A curated collection of learning resources for Generative AI, Machine Learning, Agentic AI, and related topics.
Browse the interactive cheatsheet: viveknaskar.github.io/everything-ai-ml
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- AI/ML Key Concepts
- AI/ML Building Blocks
- AI/ML Roadmap
- Generative AI – General
- Generative AI – Advanced
- Prompt Engineering
- RAG (Retrieval-Augmented Generation)
- Fine-tuning
- Frameworks
- Agentic AI
- MLOps and GenAIOps
- Security
- Google Cloud AI and ML
- AI Cost Optimization
- Adopting GenAI in Organizations
- AI Tools for Productivity
- Quantum Computing and PQC
- AI Augmented SDLC
- Coming Innovations in LLMs
- Courses
- Certifications
- Books
- Must-Read Research Papers
- Tools and Frameworks
- YouTube Channels
- Research Blogs
- Applied AI/ML Blogs
- Communities
- Practice Problems
- Interview Preparation
- Supervised Learning — Comprehensive guide to supervised learning algorithms and use cases
- Unsupervised Learning — Google Cloud's explainer on unsupervised learning techniques
- Reinforcement Learning — OpenAI Spinning Up's introduction to reinforcement learning concepts
- Deep Learning — DataCamp tutorial covering deep learning fundamentals
- Natural Language Processing (NLP) — Beginner-friendly introduction to natural language processing
- Computer Vision — GeeksforGeeks overview of computer vision fundamentals
- Generative Adversarial Networks (GANs) — AWS explainer on how generative adversarial networks work
- Dimensionality Reduction — Scikit-learn guide to PCA and other dimensionality reduction methods
- Clustering Algorithms — Scikit-learn documentation on clustering algorithms
- Bayesian Inference — StatLect lecture notes on Bayesian inference fundamentals
- Time Series Analysis — Free online textbook on forecasting and time series analysis
- Self-Supervised Learning — Lilian Weng's deep dive into self-supervised learning methods
Interactive Visualizations:
- MLU-Explain — Interactive visual explanations of core ML concepts
- CNN Explainer — In-browser interactive explainer for Convolutional Neural Networks
- Transformer Explainer — Interactive visualization of the Transformer architecture
- Mathematics for Machine Learning (UC Berkeley) — UC Berkeley lecture notes covering the math foundations for ML
- Linear Algebra for ML – MIT OpenCourseWare — MIT's full linear algebra course, free and self-paced
- Probability & Statistics – Stanford — Stanford lecture series on probability and statistics
- Calculus for Optimization – Khan Academy — Khan Academy's free multivariable calculus course
- Python for ML – Coursera — Coursera course teaching Python fundamentals for AI
- Optimization Techniques — Overview of optimization algorithms used in machine learning
- Data Preprocessing & Feature Engineering — Guide to feature engineering and data preprocessing techniques
- Model Evaluation & Metrics — Scikit-learn documentation on model evaluation metrics
- Regularization Techniques — Overview of regularization methods used to prevent overfitting
- Loss Functions — DataCamp tutorial on common loss functions in machine learning
- Activation Functions — Cheat sheet covering neural network activation functions
- Hyperparameter Tuning — Guide to hyperparameter tuning strategies
1. Learn Python and Core Libraries:
- Intro Python – Harvard CS50 — Harvard's CS50 introduction to Python programming
- Advanced Python – Harvard AI with Python — Harvard's CS50 course on AI with Python
- NumPy Quickstart — Official NumPy quickstart tutorial
- Pandas Tutorial — W3Schools tutorial on the Pandas library
- Matplotlib Tutorials — Official Matplotlib tutorials for data visualization
- Scikit-learn Tutorial — Official scikit-learn getting-started tutorial
2. Build a Strong Math Foundation:
- Linear Algebra – MIT — MIT's full linear algebra course, free and self-paced
- Probability & Statistics – Stanford — Stanford course syllabus for probability and statistics
- Multivariable Calculus – Khan Academy — Khan Academy's free multivariable calculus course
3. Learn ML Fundamentals:
- Google ML Crash Course — Google's free crash course covering core ML concepts
