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Set up your environment, then master Python, NumPy, Mathematics, and Probability — the essential building blocks for both Computer Vision and Machine Learning.
Chapter 0
Get your development tools ready
Chapter 1
Essential Python for AI/ML
Chapter 2
Master numerical computing
Chapters 3-5
Essential math for ML/CV/NLP
Set up the ideal ML development environment: OS, terminal, VS Code, Python, Git, Jupyter, Docker, and AI coding assistants.
Essential Python programming for AI: variables, data structures, control flow, functions, classes, NumPy/Pandas basics, and scikit-learn introduction.
Master NumPy arrays, Pandas DataFrames, broadcasting, reshaping, statistics, linear algebra, and data preprocessing techniques.
Essential mathematics: linear algebra, probability & statistics, information theory, calculus for optimization, matrix decompositions, and PCA.
Deep dive into probability theory, distributions, estimation, hypothesis testing, Bayesian inference, and sampling methods for ML.
Deep dive into vector spaces, orthogonality, projections, least squares, spectral theory, and numerical methods for ML/CV/NLP.
Master these foundations before moving on to the CV and ML study plans.