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
- Courses
- Certifications
- Books
- Must-Read Research Papers
- Tools and Frameworks
- YouTube Channels
- Research Blogs
- Applied ML Blogs
- Communities
- Practice Problems
- Interview Preparation
- Supervised Learning
- Unsupervised Learning
- Reinforcement Learning
- Deep Learning
- Natural Language Processing (NLP)
- Computer Vision
- Generative Adversarial Networks (GANs)
- Dimensionality Reduction
- Clustering Algorithms
- Bayesian Inference
- Time Series Analysis
- Self-Supervised Learning
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 - Garrett Thomas (UC Berkeley)
- Linear Algebra for ML - MIT OpenCourseWare
- Probability & Statistics - Stanford
- Calculus for Optimization - Khan Academy
- Python for ML - Coursera
- Optimization Techniques
- Data Preprocessing & Feature Engineering
- Model Evaluation & Metrics
- Regularization Techniques
- Loss Functions
- Activation Functions
- Hyperparameter Tuning
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Learn Python and Core Libraries
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Build a Strong Math Foundation
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Learn ML Fundamentals
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Build Practical Experience
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Specialize
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Learn MLOps
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Read Research Papers
Recommended Talks:
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:
Coursera Courses:
- GenAI for Executives & Business Leaders: An Introduction
- GenAI for Execs & Business Leaders: Integration Strategy
- GenAI for Product R&D Teams
- GenAI for Product Managers
Gemini:
- Large Multimodal Model Prompting with Gemini - DeepLearning.AI
- Gemini for Application Developers - Coursera (Google Cloud)
- Gemini CLI: Code & Create with an Open-Source Agent - DeepLearning.AI
Google ADK:
- Building Live Voice Agents with Google's ADK - DeepLearning.AI
- Understand Google Cloud Agents - Coursera
Model Context Protocol (MCP):
- Prompt Engineering Guide
- Prompt Engineering - OpenAI API
- ChatGPT Prompt Engineering for Developers - DeepLearning.AI
- Google Prompting Essentials
- Building and Evaluating Advanced RAG Applications - DeepLearning.AI
- Knowledge Graphs for RAG - DeepLearning.AI
- Building Agentic RAG with LlamaIndex - DeepLearning.AI
- Finetuning Large Language Models - DeepLearning.AI
- Generative AI Advanced Fine-Tuning for LLMs - Coursera (IBM)
- Fine-tuning & RL for LLMs: Intro to Post-Training - DeepLearning.AI
- GitHub - langchain-ai/langchain
- LangChain Documentation
- LangChain for LLM Application Development - DeepLearning.AI
- LangChain: Chat with Your Data - DeepLearning.AI
- Functions, Tools and Agents with LangChain - DeepLearning.AI
- GitHub - crewAIInc/crewAI
- CrewAI Official Site
- Multi AI Agent Systems with crewAI - DeepLearning.AI
- Practical Multi AI Agents and Advanced Use Cases with crewAI - DeepLearning.AI
- Introduction to Agent2Agent (A2A) Protocol - Google Cloud Tech
- AI Agents Series - FuturMinds (YouTube Playlist)
- Evaluating AI Agents - DeepLearning.AI
- LLMs as Operating Systems: Agent Memory - DeepLearning.AI
- AI Agentic Design Patterns with AutoGen - DeepLearning.AI
- Event-Driven Agentic Document Workflows - DeepLearning.AI
- AI Agents Fundamentals In 21 Minutes (YouTube)
- MLOps for Generative AI - Google Cloud Skill Boost
- GenAIOps: Operationalize Generative AI (YouTube)
- MLOps.org
- Full Stack Deep Learning
- Systems & Networking for AI engineers - PixelBank
- OWASP Top 10 for Large Language Model Applications
- Google's Secure AI Framework (SAIF)
- The Dawn of Agentic AI in Security Operations - Google Cloud Blog
