Math for AI & ML
The math behind every ML algorithm — built in code, not on paper
5 phases. 22 lessons. 22 labs. 1 capstone. The mathematical foundations of AI and ML — linear algebra, calculus, probability, optimization, and advanced math — every concept implemented in NumPy and PyTorch, not just written on paper. You build a personal math library with tested, visualized modules for every concept, and graduate understanding exactly what happens inside every ML algorithm you'll ever use.
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- Labs: —
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- Level: Beginner
Curriculum
- Linear Algebra — Vectors, Matrices & Spaces — Vectors and vector spaces, matrices and matrix operations, matrix transformations and eigenvalues, norms and distances, singular value decomposition, tensor operations
- Calculus & Optimization — Derivatives and gradients, chain rule and automatic differentiation, gradient descent family (batch, stochastic, mini-batch, momentum), convex optimization, numerical stability
- Probability & Statistics — Probability distributions, Bayes' theorem and statistical thinking, sampling methods, statistics for ML (mean, variance, covariance, correlation)
- Advanced Math — Information, Dimensions & Signals — Information theory (entropy, KL divergence, cross-entropy), dimensionality reduction (PCA, t-SNE, UMAP), Fourier transform for signal processing, graph theory for ML
- Math to Code — Implementation Capstone — Implementing math in NumPy and PyTorch, linear systems and matrix factorization, complex numbers for signal processing, stochastic processes
Skills You Will Learn
- Linear Algebra Implementation
- Calculus & Automatic Differentiation
- Probability & Statistics
- Optimization Algorithms
- Information Theory
- Dimensionality Reduction
- Numerical Stability
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