Linear Algebra for Data Science & Machine Learning in Python

Description

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….

Who this course is for:

• Anyone who is curious about how Linear Algebra is used in Machine Learning
• Anyone who wants to understand Maths and Linear Algebra behind Data Science
• Anyone who wants to develop fundamental foundations for deployment of Machine Learning Techniques

Requirements

• You should have familiarity with fundamentals of Maths
• All the implementation of Linear Algebra concepts are in Python, so familiarity with Python will be an added advantage