Learn AI & Machine Learning

Coming Soon

Build intelligent systems that learn from data. From classical machine learning algorithms through deep neural networks to modern LLM applications — understand how AI works and build with it.

This course covers ML fundamentals, PyTorch, transformer architectures, RAG pipelines, and deploying AI-powered applications in production.

Start Here — Learning Roadmap

A suggested path from zero to mastery. Follow these steps in order:

1. Learn Python and math foundations — Ensure solid Python skills plus basics of linear algebra, calculus, and probability/statistics
2. Understand classical ML algorithms — Learn linear/logistic regression, decision trees, SVMs, and k-means clustering with scikit-learn
3. Master model evaluation — Understand train/test splits, cross-validation, precision/recall, ROC curves, and overfitting vs underfitting
4. Build neural networks from scratch — Implement forward/backward propagation, gradient descent, and loss functions to understand the fundamentals
5. Learn PyTorch — Build models with tensors, autograd, nn.Module, DataLoaders, and training loops in the dominant DL framework
6. Study deep learning architectures — Understand CNNs for vision, RNNs/LSTMs for sequences, and the attention mechanism
7. Master transformers and LLMs — Learn self-attention, positional encoding, tokenization, and how GPT/BERT architectures work
8. Fine-tune and prompt-engineer — Adapt pre-trained models with LoRA/QLoRA, write effective prompts, and use few-shot learning
9. Build RAG applications — Create retrieval-augmented generation pipelines with embeddings, vector databases, and LangChain/LlamaIndex
10. Deploy AI in production — Serve models with vLLM/TGI, build APIs, handle inference at scale, and monitor model performance

Official & Core Documentation

• PyTorch Tutorials — Official deep learning tutorials from basics to advanced models (All levels)
• Hugging Face Documentation — Transformers, datasets, model hub, and inference API reference (Intermediate)
• TensorFlow Documentation — Google’s ML framework with tutorials, guides, and pre-trained models (Intermediate)
• OpenAI API Documentation — GPT, DALL-E, and embeddings API reference and cookbook (Intermediate)
• Anthropic API Documentation — Claude API, tool use, prompt engineering guides, and best practices (Intermediate)
• LangChain Documentation — LLM application framework for chains, agents, and RAG pipelines (Intermediate)
• LlamaIndex Documentation — Data framework for building RAG and LLM-powered applications (Intermediate)
• scikit-learn User Guide — Classical ML algorithms with theory, examples, and parameter tuning (Beginner)
• AI Engineer Roadmap — Visual step-by-step guide to the AI engineering learning path (Beginner)

GitHub Awesome Lists & Curated Collections

• awesome-machine-learning — ML frameworks, libraries, and software organized by language (66k+ stars)
• awesome-deep-learning — Deep learning tutorials, projects, and communities (24k+ stars)
• Awesome-LLM — Curated list of Large Language Model resources, papers, and tools (22k+ stars)
• awesome-generative-ai — Generative AI tools, works, models, and references (6k+ stars)
• awesome-production-machine-learning — Open source libraries to deploy, monitor, version, and scale ML (17k+ stars)
• llm-course — Free roadmap and Colab notebooks to get into Large Language Models (42k+ stars)
• awesome-ml-courses — Free machine learning and AI courses with video lectures

Interactive Courses & Hands-On Platforms

Free Courses

• fast.ai — Practical deep learning course, free and project-first with PyTorch and fastai (Intermediate)
• Google Machine Learning Crash Course — Free ML fundamentals with TensorFlow exercises (Beginner)
• Hugging Face NLP Course — Free NLP with Transformers, hands-on with real models and datasets (Intermediate)
• DeepLearning.AI Short Courses — Free bite-sized courses on LLMs, RAG, prompt engineering, and AI agents (All levels)
• Learn PyTorch (Zero to Mastery) — Hands-on PyTorch learning with code-first approach and notebooks (Beginner)

University & MOOC Courses

• Stanford CS229 — Machine Learning — Andrew Ng’s legendary ML course with free lecture notes and videos (Intermediate)
• Stanford CS231n — CNNs for Visual Recognition — Deep learning for computer vision with assignments (Advanced)
• Stanford CS224n — NLP with Deep Learning — NLP course covering word vectors through transformers (Advanced)
• MIT 6.S191 — Introduction to Deep Learning — Fast-paced intro to deep learning with TensorFlow labs (Intermediate)
• Duke — Introduction to Machine Learning — Free self-paced course covering CNNs, RNNs, and NLP with PyTorch (Beginner)

Practice & Challenges

• Kaggle Competitions — Real ML challenges with datasets, notebooks, and leaderboards (All levels)
• LLM Course Notebooks — Free Colab notebooks for fine-tuning, quantization, and RAG (Intermediate)
• Hugging Face Spaces — Deploy and share ML demos with Gradio or Streamlit for free (Intermediate)

