Learn AI & Machine Learning
π Start Your AI Learning Journey
Whether you're a complete beginner or looking to deepen your expertise, this curated collection of resources will help you master AI and machine learning concepts.
οΏ½ What's New in 2025
Latest AI Learning Resources
| Resource | Type | Description |
|---|---|---|
| Anthropic's Claude Prompt Engineering | Guide | Official guide to getting the best from Claude |
| OpenAI o1 Reasoning Models | Docs | Understanding chain-of-thought reasoning |
| Google Gemini API | Tutorial | Build with Google's multimodal AI |
| AI Engineering Guide | Course | The emerging AI Engineer role explained |
| MCP (Model Context Protocol) | Docs | New standard for AI tool integration |
οΏ½π± Beginner-Friendly Courses
Free Courses
| Course | Provider | Duration | Description |
|---|---|---|---|
| Machine Learning | Stanford/Coursera | 11 weeks | Andrew Ng's legendary ML course - the perfect starting point |
| Deep Learning Specialization | deeplearning.ai | 3 months | Five courses covering neural networks, CNNs, RNNs, and more |
| fast.ai Practical Deep Learning | fast.ai | 7 weeks | Top-down approach to deep learning with PyTorch |
| CS50's Introduction to AI | Harvard | 7 weeks | Explore AI concepts through hands-on projects |
| Google Machine Learning Crash Course | 15 hours | Quick introduction to ML with TensorFlow |
Interactive Learning
- Kaggle Learn β Free micro-courses on Python, ML, Deep Learning, and more
- Google Colab β Free GPU access for running ML notebooks
- Hugging Face Course β Learn NLP and transformers from the experts
π₯ Large Language Models (LLMs)
Understanding LLMs
| Resource | Type | Description |
|---|---|---|
| Andrej Karpathy's Neural Networks: Zero to Hero | Video Series | Build a GPT from scratch |
| LLM University by Cohere | Course | Comprehensive LLM education |
| State of GPT | Talk | Karpathy explains how ChatGPT works |
| Attention Is All You Need | Paper | The foundational Transformer paper |
Building with LLMs
- LangChain Documentation β Framework for LLM applications
- OpenAI Cookbook β Practical examples using OpenAI APIs
- Prompt Engineering Guide β Master the art of prompting
πΌοΈ Generative AI & Diffusion Models
| Resource | Type | Focus |
|---|---|---|
| Diffusion Models from Scratch | Video | Understand stable diffusion |
| AUTOMATIC1111 Wiki | Guide | Stable Diffusion Web UI |
| ComfyUI Examples | Guide | Node-based image generation |
| Midjourney Documentation | Docs | Official Midjourney guide |
π Essential Reading
Books
| Title | Author | Level |
|---|---|---|
| Hands-On Machine Learning | AurΓ©lien GΓ©ron | Beginner-Intermediate |
| Deep Learning | Goodfellow, Bengio, Courville | Intermediate-Advanced |
| Pattern Recognition and Machine Learning | Christopher Bishop | Advanced |
| The Hundred-Page Machine Learning Book | Andriy Burkov | Beginner |
Research Papers (Must-Reads)
- Attention Is All You Need β Introduced the Transformer architecture
- BERT β Bidirectional pre-training for NLP
- GPT-3 β Language models are few-shot learners
- CLIP β Connecting text and images
- Stable Diffusion β High-resolution image synthesis
π οΈ Hands-On Practice
Platforms
- Kaggle β Competitions, datasets, and community notebooks
- Papers With Code β Research papers with implementation code
- Hugging Face Hub β Models, datasets, and spaces
- Replicate β Run ML models in the cloud
Project Ideas
- Build a chatbot using OpenAI API or Ollama
- Fine-tune an LLM on your own data with LoRA
- Create an image classifier with PyTorch or TensorFlow
- Generate images with Stable Diffusion locally
- Build a RAG system with LangChain and vector databases
πΊ YouTube Channels
| Channel | Focus |
|---|---|
| 3Blue1Brown | Visual math explanations, neural network series |
| Andrej Karpathy | Deep learning, building GPT from scratch |
| Two Minute Papers | AI research summaries |
| Yannic Kilcher | In-depth paper explanations |
| StatQuest | Statistics and ML fundamentals |
πΊοΈ Learning Roadmaps
Path 1: ML Engineer
Python β Statistics β ML Basics β Deep Learning β MLOps β Production Systems
Path 2: Data Scientist
Python β Statistics β Data Analysis β ML β Visualization β Domain Expertise
Path 3: AI Researcher
Math β Deep Learning β Research Papers β Novel Experiments β Publications
Path 4: LLM Developer
Python β Transformers β Prompt Engineering β RAG β Fine-tuning β Agents
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