Linear Algebra for Data Science & Machine Learning in Python

About Linear Algebra for Data Science & Machine Learning in Python

This course will help you in understanding of the

Linear Algebra and math’s behind Data Science and Machine Learning

.

Linear Algebra

is the fundamental part of

Data Science and Machine Learning

. This course consists of lessons on each topic of

Linear Algebra + the code

or implementation of the Linear Algebra concepts or topics. There’re tons of topics in this course. To begin the course:

We have a discussion on

_what is Linear Algebra and Why we need Linear Algebra_

Then we move on to Getting Started with Python, where you will learn all about how to setup the Python environment, so that it’s easy for you to have a hands-on experience. Then we get to the essence of this course; 1.

_Vectors & Operations on Vectors_

2.

_Matrices & Operations on Matrices_

3.

_Determinant and Inverse_

4.

_Solving Systems of Linear Equations_

5.

_Norms & Basis Vectors_

6.

_Linear Independence_

7.

_Matrix Factorization_

8.

_Orthogonality_

9.

_Eigenvalues and Eigenvectors_

10.

_Singular Value Decomposition (SVD)_

Again, in each of these sections you will find Python code demos and solved problems apart from the theoretical concepts of Linear Algebra. You will also learn how to use the

Python's numpy

library which contains numerous functions for matrix computations and solving

Linear Algebric

problems.

_So, let’s get started…._