Video Courses & YouTube Channels

Structured Course Playlists

• Andrej Karpathy — Neural Networks: Zero to Hero — Build GPT from scratch, the best deep learning lecture series available (Intermediate)
• 3Blue1Brown — Neural Networks — Visual, intuitive neural network explanations (Beginner)
• DeepLearning.AI (Andrew Ng) — ML courses, LLM tutorials, and industry interviews (All levels)
• freeCodeCamp — Machine Learning with Python — Full ML course covering neural networks, CNNs, and NLP (Beginner)

Individual Creators & Channels

• Yannic Kilcher — ML paper reviews and in-depth explanations of cutting-edge research (Advanced)
• Two Minute Papers — Accessible AI research summaries with visual demonstrations (Beginner)
• AI Explained — Clear breakdowns of the latest AI developments, papers, and benchmarks (Intermediate)
• StatQuest with Josh Starmer — ML and statistics concepts explained simply with animations (Beginner)
• Jeremy Howard (fast.ai) — Practical deep learning insights from the fast.ai creator (Intermediate)

Books & Long-Form Reading

Free Online Books

• Deep Learning (Goodfellow et al.) — The foundational deep learning textbook, free to read online (Advanced)
• Speech and Language Processing (3rd Ed.) — Free draft by Jurafsky & Martin covering NLP through transformers and LLMs (Advanced)
• Dive into Deep Learning — Interactive deep learning textbook with code in PyTorch, TensorFlow, and JAX (Intermediate)
• Neural Networks and Deep Learning — Free online book covering backpropagation and deep network training (Intermediate)

Essential Paid Books

• Build a Large Language Model (From Scratch) — Understand LLMs by building a transformer with PyTorch (Advanced, Paid)
• AI Engineering — Chip Huyen’s guide to building AI systems in production (Advanced, Paid)
• Hands-On Machine Learning (3rd Ed.) — Practical ML with scikit-learn, Keras, and TensorFlow (Intermediate, Paid)
• Natural Language Processing with Transformers — By Hugging Face engineers, covers fine-tuning and inference (Intermediate, Paid)
• The LLM Engineer’s Handbook — Prompt engineering, fine-tuning, RAG, and deployment patterns (Intermediate, Paid)

Community, Practice & News

Forums & Discussion

• Hugging Face Community — Forum for Transformers, model sharing, and ML discussion
• r/MachineLearning — Research papers, discussions, and industry news (2.8M+ members)
• r/LocalLLaMA — Active community for running and fine-tuning open-source LLMs locally
• MLOps Community — Community for ML engineering, deployment, and production best practices

Newsletters & Blogs

• The Batch (DeepLearning.AI) — Andrew Ng’s weekly AI newsletter with curated news and original articles
• Ahead of AI — Sebastian Raschka’s newsletter on ML, deep learning, and AI research
• Latent Space — Newsletter, podcast, and community for AI engineers covering LLMs and tooling
• TLDR AI — Daily newsletter delivering AI, ML, and data science updates (620k+ subscribers)
• The Rundown AI — Daily AI newsletter covering developments, trends, and practical applications (750k+ subscribers)

Ecosystem Resources

• Papers With Code — ML papers with code implementations, benchmarks, and state-of-the-art leaderboards
• Kaggle Discussion Forums — Competition strategies, data science Q&A, and notebook sharing
• Arxiv Sanity — Browse, search, and filter ML research papers from arXiv

Tools & Environments

• Google Colab — Free cloud notebooks with GPU/TPU access for training models
• Hugging Face Model Hub — Repository of 500k+ pre-trained models ready for inference and fine-tuning
• Weights & Biases — ML experiment tracking, visualization, and model registry (free for individuals)
• Ollama — Run open-source LLMs locally on your machine with a simple CLI
• LM Studio — Desktop app for downloading and running LLMs locally with a chat interface
• vLLM — High-throughput LLM serving engine with PagedAttention for production inference
• Gradio — Build and share ML demos with a web interface in a few lines of Python
• Lightning AI — Platform for building, training, and deploying AI models with free GPU access

Cheat Sheets & Quick References

• PyTorch Cheat Sheet — Official quick reference for tensors, autograd, models, and training loops
• Machine Learning Cheat Sheet (Stanford) — Comprehensive visual reference for ML algorithms and concepts from CS229
• Deep Learning Cheat Sheet (Stanford) — Visual reference for CNNs, RNNs, training tips, and optimization from CS230
• Transformer Architecture Diagram — Jay Alammar’s visual guide explaining attention and transformer internals step by step
• Prompt Engineering Guide — Comprehensive guide to prompt engineering techniques for LLMs

Research & Staying Current

• Papers With Code — State of the Art — Track state-of-the-art results across ML benchmarks and tasks
• Arxiv Sanity — Browse, search, and filter the latest ML research papers from arXiv
• Distill.pub — Clear, visual explanations of machine learning concepts through interactive articles
• The Gradient — Long-form essays on AI research, policy, and industry trends from Stanford researchers
• Sebastian Raschka’s Blog — In-depth articles on deep learning, LLMs, and research methodology