Phase 1 — Python & Math
April 28, 2026
Continue learning Mathematics for ML and Python crash course
What I Did
Completed week 4 of the linear algebra part in Mathematics for ML
What I Learned
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
Bugs & Blockers
None
Concepts That Need More Time
N/A
Tomorrow
Finish the last programming assignment for week 4 of the Mathematics for ML(Linear Algebra)
Continue reading my Python crash course book(I only need to do the project part)
Wins
Completed week 4 of the Mathematics for ML(Linear Algebra)