research. Each post is a self-contained tutorial with extensive references and illustrations.
Illustrated Transformer & LLM Visualizations
Visual explanations of transformers, GPT, and BERT used in courses at Stanford, Harvard, MIT, and CMU.
Distill.pub & colah's blog — Neural Network Interpretability
Pioneering visual explanations of neural network internals. Feature visualization, circuits, and building blocks of interpretability.
Neural Networks: From Basics to Transformers
Visual, intuition-first explanations of neural networks, gradient descent, backpropagation, and transformer attention with stunning animations.
Essence of Linear Algebra
Geometric intuition for linear algebra concepts essential to ML. Vectors, linear transformations, matrix operations, determinants, and eigenvalues.
StatQuest Machine Learning
Bite-sized, jargon-free explanations of ML algorithms. Linear regression, logistic regression, decision trees, random forests, SVMs, PCA, and clustering.
Khan Academy Linear Algebra
Structured linear algebra curriculum with practice exercises. Vectors, matrix operations, transformations, and eigenvalues at a self-study pace.
Khan Academy Statistics and Probability
Comprehensive statistics and probability with interactive exercises. Descriptive statistics, distributions, regression, and hypothesis testing.
Mathematics for Machine Learning Specialization
Three-course specialization covering the math underpinning ML: linear algebra, multivariate calculus, and PCA with worked examples and Python exercises.
Google AI Essentials
Google's intro to AI for non-technical professionals. Learn to use AI tools effectively and responsibly.
Introduction to Generative AI Learning Path
Google Cloud's introductory gen AI path. What LLMs are, how they work, responsible AI principles, and Generative AI Studio basics.
Introduction to Vertex AI Studio
Build Gemini multimodal applications with Vertex AI Studio. Prompt-to-product lifecycle, prompt engineering, and model tuning.
Introduction to AI and Machine Learning on Google Cloud
Overview of Google Cloud AI capabilities. AI foundations, ML pipelines, and generative AI project setup.
Google ML Recommender Systems Course
Google's practical guide to building recommendation systems. Candidate generation, scoring, and re-ranking with production-oriented examples.
AWS Foundations of Prompt Engineering
AWS course covering basic to advanced prompt techniques for working with foundation models on Amazon Bedrock.
AWS Generative AI and AI Agents with Amazon Bedrock
Build generative AI applications and AI agents on Amazon Bedrock. Knowledge Bases, RAG, Guardrails, and multi-step agent workflows.
AWS Generative AI Applications Professional Certificate
No-code gen AI certificate from AWS. Bedrock Console, prompt engineering, Guardrails, and PartyRock through hands-on business scenarios.
Microsoft Azure AI Fundamentals (AI-900)
Microsoft's AI fundamentals covering ML concepts, computer vision, NLP, and responsible AI with Azure services. Prepares for the AI-900 exam.
Develop AI Solutions with Azure (AI-102)
Build AI solutions using Azure Cognitive Services, Bot Framework, and Azure Search. Vision, language, speech, and decision APIs.
Microsoft AI & ML Engineering Professional Certificate
Microsoft's professional certificate for AI/ML engineering. Building, training, and deploying ML models with Azure AI services.
NVIDIA Fundamentals of Deep Learning (Self-paced)
Hands-on deep learning fundamentals with GPU-accelerated cloud labs. Train CNNs, apply data augmentation, and deploy models in 8 hours.
NVIDIA Generative AI Explained
No-code overview of generative AI. How LLMs, diffusion models, and GANs work, plus applications and challenges. Certificate on completion.
IBM AI Engineering Professional Certificate
Six-course certificate covering deep learning with Keras, TensorFlow, and PyTorch. Build and deploy neural networks and CV models.
IBM Generative AI Engineering Professional Certificate
Gen AI engineering skills in six months. Foundation models, prompt engineering, RAG, fine-tuning, and deploying generative AI applications.
IBM RAG and Agentic AI Professional Certificate
Build production RAG pipelines and agentic AI systems. Retrieval strategies, agent architectures, and deployment patterns.
IBM AI Foundations for Business
Non-technical introduction to AI for business leaders. AI concepts, industry use cases, and building an AI strategy.
Fundamentals of Building AI Agents
Foundations of tool calling and chaining with LangChain. How LLMs trigger tool use and connect to calculators, code, and external data.
IBM Generative AI for Cybersecurity Professionals
Generative AI tools for cybersecurity tasks including threat analysis, incident response, and security automation.
Building AI Products
W&B courses on ML experiment tracking, LLM evaluation, fine-tuning, and CI/CD for ML pipelines.
Made With ML
End-to-end MLOps course from experimentation to production. MLflow, pipeline orchestration, deployment patterns, monitoring, and testing.
ML Observability Course
Six-module, 40-lesson course on production ML monitoring. Data quality, model performance, drift detection, and monitoring dashboards.