Learning Paths on Cloud Skills Boost:
- Gemini for Google Cloud
- Beginner: Introduction to Generative AI
- Intermediate: Generative AI Labs with Gemini
- Deploy and Manage Generative AI Models
- Machine Learning Engineer Learning Path
- Build and Modernize Applications With Generative AI
- Integrate Generative AI Into Your Data Workflow
- Generate Smarter Generative AI Outputs
- Three Proven Strategies for Optimizing AI Costs - Google Cloud
- Reduce Cost and Improve Your AI Workloads - Google Cloud Blog
- Vertex AI Pricing
- Generative AI for Executives and Business Leaders Specialization - Coursera (IBM)
- GenAI for Execs & Business Leaders: Integration Strategy - Coursera (IBM)
- GenAI for Everyone - Coursera (DeepLearning.AI)
- Maximize Productivity with AI Tools - Coursera (Google)
- Google AI Professional Certificate - Coursera
- Microsoft 365 Copilot for Productivity - Coursera (Microsoft)
- Introduction to Post-Quantum Cryptography - edX (UMBC)
- Practical Introduction to Quantum-Safe Cryptography - IBM Quantum Learning
- Generative AI for Software Development Specialization - DeepLearning.AI
- AI-Powered Software Development - Coursera
- GitHub Copilot Fundamentals - Microsoft Learn
- AI For Everyone by Andrew Ng (Coursera)
- Deep Learning Specialization (Coursera)
- Machine Learning with Python (edX - IBM)
- Reinforcement Learning Specialization (Coursera)
- RL Course by David Silver
- NLP with Deep Learning - Stanford CS224n
- AWS Certified Machine Learning Engineer - Associate
- AWS Certified AI Practitioner - Skill Builder
- Microsoft Certified: Azure AI Engineer Associate
- Stanford AI and Machine Learning Certificate
- Hands-On Large Language Models - Jay Alammar & Maarten Grootendorst — GitHub (notebooks)
- Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow
- AI Engineering: Building Applications with Foundational Models
- Introduction to Machine Learning Interviews - Chip Huyen
- Designing Data-Intensive Applications
- Designing Machine Learning Systems
- Deep Learning (Goodfellow, Bengio, Courville)
- Patterns, Predictions, and Actions - Hardt & Recht (Princeton University Press) — Free textbook covering supervised learning, deep learning, causal inference, and reinforcement learning
- Attention Is All You Need (Google)
- DeepSeek R1: Incentivizing Reasoning Capability in LLMs
- Monolith: Real Time Recommendation System (TikTok/ByteDance)
- BERT: Pre-training of Deep Bidirectional Transformers
- Understanding Deep Learning Requires Rethinking Generalization
- Playing Atari with Deep Reinforcement Learning
- Distilling the Knowledge in a Neural Network
- OpenAI Key Papers in Deep RL
Research Discovery Tools:
- Ai2 Asta — Agentic research assistant by Allen Institute for AI; discovers and synthesizes literature across 200M+ papers with traceable citations
- PyTorch
- TensorFlow
- TensorFlow Playground — Browser-based neural network experimentation tool
- Scikit-Learn
- XGBoost
- Keras
- Whisper - OpenAI
- OpenAI Blog
- Google DeepMind
- Google Research
- Apple ML Research
- Amazon Science
- Microsoft AI
- Meta AI Blog
- AWS Machine Learning Blog
- NVIDIA Deep Learning Blog
- AirBnB Engineering - AI & ML
- Spotify Engineering
- Uber Engineering - AI
- Netflix Tech Blog
- Google AI Blog
Easy:
Medium:
- Single Neuron
- K-Means Clustering
- Predicting Loan Default Risk - Kaggle
- Sentiment Analysis on Movie Reviews - Kaggle
Hard:
- Decision Tree Learning
- Implement a Simple RNN with Backpropagation
- GANs for Image Synthesis - Kaggle
- ML Interviews MVP - GitHub
- 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
- ML Coding questions - PixelBank
Feel free to open a PR if you have useful resources to add.
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