Foundations
Set up your environment, then master Python, NumPy, Mathematics, and Probability — the essential building blocks for both Computer Vision and Machine Learning.
Recommended Study Path
Environment
Chapter 0
- OS & Terminal Setup
- VS Code & Python Install
- Git, Jupyter & Docker
Get your development tools ready
Programming
Chapter 1
- Python Core Language
- Data Structures & OOP
- NumPy/Pandas & Scikit-learn Intro
Essential Python for AI/ML
Data Skills
Chapter 2
- NumPy Arrays & Broadcasting
- Pandas DataFrames
- Data Cleaning & Preparation
Master numerical computing
Mathematics
Chapters 3-5
- Linear Algebra & Calculus
- Probability & Statistics
- Advanced Linear Algebra
Essential math for ML/CV/NLP
All Chapters
Development Setup (Optional)
Set up the ideal ML development environment: OS, terminal, VS Code, Python, Git, Jupyter, Docker, and AI coding assistants.
Python Foundations
Essential Python programming for AI: variables, data structures, control flow, functions, classes, NumPy/Pandas basics, and scikit-learn introduction.
NumPy & Data Wrangling
Master NumPy arrays, Pandas DataFrames, broadcasting, reshaping, statistics, linear algebra, and data preprocessing techniques.
Mathematical Foundations
Essential mathematics: linear algebra, probability & statistics, information theory, calculus for optimization, matrix decompositions, and PCA.
Probability & Statistics
Deep dive into probability theory, distributions, estimation, hypothesis testing, Bayesian inference, and sampling methods for ML.
Linear Algebra
Deep dive into vector spaces, orthogonality, projections, least squares, spectral theory, and numerical methods for ML/CV/NLP.
Where to Go Next
Master these foundations before moving on to the CV and ML study plans.