Linear Algebra for Data Science and Machine Learning in Urdu (Codanics)

A 56-lesson, roughly 10-hour Urdu series on the linear algebra behind machine learning. Short lessons cover vectors, unit vectors, scaling and addition, vector spaces, span and linear dependence, dot and cross products, basis vectors, and matrices as linear transformations including shear. The later lessons repeat the ideas in Python, ending with linear transformations and singular value decomposition (SVD) in code. Useful for students who want the maths of ML explained in plain Urdu before tackling algorithms like PCA or neural networks.