- Machine Learning by Andrew Ng — Andrew Ng's foundational machine learning course on Coursera
- The Hundred-Page ML Book — Concise book covering the essentials of machine learning
4. Build Practical Experience:
- Practical Deep Learning for Coders – fast.ai — fast.ai's hands-on deep learning course for coders
- Structured ML Projects – Coursera — Andrew Ng's course on structuring machine learning projects
- Build GPT from Scratch – Karpathy — Andrej Karpathy's video walkthrough building a GPT model from scratch
5. Specialize:
- NLP Course – Hugging Face — Hugging Face's free course on NLP with transformers
- Deep RL Course – Hugging Face — Hugging Face's free course on deep reinforcement learning
- Computer Vision – Kaggle — Kaggle's hands-on computer vision mini-course
- Deep Learning – CS231n Stanford — Stanford's CS231n course on CNNs for visual recognition
- Computer Vision, LLM, VLM Courses – PixelBank — PixelBank's course collection on CV, LLMs, and VLMs
6. Learn MLOps:
- Intro to MLOps — Introduction to MLOps principles and practices
- Three Levels of ML Software — ml-ops.org's breakdown of the three levels of ML software maturity
- Full Stack Deep Learning — Course on building and deploying production ML systems
7. Read Research Papers:
- ArXiv — Preprint server for ML and AI research
Recommended Talks:
- Andrej Karpathy – How I Use LLMs — Andrej Karpathy's walkthrough of his personal LLM workflow
- Andrej Karpathy – Intro to Large Language Models (1hr) — Andrej Karpathy's hour-long introduction to large language models
Visual Explainers:
- The Illustrated Transformer – Jay Alammar — Definitive visual guide to the Transformer architecture
- 3D Visualization of LLMs – Brendan Bycroft — Step-by-step 3D walkthrough of transformer execution
Learning Paths:
- Beginner: Introduction to Generative AI — Google Cloud Skills Boost path introducing generative AI fundamentals
- Intermediate: Gemini for Google Cloud — Learning path on using Gemini within Google Cloud
- Advanced: Generative AI for Developers — Advanced Google Cloud path on building with generative AI
Coursera Courses:
- GenAI for Executives & Business Leaders: An Introduction — Coursera course introducing generative AI concepts for business leaders
- GenAI for Execs & Business Leaders: Integration Strategy — Coursera course on integrating generative AI into business strategy
- GenAI for Product R&D Teams — Coursera course on applying generative AI in product R&D
- GenAI for Product Managers — Coursera course on generative AI for product management
Gemini:
- Large Multimodal Model Prompting with Gemini – DeepLearning.AI — DeepLearning.AI short course on prompting Gemini's multimodal capabilities
- Gemini for Application Developers – Coursera — Coursera course on building applications with Gemini
- Gemini CLI: Code & Create with an Open-Source Agent — DeepLearning.AI course on using the open-source Gemini CLI agent
Google ADK:
- Building Live Voice Agents with Google's ADK – DeepLearning.AI — DeepLearning.AI course on building live voice agents with Google's Agent Development Kit
- Understand Google Cloud Agents – Coursera — Coursera course explaining Google Cloud's agent-building blocks
Model Context Protocol (MCP):
- Intro to Model Context Protocol (MCP) – Coursera (Scrimba) — Scrimba course on Coursera introducing the Model Context Protocol
- Prompt Engineering Guide — Comprehensive guide to prompt engineering techniques
- Prompt Engineering – OpenAI API — OpenAI's official guide to prompt engineering techniques
- Prompt Engineering – OpenAI Developer Docs — Official OpenAI developer documentation on prompt engineering best practices
- Prompt Engineering Overview – Anthropic — Official Anthropic guide to prompt engineering for Claude
- Claude Code Prompt Library — Copy-paste prompt examples for Claude Code, tagged by task and role
- ChatGPT Prompt Engineering for Developers – DeepLearning.AI — DeepLearning.AI short course on prompt engineering with ChatGPT
- Google Prompting Essentials — Google's free course on prompting essentials
- The Prompt Report: A Systematic Survey of Prompting Techniques — Comprehensive survey of 58 LLM prompting techniques with a unified taxonomy and vocabulary
