Python, Matrices, and Linear Algebra for Data Science and ML
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.
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
-
RAHUL RAI
Professor at Clemson University
Rating Distribution
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- 2 stars: 0 votes
- 3 stars: 0 votes
- 4 stars: 2 votes
- 5 stars: 1 votes
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