In this repository, you will find the foundations of Linear Algebra necessary for Machine Learning.
Linear algebra forms the foundational framework for representing and manipulating data in the form of matrices and vectors, which are fundamental data structures in machine learning. Concepts like matrix operations, eigenvectors, eigenvalues, matrix factorisation, and linear transformations are essential for understanding algorithms such as principal component analysis (PCA), singular value decomposition (SVD), linear regression, neural networks, and more. Mastery of linear algebra is necessary to efficiently process and manipulate large datasets, design and comprehend complex models, optimise algorithms, and develop innovative solutions across diverse machine learning domains.
For the hands-on Jupyter Notebook labs, please refer to my Github repository.