Everything AI/ML

Curated learning resources

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Everything AI/ML

A curated cheatsheet of learning resources for Generative AI, Machine Learning, Agentic AI, Prompt Engineering, RAG, Fine-tuning, MLOps, and more.

◆30 sections◆223+ curated resources◆Beginner → Advanced learning paths

🧠

AI/ML Key Concepts

15

Supervised Learning

Unsupervised Learning

Reinforcement 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

Interactive visual explanations of core ML concepts

In-browser interactive explainer for Convolutional Neural Networks

Transformer Explainer

Interactive visualization of the Transformer architecture

🔧

AI/ML Building Blocks

12

Mathematics for Machine Learning (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

Activation Functions

Hyperparameter Tuning

🗺️

AI/ML Roadmap

24

1. Learn Python and Core Libraries

Intro Python – Harvard CS50

Advanced Python – Harvard AI with Python

NumPy Quickstart

Pandas Tutorial

Matplotlib Tutorials

Scikit-learn Tutorial

2. Build a Strong Math Foundation

Linear Algebra – MIT

Probability & Statistics – Stanford

Multivariable Calculus – Khan Academy

3. Learn ML Fundamentals

Google ML Crash Course

Machine Learning by Andrew Ng

The Hundred-Page ML Book

4. Build Practical Experience

Practical Deep Learning for Coders – fast.ai

Structured ML Projects – Coursera

Build GPT from Scratch – Karpathy

5. Specialize

NLP Course – Hugging Face

Deep RL Course – Hugging Face

Computer Vision – Kaggle

Deep Learning – CS231n Stanford

Computer Vision, LLM, VLM Courses – PixelBank

6. Learn MLOps

Three Levels of ML Software

Full Stack Deep Learning

7. Read Research Papers

Preprint server for ML and AI research

✨

Generative AI – General

11

Recommended Talks

Andrej Karpathy – How I Use LLMs

Andrej Karpathy – Intro to Large Language Models (1hr)

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 AIBeginner

Intermediate: Gemini for Google CloudIntermediate

Advanced: Generative AI for DevelopersAdvanced

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

🚀

Generative AI – Advanced

6

Gemini

Large Multimodal Model Prompting with Gemini – DeepLearning.AI

Gemini for Application Developers – Coursera

Gemini CLI: Code & Create with an Open-Source Agent

Google ADK

Building Live Voice Agents with Google's ADK – DeepLearning.AI

Understand Google Cloud Agents – Coursera

Model Context Protocol (MCP)

Intro to Model Context Protocol (MCP) – Coursera (Scrimba)NEW

💬

Prompt Engineering

10

Prompt Engineering Guide

Comprehensive guide to prompt engineering techniques

Prompt Engineering – OpenAI API

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

Google 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

🔍

RAG (Retrieval-Augmented Generation)

3

Building and Evaluating Advanced RAG Applications – DeepLearning.AI

Knowledge Graphs for RAG – DeepLearning.AI

Building Agentic RAG with LlamaIndex – DeepLearning.AI

⚙️

Fine-tuning

3

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

🛠️

Frameworks

16

LangChain

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

LangGraph

GitHub – langchain-ai/langgraph

LangGraph Tutorials

AI Agents in LangGraph – DeepLearning.AI

CrewAI

GitHub – crewAIInc/crewAI

CrewAI Official Site

Multi AI Agent Systems with crewAI – DeepLearning.AI

Practical Multi AI Agents and Advanced Use Cases with crewAI

Google Agent Development Kit (ADK)

Google ADK Documentation

GitHub – google/adk-python

Agno (formerly Phidata)

Agno Documentation – Agents

GitHub – agno-agi/agno

🤖

Agentic AI

13

Agent Skills – Open Standard for Extending AI Agent CapabilitiesNEW

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 SkillNEW

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 SkillNEW

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 AgentsNEW

Operational state and coordination layer for agent fleets with multimodal retrieval, Git-style branching, and object-storage-native deployment

Introduction to Agent2Agent (A2A) Protocol – Google Cloud

AI Agents Series – FuturMinds (YouTube Playlist)

Evaluating AI Agents – DeepLearning.AI

LLMs as Operating Systems: Agent Memory – DeepLearning.AI

AI Agents in LangGraph – DeepLearning.AI

AI Agentic Design Patterns with AutoGen – DeepLearning.AI

Multi AI Agent Systems with crewAI – DeepLearning.AI

Building Agentic RAG with LlamaIndex – DeepLearning.AI

Event-Driven Agentic Document Workflows – DeepLearning.AI

⚡

MLOps and GenAIOps

5

MLOps for Generative AI – Google Cloud Skill Boost

GenAIOps: Operationalize Generative AI (YouTube)

