🚀 Open SourceFree ResourcesActively Maintained
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◆211+ curated resources◆Beginner → Advanced learning paths
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AI/ML Key Concepts
15🔧
AI/ML Building Blocks
12Mathematics 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
Loss Functions
Activation Functions
Hyperparameter Tuning
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AI/ML Roadmap
241. Learn Python and Core Libraries
2. Build a Strong Math Foundation
3. Learn ML Fundamentals
4. Build Practical Experience
5. Specialize
7. Read Research Papers
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Generative AI – General
11Recommended Talks
Visual Explainers
Learning Paths
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Generative AI – Advanced
6Gemini
Google ADK
Model Context Protocol (MCP)
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Prompt Engineering
4🔍
RAG (Retrieval-Augmented Generation)
3⚙️
Fine-tuning
3🛠️
Frameworks
16LangChain
CrewAI
Google Agent Development Kit (ADK)
Agno (formerly Phidata)
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Agentic AI
10Agent 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
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
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MLOps and GenAIOps
5🔒
Security
3☁️
Google Cloud AI and ML
8Learning 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
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AI Cost Optimization
3🏢
Adopting GenAI in Organizations
3⚡
AI Tools for Productivity
3⚛️
Quantum Computing and PQC
2💻
AI Augmented SDLC
3🔮
Coming Innovations in LLMs
1🎓
Courses
10Machine 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
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Certifications
4📚
Books
8Hands-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
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Must-Read Research Papers
9Attention 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
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Tools and Frameworks
8▶️
YouTube Channels
5🔬
Research Blogs
7📝
Applied ML Blogs
7👥
Communities
3🏋️
Practice Problems
10Easy
Medium
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