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

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Python, Matrices, and Linear Algebra for Data Science and ML, available at $44.99, has an average rating of 4.5, with 54 lectures, based on 3 reviews, and has 30 subscribers.

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You will learn about 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 This course is ideal for individuals who are Students who want to learn linear algebra and python programming concepts. or Students who want to develop foundations in linear algebra for Data Science, Machine Learning, and Deep Learning domains or Anyone who is interested in learning python and wants to have a conceptual understanding of linear algebra concepts. or Data scientists and machine learning students who want to review their basics in the linear algebra domain. or Anyone who wants to learn Python for data science, machine learning, and AI domains It is particularly useful for Students who want to learn linear algebra and python programming concepts. or Students who want to develop foundations in linear algebra for Data Science, Machine Learning, and Deep Learning domains or Anyone who is interested in learning python and wants to have a conceptual understanding of linear algebra concepts. or Data scientists and machine learning students who want to review their basics in the linear algebra domain. or Anyone who wants to learn Python for data science, machine learning, and AI domains.

Enroll now: Python, Matrices, and Linear Algebra for Data Science and ML

Summary

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

Price: $44.99

Average Rating: 4.5

Number of Lectures: 54

Number of Published Lectures: 54

Number of Curriculum Items: 54

Number of Published Curriculum Objects: 54

Original Price: $89.99

Quality Status: approved

Status: Live

What You Will 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

Who Should Attend

  • 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

Target Audiences

  • 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 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).

Course Curriculum

Chapter 1: Introduction to Python

Lecture 1: Introduction: Python Features, Variable Types and Strings

Lecture 2: Python Basics: Lists and Dictionaries

Lecture 3: Python Basics: Operators

Lecture 4: Python Basics: Loops

Lecture 5: Python Basics: Functions, Classes, and Object

Lecture 6: Numpy

Lecture 7: Plots

Lecture 8: Broadcasting and Pandas

Chapter 2: Vector and Matrices – 1

Lecture 1: Introduction

Lecture 2: Vectors: Geometrical Intuition

Lecture 3: Vectors: Numerical and Mathematical Perspective

Lecture 4: Vectors Operations

Lecture 5: Matrices and Tensors

Chapter 3: Vector and Matrices – II

Lecture 1: Introduction and Concept of Functions

Lecture 2: Linear Transformation

Lecture 3: Matrix-Matrix Multiplication

Lecture 4: Determinant

Chapter 4: Vector and Matrices – III

Lecture 1: Introduction and Vector Operations

Lecture 2: Matrix Operations

Lecture 3: Linear System of Equations

Lecture 4: Matrix Inversion

Lecture 5: System of Equation: Example

Lecture 6: Rank of a Matrix

Chapter 5: Eigen Vectors and Eigen Values

Lecture 1: Geometric Intuition

Lecture 2: Numerical Computation

Lecture 3: Numerical Example and Related Concepts

Chapter 6: Vector and Matrices – IV

Lecture 1: System of Equation: Cramer’s Rule

Lecture 2: Cramer’s Rule Geometric Intuition – Part I

Lecture 3: Cramer’s Rule Geometric Intuition – Part II

Lecture 4: Orthogonal Matrix

Lecture 5: Sparse Matrices and Vector Norms

Chapter 7: Singular Value Decomposition (SVD) and Principal Component Analysis (PCA)

Lecture 1: SVD, PCA, and Dimensionality Reduction

Lecture 2: Singular Value Decomposition (SVD)

Lecture 3: SVD Example Problem

Lecture 4: SVD: Dimensionality Reduction and Properties

Lecture 5: SVD: Matrix Approximation

Lecture 6: Principal Component Analysis (PCA): Geometric Intuition

Lecture 7: Principal Component Analysis

Lecture 8: PCA Example

Lecture 9: PCA through SVD

Chapter 8: Vector and Matrices in Python

Lecture 1: Introduction, Determinant, Matrix Multiplication and Inversion

Lecture 2: Rank, Matrix Addition, Transpose and Trace

Lecture 3: Vector Dot and Cross Product and Solving System of Linear Equations

Lecture 4: Orthogonal and Sparse Matrix

Lecture 5: Vector Norms

Lecture 6: Eigen Value Part 1: Map Coloring Problem

Lecture 7: Eigen Value Part 2

Lecture 8: Eigen Value Part 3

Chapter 9: SVD and PCA in Python

Lecture 1: Introduction and SVD of a simple Matrix

Lecture 2: SVD: Image Compression

Lecture 3: PCA of a simple Matrix

Lecture 4: PCA: Complex Dataset

Lecture 5: PCA- Iris Dataset Part- I

Lecture 6: PCA- Iris Dataset Part- II

Instructors

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  • 5 stars: 1 votes

Frequently Asked Questions


How long do I have access to the course materials?


You can view and review the lecture materials indefinitely, like an on-demand channel.


Can I take my courses with me wherever I go?


Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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