Awesome AI/ML Roadmap

This repository contains a roadmap to transition to AI/ML engineering.

🚗 AI/ML Roadmap

1. Build a Strong Math Foundation

   • Linear Algebra
   • Probability & Statistics
   • Calculus

2. Learn Python and Core Libraries

   • Intro Python
   • Advanced Python
   • NumPy: Numerical computing and arrays
   • Pandas: Data manipulation and analysis
   • Matplotlib & Seaborn: Data visualization
   • scikit-learn: Implement ML algorithms

3. Learn Machine Learning Fundamentals

   • Google Machine Learning Crash Course
   • Machine Learning by Andrew Ng
   • Read Hundred-Page ML Book

4. Building Practical Experience

   • Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow
   • Practical Deep Learning for Coders
   • Structured Machine Learning Projects
   • Build GPT

5. Deepen Knowledge in Specialized Areas

   • Natural Language Processing
   • Reinforcement Learning
   • Computer Vision
   • Deep Learning
   • Transformers

6. Learn about MLOps

   • Intro to MLOps
   • Three levels of ML
   • Fullstackdeeplearning

7. AI Research and Continuous Learning

   • ArXiv for Research Papers

8. Prepare for AI/ML Job Interviews

   • Introduction to Machine Learning Interviews
   • Designing Machine Learning Systems