- Anthropic Prompt Engineering Interactive Tutorial — Hands-on Jupyter notebook tutorial covering prompt engineering techniques for Claude
- OpenAI Tokenizer — Interactive tool to visualize how text is tokenized and count tokens for OpenAI models
- Building and Evaluating Advanced RAG Applications – DeepLearning.AI — DeepLearning.AI short course on building and evaluating RAG systems
- Knowledge Graphs for RAG – DeepLearning.AI — DeepLearning.AI short course on combining knowledge graphs with RAG
- Building Agentic RAG with LlamaIndex – DeepLearning.AI — DeepLearning.AI short course on building agentic RAG pipelines with LlamaIndex
- Finetuning Large Language Models – DeepLearning.AI — DeepLearning.AI short course on fine-tuning LLMs
- Generative AI Advanced Fine-Tuning for LLMs – Coursera (IBM) — IBM's Coursera course on advanced LLM fine-tuning techniques
- Fine-tuning & RL for LLMs: Intro to Post-Training – DeepLearning.AI — DeepLearning.AI course covering post-training techniques including fine-tuning and RL
LangChain:
- GitHub – langchain-ai/langchain — Framework for building applications powered by LLMs
- LangChain Documentation — Official LangChain documentation
- LangChain for LLM Application Development – DeepLearning.AI — DeepLearning.AI short course on building LLM apps with LangChain
- LangChain: Chat with Your Data – DeepLearning.AI — DeepLearning.AI short course on building RAG chatbots with LangChain
- Functions, Tools and Agents with LangChain – DeepLearning.AI — DeepLearning.AI short course on building agents with LangChain
LangGraph:
- GitHub – langchain-ai/langgraph — Library for building stateful, multi-actor LLM agent workflows
- LangGraph Tutorials — Official LangGraph tutorials and getting-started guides
- AI Agents in LangGraph – DeepLearning.AI — DeepLearning.AI short course on building agents with LangGraph
CrewAI:
- GitHub – crewAIInc/crewAI — Framework for orchestrating role-playing autonomous AI agents
- CrewAI Official Site — Official site for the CrewAI multi-agent framework
- Multi AI Agent Systems with crewAI – DeepLearning.AI — DeepLearning.AI short course on building multi-agent systems with crewAI
- Practical Multi AI Agents and Advanced Use Cases with crewAI — DeepLearning.AI course on advanced multi-agent crewAI use cases
Google Agent Development Kit (ADK):
- Google ADK Documentation — Official documentation for Google's Agent Development Kit
- GitHub – google/adk-python — Python SDK for Google's Agent Development Kit
Agno (formerly Phidata):
- Agno Documentation – Agents — Official documentation for building agents with Agno
- GitHub – agno-agi/agno — Lightweight framework for building multi-agent systems
- Zero – Open-Source Coding Agent — The coding agent that answers to you, your model, your machine, your rules
- Agent Skills – Open Standard for Extending AI Agent Capabilities — Open standard for building reusable skills that extend AI agents across 30+ platforms including Claude, GitHub Copilot, and OpenAI Codex
- Caveman – Token-Saving Output Compression Skill — Agent skill that compresses AI output ~65% with terse, fragment-based responses while preserving reasoning; works across Claude Code, Codex, Gemini, Cursor and 30+ agents
- Ponytail – Minimal-Code Agent Skill — Agent skill enforcing a 'lazy senior developer' philosophy: check YAGNI, codebase, stdlib, platform and existing deps before writing new code (~54% less code); works across 16+ agents
- Omnigraph – Lakehouse Graph Database for Agents — Operational state and coordination layer for agent fleets with multimodal retrieval, Git-style branching, and object-storage-native deployment
- Claude SEO – AI-Powered SEO Audit Skill — Open-source Claude Code skill that runs 25 sub-skills and 18 parallel agents to audit technical SEO, content quality, schema markup, and AI search optimization with falsifiable recommendations
- Introduction to Agent2Agent (A2A) Protocol – Google Cloud — Google Cloud video introducing the Agent2Agent protocol for agent interoperability
- AI Agents Series – FuturMinds (YouTube Playlist) — YouTube playlist covering AI agent concepts and tutorials
- Evaluating AI Agents – DeepLearning.AI — DeepLearning.AI short course on evaluating AI agent performance
