Mathematics for Machine Learning and Data Science Specialization

Mathematical foundations for machine learning — linear algebra, multivariable calculus and gradient descent, probability, and inferential statistics.

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Skills covered

A 3-course specialization from DeepLearning.AI, taught by Luis Serrano. Builds the mathematical toolkit that the rest of the DeepLearning.AI catalogue assumes.

• Course 1 — Linear Algebra for Machine Learning and Data Science: systems of linear equations, vector spaces, matrix operations, determinants, eigenvalues and eigenvectors, applications to ML such as PCA intuition
• Course 2 — Calculus for Machine Learning and Data Science: derivatives in one and many variables, partial derivatives, the chain rule, gradient descent (and its variants) as the optimization backbone of ML
• Course 3 — Probability & Statistics for Machine Learning and Data Science: probability distributions, joint and conditional distributions, Bayes’ theorem, sampling, descriptive and inferential statistics, hypothesis testing, p-values, confidence intervals
• Hands-on labs throughout in Python / NumPy tying each concept back to ML use cases

Issued

DeepLearning.AI — 2025-01-07