Machine Learning Crash Path with Resources

ML_Crash_course.md

Machine Learning Learning Path with Resources

Essential Books

1. "Deep Learning" by Ian Goodfellow, Yoshua Bengio, and Aaron Courville
2. "Build a Large Language Model from Scratch" - Bestseller on implementing LLMs

Foundational Level

Mathematics & Statistics

• Linear Algebra Fundamentals

  • Course: 3Blue1Brown Linear Algebra Series
  • Course: MIT OpenCourseWare 18.06 Linear Algebra
  • Topics: Vectors, matrices, eigenvalues, matrix operations

• Statistics & Probability

  • Course: StatQuest with Josh Starmer
  • Resource: Khan Academy Statistics & Probability
  • Topics: Distributions, hypothesis testing, confidence intervals

Programming & Tools

• Python for ML
  • Course: Python for Data Science - freeCodeCamp
  • Libraries: NumPy, Pandas, Matplotlib tutorials
  • Practice: Kaggle Learn Python & Pandas

Intermediate Level

Machine Learning Foundations

• ML Basics
  • Course: Andrew Ng's Machine Learning Specialization
  • Course: fast.ai Practical Deep Learning
  • Topics: Linear regression, logistic regression, decision trees

Deep Learning Fundamentals

• Neural Networks
  • Course: 3Blue1Brown Neural Networks Series
  • Course: Andrej Karpathy's Zero to Hero Series
  • Topics: Backpropagation, activation functions, optimization

Advanced Level

Modern Deep Learning

• Transformer Architecture
  • Paper: Attention Is All You Need
  • Video: Yannic Kilcher's Transformer Explanation
  • Course: Stanford CS25: Transformers United

Large Language Models

• Foundation Models
  • Paper: GPT-3 Paper
  • Course: Stanford's Foundation Models Course
  • Resource: Full Stack Deep Learning

Training & Fine-tuning

• Advanced Training Methods
  • Paper: LoRA Paper
  • Paper: RLHF Paper
  • Resource: Hugging Face Course

Expert Level

Reasoning & Planning

• Advanced AI Techniques
  • Paper: Chain of Thought Paper
  • Paper: Tree of Thoughts
  • Paper: ReACT Paper

Latest Research Areas

• Cutting Edge Topics
  • Resource: Papers with Code
  • Resource: arXiv Sanity Preserver
  • Follow: Leading AI Labs' Research Blogs (DeepMind, OpenAI, Anthropic)

Additional Resources

Comprehensive Resource Collections

• History of Deep Learning
• a16z AI Canon
• Prompting Guide
• 2025 AI Engineer Reading List

Practice Platforms

• Kaggle Competitions
• Google Colab (Free GPU access)
• Hugging Face Spaces (Deploy models)

Survey Papers

• LLM Survey (2024)
• Agent Survey (2023)
• Prompt Engineering Survey (2024)

Benchmarks

• BIG-Bench
• SWE-Bench
• Chatbot Arena

Note:

1. Replace "[ ]" with "[x]" to mark topics as completed
2. Follow the order as concepts build upon each other
3. Practice with real projects alongside theoretical learning
4. Join AI communities (Discord servers, Reddit r/MachineLearning, Twitter AI community)
5. Keep up with latest developments through ML paper reading groups