- LLMs as Operating Systems: Agent Memory – DeepLearning.AI — DeepLearning.AI short course on agent memory architectures
- AI Agents in LangGraph – DeepLearning.AI — DeepLearning.AI short course on building agents with LangGraph
- AI Agentic Design Patterns with AutoGen – DeepLearning.AI — DeepLearning.AI short course on agentic design patterns using AutoGen
- Multi AI Agent Systems with crewAI – DeepLearning.AI — DeepLearning.AI short course on building multi-agent systems with crewAI
- Building Agentic RAG with LlamaIndex – DeepLearning.AI — DeepLearning.AI short course on building agentic RAG pipelines with LlamaIndex
- Event-Driven Agentic Document Workflows – DeepLearning.AI — DeepLearning.AI short course on event-driven agentic document processing
- MLOps for Generative AI – Google Cloud Skill Boost — Google Cloud Skills Boost course on MLOps for generative AI
- GenAIOps: Operationalize Generative AI (YouTube) — Video overview of operationalizing generative AI systems
- MLOps.org — Community resource hub for MLOps best practices
- Full Stack Deep Learning — Course on building and deploying production ML systems
- Systems & Networking for AI Engineers – PixelBank — PixelBank course on systems and networking fundamentals for AI engineers
- OWASP Top 10 for Large Language Model Applications — OWASP's list of the top 10 security risks for LLM applications
- Google's Secure AI Framework (SAIF) — Google's framework for building AI systems securely
- The Dawn of Agentic AI in Security Operations – Google Cloud — Google Cloud blog on agentic AI's role in security operations
Learning Paths on Cloud Skills Boost:
- Gemini for Google Cloud — Learning path on using Gemini within Google Cloud
- Beginner: Introduction to Generative AI — Google Cloud Skills Boost path introducing generative AI fundamentals
- Intermediate: Generative AI Labs with Gemini — Hands-on labs for generative AI with Gemini
- Deploy and Manage Generative AI Models — Learning path on deploying and managing generative AI models on Google Cloud
- Machine Learning Engineer Learning Path — Google Cloud's learning path for machine learning engineers
- Build and Modernize Applications With Generative AI — Learning path on modernizing applications with generative AI
- Integrate Generative AI Into Your Data Workflow — Learning path on integrating generative AI into data workflows
- Generate Smarter Generative AI Outputs — Learning path on improving generative AI output quality
- Three Proven Strategies for Optimizing AI Costs – Google Cloud — Google Cloud blog on strategies to reduce AI costs
- Reduce Cost and Improve Your AI Workloads – Google Cloud — Google Cloud blog on optimizing AI workload cost and performance
- Vertex AI Pricing — Official pricing page for Google Cloud's Vertex AI
- Generative AI for Executives and Business Leaders Specialization – Coursera (IBM) — Coursera specialization on generative AI strategy for executives
- GenAI for Execs & Business Leaders: Integration Strategy – Coursera — Coursera course on integrating generative AI into business strategy
- GenAI for Everyone – Coursera (DeepLearning.AI) — DeepLearning.AI's beginner-friendly course on generative AI for everyone
- Maximize Productivity with AI Tools – Coursera (Google) — Google's Coursera course on using AI tools to boost productivity
- Google AI Professional Certificate – Coursera — Google's professional certificate covering practical AI skills
- Microsoft 365 Copilot for Productivity – Coursera (Microsoft) — Microsoft's Coursera course on using Copilot in Microsoft 365
- Introduction to Post-Quantum Cryptography – edX (UMBC) — University of Maryland's edX course on post-quantum cryptography
- Practical Introduction to Quantum-Safe Cryptography – IBM Quantum — IBM Quantum's course on quantum-safe cryptography
- Generative AI for Software Development Specialization – DeepLearning.AI — DeepLearning.AI specialization on using generative AI in software development
- AI-Powered Software Development – Coursera — Coursera specialization on AI-powered software development practices
- GitHub Copilot Fundamentals – Microsoft Learn — Microsoft Learn path covering GitHub Copilot fundamentals
