Phase 1 — Python & Math
April 29, 2026
Finish assignment for Mathematics for ML(Linear Algebra) and Python crash course and start the Calculus course
What I Did
- 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
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 week 1 programming labs, ran out of study time while doing it yesterday
Wins
- 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)
Notes
- Linear Algebra Notes: Linear Algebra Notes.pdf