Full Stack Deep Learning

Systems & Networking for AI Engineers – PixelBank

🔒

Security

3

OWASP Top 10 for Large Language Model Applications

Google's Secure AI Framework (SAIF)

The Dawn of Agentic AI in Security Operations – Google Cloud

☁️

Google Cloud AI and ML

8

Learning Paths on Cloud Skills Boost

Gemini for Google Cloud

Beginner: Introduction to Generative AIBeginner

Intermediate: Generative AI Labs with GeminiIntermediate

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

💰

AI Cost Optimization

3

Three Proven Strategies for Optimizing AI Costs – Google Cloud

Reduce Cost and Improve Your AI Workloads – Google Cloud

Vertex AI Pricing

🏢

Adopting GenAI in Organizations

3

Generative AI for Executives and Business Leaders Specialization – Coursera (IBM)

GenAI for Execs & Business Leaders: Integration Strategy – Coursera

GenAI for Everyone – Coursera (DeepLearning.AI)

⚡

AI Tools for Productivity

3

Maximize Productivity with AI Tools – Coursera (Google)

Google AI Professional Certificate – Coursera

Microsoft 365 Copilot for Productivity – Coursera (Microsoft)

⚛️

Quantum Computing and PQC

2

Introduction to Post-Quantum Cryptography – edX (UMBC)

Practical Introduction to Quantum-Safe Cryptography – IBM Quantum

💻

AI Augmented SDLC

3

Generative AI for Software Development Specialization – DeepLearning.AI

AI-Powered Software Development – Coursera

GitHub Copilot Fundamentals – Microsoft Learn

🔮

Coming Innovations in LLMs

1

Google DeepMind Blog

Latest research and announcements from Google DeepMind

🎓

Courses

10

Machine Learning by Andrew Ng (Coursera)

AI For Everyone by Andrew Ng (Coursera)

Deep Learning Specialization (Coursera)

Machine Learning with Python (edX – IBM)

Reinforcement Learning Specialization (Coursera)

CS231n: CNNs for Visual Recognition (Stanford)

RL Course by David Silver

NLP with Deep Learning – Stanford CS224n

Practical Deep Learning for Coders – fast.ai

CV, LLM, VLM Courses – PixelBank

🏆

Certifications

4

AWS Certified Machine Learning Engineer – Associate

AWS Certified AI Practitioner – Skill Builder

Microsoft Certified: Azure AI Engineer Associate

Stanford AI and Machine Learning Certificate

📚

Books

8

Hands-On Large Language Models – Jay Alammar & Maarten Grootendorst

GitHub notebooks available

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

Free textbook covering supervised learning, deep learning, causal inference, and RL

📄

Must-Read Research Papers

11

Attention Is All You Need (Google)

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

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

Agentic research assistant by Allen Institute for AI; discovers and synthesizes literature across 200M+ papers

🔨

Tools and Frameworks

8

TensorFlow Playground

Browser-based neural network experimentation tool

Whisper – OpenAI

Check if your hardware can run AI models locally

▶️

YouTube Channels

5

Stanford Online

Andrej Karpathy

🔬

Research Blogs

7

Google DeepMind

Google Research

Apple ML Research

📝

Applied ML Blogs

7

AWS Machine Learning Blog

NVIDIA Deep Learning Blog

AirBnB Engineering – AI & ML

Spotify Engineering

Uber Engineering – AI

Netflix Tech Blog

👥

Communities

3

r/LearnMachineLearning

Chip Huyen MLOps Discord

Hugging Face Discord

🏋️

Practice Problems

10

Easy

Matrix times VectorEasy

Titanic: ML from Disaster – KaggleEasy

Predicting House Prices – KaggleEasy

Medium

Single NeuronMedium

K-Means ClusteringMedium

Predicting Loan Default Risk – KaggleMedium

Sentiment Analysis on Movie Reviews – KaggleMedium

Hard

Decision Tree LearningHard

Implement a Simple RNN with BackpropagationHard

GANs for Image Synthesis – KaggleHard

🎯

Interview Preparation

6

Introduction to Machine Learning Interviews – Chip Huyen

ML Interviews MVP – GitHub

Designing Machine Learning 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

ML Coding Questions – PixelBank