- Google DeepMind Blog — Latest research and announcements from Google DeepMind
- Machine Learning by Andrew Ng (Coursera) — Andrew Ng's foundational machine learning course on Coursera
- AI For Everyone by Andrew Ng (Coursera) — Andrew Ng's non-technical introduction to AI for business
- Deep Learning Specialization (Coursera) — Andrew Ng's five-course specialization on deep learning
- Machine Learning with Python (edX – IBM) — IBM's edX course on practical machine learning with Python
- Reinforcement Learning Specialization (Coursera) — University of Alberta's Coursera specialization on reinforcement learning
- CS231n: CNNs for Visual Recognition (Stanford) — Stanford's CS231n course on CNNs for visual recognition
- RL Course by David Silver — David Silver's classic reinforcement learning lecture series
- NLP with Deep Learning – Stanford CS224n — Stanford's CS224n course on deep learning for NLP
- Practical Deep Learning for Coders – fast.ai — fast.ai's hands-on deep learning course for coders
- CV, LLM, VLM Courses – PixelBank — PixelBank's course collection on CV, LLMs, and VLMs
- AWS Certified Machine Learning Engineer – Associate — AWS certification validating machine learning engineering skills
- AWS Certified AI Practitioner – Skill Builder — AWS certification covering foundational AI and ML knowledge
- Microsoft Certified: Azure AI Engineer Associate — Microsoft certification for building AI solutions on Azure
- Stanford AI and Machine Learning Certificate — Stanford's professional certificate program in AI and machine learning
- Hands-On Large Language Models – Jay Alammar & Maarten Grootendorst — GitHub notebooks available
- Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow — Practical guide to ML and deep learning with popular Python libraries
- AI Engineering: Building Applications with Foundational Models — O'Reilly book on building applications with foundation models
- Introduction to Machine Learning Interviews – Chip Huyen — Chip Huyen's free book preparing candidates for ML interviews
- Designing Data-Intensive Applications — Classic book on building reliable, scalable data systems
- Designing Machine Learning Systems — Chip Huyen's book on building production-ready ML systems
- Deep Learning (Goodfellow, Bengio, Courville) — The definitive textbook on deep learning theory and practice
- Patterns, Predictions, and Actions – Hardt & Recht — Free textbook covering supervised learning, deep learning, causal inference, and RL
- Attention Is All You Need (Google) — The paper that introduced the Transformer architecture
- Language Models are Few-Shot Learners – GPT-3 (OpenAI) — Introduces GPT-3, a 175B parameter model demonstrating strong few-shot learning across NLP tasks
- Chain-of-Thought Prompting Elicits Reasoning in Large Language Models (Google) — Introduces chain-of-thought prompting, showing intermediate reasoning steps significantly improve LLM performance on complex tasks
- DeepSeek R1: Incentivizing Reasoning Capability in LLMs — Paper detailing DeepSeek's reasoning-focused R1 model trained with reinforcement learning
- Monolith: Real Time Recommendation System (TikTok/ByteDance) — TikTok/ByteDance's paper on their real-time recommendation system
- BERT: Pre-training of Deep Bidirectional Transformers — Paper introducing BERT, a bidirectional transformer for language understanding
- Understanding Deep Learning Requires Rethinking Generalization — Paper challenging traditional views of generalization in deep learning
- Playing Atari with Deep Reinforcement Learning — DeepMind's paper introducing deep Q-networks for Atari games
- Distilling the Knowledge in a Neural Network — Hinton et al.'s paper introducing knowledge distillation
- OpenAI Key Papers in Deep RL — OpenAI's curated reading list of foundational deep RL papers
Research Discovery Tools:
- Ai2 Asta — Agentic research assistant by Allen Institute for AI; discovers and synthesizes literature across 200M+ papers
- PyTorch — Video introduction to the PyTorch deep learning framework
- TensorFlow — Video introduction to the TensorFlow deep learning framework
- TensorFlow Playground — Browser-based neural network experimentation tool
