I've completed courses related to linear algebra and basic statistics. Since I had time to explore more during the summer break, I did quite some practice in python regarding linear algebra, statistics, linear regression, etc., to prepare myself for machine learning classes. I gleaned all the questions I've done (both in python and by hand), singled out the most representative, and compiled them into this document as a tutorial for those new to Jupyter. I hope those who are interested will find this document helpful!
This document demonstrates the usage of the most fundamental functions involved in matrix operations in numpy and their associated caveats.
Table of Contents:
1. Introduction (we are here)
2. Import numpy
3. Broadcasting Example
3.1 example 1
3.2 example 2
3.3 example 3
4. matrix in numpy
4.1 Transpose
4.2 Matrix Multiplication
4.3 Matrix-vector multiplication (Ax = b)
4.4 Inverse of matrices
4.5 Linear regression
4.6 Eigenvalues & Eigenvectors
5. Descriptive statistics
5.1 Mean
5.2 Variance
5.3 Standard Deviation
5.4 Covariance