Foundations

ML Fundamentals

Neural networks, backpropagation, gradient descent — the maths behind AI

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ML Maths Basics

Manipulate vectors and matrices

Supervised Learning

Train decision trees, random forests, and neural nets

Unsupervised Learning

Apply k-means and DBSCAN clustering

Engineer features and handle missing data

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