- Scikit-Learn — Official getting-started guide for the scikit-learn library
- XGBoost — Official documentation for the XGBoost gradient boosting library
- Keras — Official getting-started guide for the Keras deep learning API
- Whisper – OpenAI — OpenAI's open-source automatic speech recognition model
- Can I Run AI? — Check if your hardware can run AI models locally
- Stanford Online — Stanford's YouTube channel featuring AI and ML lecture series
- Andrej Karpathy — Andrej Karpathy's channel on deep learning and LLMs from scratch
- FreeCodeCamp — FreeCodeCamp's channel with full-length coding and ML courses
- 3Blue1Brown — Grant Sanderson's channel known for visual, intuitive math explanations
- Sentdex — Channel with practical Python and machine learning tutorials
- OpenAI Blog — Official news and research updates from OpenAI
- Google DeepMind — Official blog covering DeepMind's research and announcements
- Google Research — Google's official blog on research across AI and computer science
- Apple ML Research — Apple's official machine learning research blog
- Amazon Science — Amazon's blog covering science and ML research across the company
- Microsoft AI — Microsoft's official blog on AI research and products
- Meta AI Blog — Meta's official blog on AI research and models
- Everything You Need To Know About AI Agent Engineering, In Brief — Explains how Context, Memory, and Harness Engineering differ for AI agents, through one simple analogy
- AWS Machine Learning Blog — AWS's blog on applied machine learning and ML services
- NVIDIA Deep Learning Blog — NVIDIA's blog covering deep learning research and applications
- AirBnB Engineering – AI & ML — Airbnb Engineering's posts on applied AI and ML
- Spotify Engineering — Spotify's engineering blog covering ML and platform work
- Uber Engineering – AI — Uber Engineering's articles on applied AI
- Netflix Tech Blog — Netflix's engineering blog covering ML and recommendation systems
- Google AI Blog — Google's blog on applied AI products and research
- r/LearnMachineLearning — Reddit community for people learning machine learning
- Chip Huyen MLOps Discord — Discord community focused on MLOps discussions
- Hugging Face Discord — Official Hugging Face community Discord server
Easy:
- Matrix times Vector — Deep-ML coding problem on matrix-vector multiplication
- Titanic: ML from Disaster – Kaggle — Kaggle's classic beginner competition predicting Titanic survival
- Predicting House Prices – Kaggle — Kaggle competition on predicting house prices with regression
Medium:
- Single Neuron — Deep-ML coding problem implementing a single neuron
- K-Means Clustering — Deep-ML coding problem implementing K-Means clustering from scratch
- Predicting Loan Default Risk – Kaggle — Kaggle competition on predicting loan default risk
- Sentiment Analysis on Movie Reviews – Kaggle — Kaggle competition on classifying movie review sentiment
Hard:
- Decision Tree Learning — Deep-ML coding problem implementing decision tree learning
- Implement a Simple RNN with Backpropagation — Deep-ML coding problem implementing an RNN with backpropagation from scratch
- GANs for Image Synthesis – Kaggle — Kaggle competition on generating images with GANs
- Introduction to Machine Learning Interviews – Chip Huyen — Chip Huyen's free book preparing candidates for ML interviews
- ML Interviews MVP – GitHub — Curated GitHub repo of resources for ML interview preparation
- Designing Machine Learning Systems — Chip Huyen's book on building production-ready ML systems
- ML System Design: 650 Case Studies – GitHub — Real-world ML use cases from 100+ companies including Netflix, Airbnb, and Uber
- AI Engineering from Scratch – GitHub — GitHub repo teaching AI engineering concepts from first principles
- ML Coding Questions – PixelBank — PixelBank's collection of ML coding interview questions
Feel free to open a PR if you have useful resources to add.
The resource list above is generated from a single source of truth:
website/src/data/resources.ts. Edit that file,
then regenerate this README so the two stay in sync:
cd website
npm install # first time only
npm run gen:readmeSee CONTRIBUTING.md for details.
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