An intermediate-level course that introduces the mathematical foundations to derive Principal Component Analysis (PCA), a fundamental dimensionality reduction technique. It covers basic statistics of data sets, such as mean values and variances and the computation of distances and angles between vectors using inner products.
Build the mathematical foundation for ML with free courses from MIT, 3Blue1Brown, and fast.ai. Covers linear algebra, calculus, statistics, and probability.
Start your journey into machine learning pipelines with foundational concepts and hands-on exercises designed for newcomers.
Build the mathematical foundation essential for ML. Master linear algebra, calculus, probability, and statistics—the core concepts powering every machine learning algorithm.
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