Python, Matrices, and Linear Algebra for Data Science and ML

Name: Python, Matrices, and Linear Algebra for Data Science and ML
Rating: 4.4 (5 reviews)

This course introduces students to essential concepts of linear algebra and python

Created byRAHUL RAI

Last updated 6/2022

English

What you'll learn

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

Course content

9 sections • 54 lectures • 8h 22m total length

Introduction: Python Features, Variable Types and Strings10:00
Python Basics: Lists and Dictionaries12:44
Explore Python basics with lists and dictionaries, learning how to append elements, index and slice lists, and modify values while mapping keys to values like stock prices.
Python Basics: Operators13:25
Python Basics: Loops7:36
Python Basics: Functions, Classes, and Object6:30
Learn to define and call Python functions with def, pass arguments including lists, and reuse code; then explore classes, objects, instantiation, and accessing methods like greet.
Numpy17:06
Plots5:46
Explore plotting with Matplotlib in Python, including creating sine and cosine curves, customizing labels, colors, and line styles, and building subplots to visualize results.
Broadcasting and Pandas10:24
Learn broadcasting in numpy by adding a vector to each row of a matrix. Then use pandas to read a csv and split features from price for regression.

System of Equation: Cramer’s Rule5:29
Cramer’s Rule Geometric Intuition – Part I7:00
Immerse in the geometric intuition of cramer's rule by solving Ax = v as finding x that maps to v under a transformation, using 2d parallelograms and determinants.
Cramer’s Rule Geometric Intuition – Part II6:59
Orthogonal Matrix10:11
Sparse Matrices and Vector Norms10:45
Discover sparse matrices and efficient storage with structures like dictionary of keys, lists of lists, and compressed sparse row, and apply L1, L2, and infinity norms in ML.

SVD, PCA, and Dimensionality Reduction7:25
Singular Value Decomposition (SVD)5:06
SVD Example Problem5:47
SVD: Dimensionality Reduction and Properties15:23
SVD: Matrix Approximation9:51
Principal Component Analysis (PCA): Geometric Intuition10:15
Explore the geometric intuition of PCA by projecting data onto the primary and secondary axes to maximize variance, minimize covariance, and enable dimensionality reduction.
Principal Component Analysis9:57
PCA Example10:42
PCA through SVD6:32

Requirements

no prerequisites

Description

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).

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 domains

Python, Matrices, and Linear Algebra for Data Science and ML

What you'll learn

Explore related topics

Course content

Introduction to Python8 lectures • 1hr 24min

Vector and Matrices - 15 lectures • 47min

Vector and Matrices - II4 lectures • 46min

Vector and Matrices - III6 lectures • 55min

Eigen Vectors and Eigen Values3 lectures • 30min

Vector and Matrices - IV5 lectures • 40min

Singular Value Decomposition (SVD) and Principal Component Analysis (PCA)9 lectures • 1hr 21min

Vector and Matrices in Python8 lectures • 1hr 9min

SVD and PCA in Python6 lectures • 51min

Requirements

Description

Who this course is for: