Python, Matrices, and Linear Algebra for Data Science and Machine Learning

Course Description

This course introduces students to essential concepts of linear algebra and python that are necessary as a foundation for learning concepts in data science and machine learning. The emphasis has been on creating lectures in a format that provides both geometrical intuitions and computational implementation of all the important concepts in linear algebra. Additionally, all the covered concepts are implemented and discussed in the python programming context. The following topics will be covered:

1. Introduction to Python

2. Vector and Matrices in Data Science and Machine Learning

3. Vector and Matrices Operations

4. Computing Eigenvalues

5. Computing Singular Values

6. Matrix Operations in Machine Learning Algorithm

7. Python Data Science and Machine Learning Libraries

Who this course is for:

• Students who want to learn linear algebra and python programming concepts

• Students who want to develop foundations in linear algebra for Data Science, Machine Learning, and Deep Learning domains

• Anyone who is interested in learning python and wants to have a conceptual understanding of linear algebra concepts.

• Data scientists and machine learning students who want to review their basics in the linear algebra domain

• Anyone who wants to learn Python for data science, machine learning, and AI domain

This course is taught by professor Rahul Rai who joined the Department of Automotive Engineering in 2020 as Dean’s Distinguished Professor in the Clemson University International Centre for Automotive Research (CU-ICAR). Previously, he served on the Mechanical and Aerospace Engineering faculty at the University at Buffalo-SUNY (2012-2020) and has experience in industrial research center experiences at United Technology Research Centre (UTRC) and Palo Alto Research Centre called as (PARC).