Advanced RAG Certification Course
40+ lessons and 7 interactive projects on production RAG. Advanced retrieval techniques, evaluation, and multi-tenant architectures.
ML Zoomcamp
Free four-month ML course. Covers regression through deployment with hands-on homework and a capstone project. Certificate available.
LLM Zoomcamp
Ten-week course on building AI applications with LLMs. RAG, vector databases, orchestration, and monitoring with OpenAI, Elasticsearch, and Ollama.
MLOps Zoomcamp
Free 6-module MLOps course covering experiment tracking, workflow orchestration, deployment patterns, and monitoring with Evidently AI.
Kaggle Intro to Machine Learning
Quick hands-on introduction to ML. Build your first model, learn validation, overfitting/underfitting, and random forests with real Kaggle datasets.
Kaggle Intermediate Machine Learning
Handle missing values, categorical variables, pipelines, cross-validation, XGBoost, and data leakage. Practical extensions to the intro course.
Kaggle Feature Engineering
Create better features for ML models. Mutual information, K-Means clustering for features, PCA, and target encoding with hands-on exercises.
Kaggle Intro to Deep Learning
Build and train neural networks with TensorFlow and Keras. Dense layers, optimization, regularization, and dropout in a hands-on format.
Kaggle Computer Vision
Build CNNs for image classification. Convolution, transfer learning with pre-trained models, and data augmentation techniques.
Kaggle Natural Language Processing
Intro to NLP covering text classification with spaCy, word vectors, and sentiment analysis. Short, practical micro-course.
Kaggle Intro to AI Ethics
Practical introduction to AI ethics. Human-centered design, identifying bias, AI fairness, and model cards in a hands-on format.
Applied Data Science with Python Specialization
Five-course specialization covering Python data science with pandas, matplotlib, scikit-learn, and NLTK.
Recommender Systems Specialization
Non-personalized, content-based, and collaborative filtering through matrix factorization and hybrid methods.
AI in Finance Specialization
Machine learning and reinforcement learning methods applied to finance — trading, portfolio management, risk.
AI for Healthcare Specialization
AI applications in healthcare — clinical data, imaging, and evaluating medical AI systems.
AI for Cybersecurity
Machine learning methods applied to cybersecurity problems. Threat detection, anomaly identification, and defensive AI systems.
AI for Legal Professionals
How AI is transforming legal practice — contract review, case research, and compliance automation.
Practical AI for Business Leaders
Wharton's course on AI strategy for decision-makers. Use cases, ROI frameworks, and organizational adoption.
AI Safety Fundamentals: Alignment Course
Seven-week course introducing key AI safety and alignment concepts. Used to train 4,000+ professionals now at Anthropic, OpenAI, and AI safety institutes.
AI Safety, Ethics, and Society
Online course covering AI safety from technical risks to governance. Includes project work and is open to global participants.
PyTorch Official Tutorials
Official beginner tutorials for a complete ML workflow in PyTorch — tensors, autograd, datasets, model building, training, and saving.
Learn PyTorch for Deep Learning
Hands-on, code-first PyTorch course as a free online book. Fundamentals through transfer learning and experiment tracking.
OpenCV Free Courses
Free courses from the official OpenCV team covering image manipulation, object detection, face detection, and a PyTorch bootcamp.
Vision Language Model Bootcamp
Techniques from CLIP to Qwen2.5-VL for image captioning, object detection, and multimodal understanding.
DataCamp AI Fundamentals
No-code introduction to AI concepts and tools. How AI and LLMs work, prompt engineering, and practical AI applications.
DataCamp Machine Learning Scientist with Python
93-hour track covering supervised, unsupervised, and deep learning techniques with real-world datasets and hands-on coding.
Udacity Machine Learning DevOps Engineer Nanodegree
Production ML engineering focused on deployment, automation, and monitoring. CI/CD, container orchestration, and model performance tracking.
Udacity Intro to Machine Learning with PyTorch
Supervised, unsupervised, and deep learning with PyTorch through projects in customer segmentation and image classification.
Codecademy ML/AI Engineer Career Path
Full career path with curated courses, projects, and technical interview prep. Fundamentals through deep learning and NLP.
Brilliant — Machine Learning
Interactive, problem-solving-based ML fundamentals. Visual neural network explorations and math foundations from top institutions.
AI Maker Space — AI Engineering Bootcamp
Build, ship, and share production-grade AI prototypes. Designed for backend engineers going all-in on AI applications.
Fine-Tuning LLMs Using LoRA and QLoRA
Hands-on parameter-efficient fine-tuning. Customize Llama 3 and other models with LoRA and QLoRA on limited compute resources.
Time Series Forecasting with Machine Learning
Forecast multiple time series using ML algorithms — linear regression, decision trees, random forests, and gradient boosting machines.