April 29, 2026

Finish the last programming assignment for week 4 of the Mathematics for ML(Linear Algebra)

Completed Python crash course book

Completed linear algebra part in Mathematics for ML and got a certificate

Learned about Variance and Covariance and how they related to spread in a dataset

Learned about Covariance matrix and how they can be used with Eigenvalues and Eigenvectors for dimensionality reduction

Learned about projections as a linear transformation(projecting a higher dimension dataset to lower dimention)

Learn about Principal Component Analysis(PCA) which can be used to reduce dimensionality of datasets without losing too much information

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Finish the week 1 programming labs, ran out of study time while doing it yesterday

Completed week 4 assignment of the Mathematics for ML(Linear Algebra)

Completed week 1 of Mathematics for ML(Calculus)

Got a mathematics for Mathematics for ML certificate(first certificate for the year)

Linear Algebra Notes: Linear Algebra Notes.pdf