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OBJECTIVE: learn NumPy and the linear algebra and probability that AI uses — through 4 mini projects (marks analyzer, movie recommender, photo filters, probability lab). WHAT WE COVER: • Arrays: shape, dtype, indexing, slicing, boolean masks • Aggregations along axes, broadcasting and z-scores • Vectors, dot product, cosine similarity and distances • Matrix multiplication for recommendations • Images as arrays, filters, transformations and SVD (glimpse) • Random simulation: expectation, variance, Law of Large Numbers, Central Limit Theorem, Bayes AFTER THIS MODULE YOU CAN: • Work with numerical data fast in NumPy • Explain vectors, similarity and matrix operations with code • Simulate probability questions instead of guessing
Total: 2.8 h of applied study listed above.