Linear Algebra for Data Science Machine Learning in Python

Linear Algebra for Data Science & Machine Learning in Python, available at $69.99, has an average rating of 4.2, with 150 lectures, 55 quizzes, based on 10 reviews, and has 63 subscribers.

You will learn about Fundamentals of Linear Algebra Applications of Vectors and Matrices with implementation in Python Operations on Vectors and Matrices with implementation in Python Solve Systems of Linear Equations and implementation in Python Matrix Factorization and implementation in Python Computation of Eigenvalues, Eigenvectors Singular Value Decomposition with its implementation in Python Eigen Decomposition with their implementation in Python This course is ideal for individuals who are Anyone who is curious about how Linear Algebra is used in Machine Learning or Anyone who wants to understand Maths and Linear Algebra behind Data Science or Anyone who wants to develop fundamental foundations for deployment of Machine Learning Techniques It is particularly useful for Anyone who is curious about how Linear Algebra is used in Machine Learning or Anyone who wants to understand Maths and Linear Algebra behind Data Science or Anyone who wants to develop fundamental foundations for deployment of Machine Learning Techniques.

Enroll now: Linear Algebra for Data Science & Machine Learning in Python

Summary

Title: Linear Algebra for Data Science & Machine Learning in Python

Price: $69.99

Average Rating: 4.2

Number of Lectures: 150

Number of Quizzes: 55

Number of Published Lectures: 150

Number of Published Quizzes: 55

Number of Curriculum Items: 205

Number of Published Curriculum Objects: 205

Original Price: ?5,499

Quality Status: approved

Status: Live

What You Will Learn

Fundamentals of Linear Algebra

Applications of Vectors and Matrices with implementation in Python

Operations on Vectors and Matrices with implementation in Python

Solve Systems of Linear Equations and implementation in Python

Matrix Factorization and implementation in Python

Computation of Eigenvalues, Eigenvectors

Singular Value Decomposition with its implementation in Python

Eigen Decomposition with their implementation in Python

Who Should Attend

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

Target Audiences

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

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….

Course Curriculum

Chapter 1: Introduction

Lecture 1: What you are going to learn in this course

Lecture 2: Introduction

Lecture 3: What is Linear Algebra?

Lecture 4: Why Linear Algebra?

Chapter 2: Getting Started with Python

Lecture 1: Installing Python

Lecture 2: Installing Jupyter Notebook

Chapter 3: Vectors

Lecture 1: Scalars and Vectors

Lecture 2: Vectors in 2-Dimensional Space

Lecture 3: Vectors in 3-Dimensional Space

Lecture 4: Vectors with n-Components

Lecture 5: Python Code – Creating Vectors

Lecture 6: Python Code – Creating Vectors using arange()

Lecture 7: Python Code – Accessing and Modifying Vectors

Lecture 8: Zero and Ones Vectors

Lecture 9: Python Code – Zero and Ones Vector

Lecture 10: Quiz Solutions

Chapter 4: Operations on Vectors

Lecture 1: Vector Addition

Lecture 2: Python Code – Vector Addition

Lecture 3: Scalar Multiplication

Lecture 4: Python Code – Scalar Multiplication

Lecture 5: Vector Properties

Lecture 6: Linear Combinations of Vectors

Lecture 7: Python Code – Linear Combinations of Vectors

Lecture 8: Vector Transpose

Lecture 9: Python Code – Vector Transpose

Lecture 10: Dot Product or Inner Product

Lecture 11: Python Code – Dot Product

Lecture 12: Outer Product

Lecture 13: Python Code – Outer Product

Lecture 14: Quiz Solutions

Chapter 5: Matrices

Lecture 1: Matrices – Context of Data Science

Lecture 2: Dimension or Size

Lecture 3: Python Code – Creating Matrices using array()

Lecture 4: Python Code – Creating Matrices using mat()

Lecture 5: Python Code – Accessing and Modifying Elements

Lecture 6: Matrix Transpose

Lecture 7: Python Code – Matrix Transpose

Lecture 8: Symmetric Matrix

Lecture 9: Python Code – Symmetric Matrix

Lecture 10: Identity Matrices

Lecture 11: Python Code – Identity Matrices

Lecture 12: Diagonal Matrix

Lecture 13: Python Code – Diagonal Matrix

Lecture 14: Triangular Matrix

Lecture 15: Zero and Ones Matrix

Lecture 16: Python Code – Zero and Ones Matrix

Lecture 17: Quiz Solutions

Chapter 6: Operations on Matrices

Lecture 1: Matrix Addition

Lecture 2: Python Code – Matrix Addition

Lecture 3: Scalar Multiplication

Lecture 4: Python Code – Scalar Multiplication

Lecture 5: Hadamard Product

Lecture 6: Python Code – Hadamard Product

Lecture 7: Trace of Matrix

Lecture 8: Python Code – Trace of Matrix

Lecture 9: Matrix Multiplication

Lecture 10: Python Code – Matrix Multiplication

Lecture 11: Properties of Matrix Operations

Lecture 12: Matrix Power

Lecture 13: Python Code – Matrix Power

Lecture 14: Diagonal Matrix Multiplication

Lecture 15: Python Code – Diagonal Matrix Multiplication

Lecture 16: Quiz Solutions

Chapter 7: Matrix Determinant and Inverse

Lecture 1: Determinants

Lecture 2: Python Code – Determinants

Instructors

Syed Mohiuddin
Instructor

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4 stars: 5 votes

5 stars: 4 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!