Linear Algebra for Data Science and Machine Learning using R

Linear Algebra for Data Science and Machine Learning using R, available at $34.99, has an average rating of 5, with 160 lectures, 58 quizzes, based on 3 reviews, and has 37 subscribers.

You will learn about Fundamentals of Linear Algebra Applications of Matrices, Vectors and operations on Matrices and Vectors with implementation in R Solve Systems of Linear Equations and implementation in R Matrix Factorization and implementation in R Computation of Eigenvalues, Eigenvectors and Eigen Decomposition with their implementation in R Solving Least Squares problems Singular Value Decomposition with its implementation in R 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 and Machine Learning using R

Summary

Title: Linear Algebra for Data Science and Machine Learning using R

Price: $34.99

Average Rating: 5

Number of Lectures: 160

Number of Quizzes: 58

Number of Published Lectures: 160

Number of Published Quizzes: 58

Number of Curriculum Items: 218

Number of Published Curriculum Objects: 218

Original Price: ?5,499

Quality Status: approved

Status: Live

What You Will Learn

Fundamentals of Linear Algebra

Applications of Matrices, Vectors and operations on Matrices and Vectors with implementation in R

Solve Systems of Linear Equations and implementation in R

Matrix Factorization and implementation in R

Computation of Eigenvalues, Eigenvectors and Eigen Decomposition with their implementation in R

Solving Least Squares problems

Singular Value Decomposition with its implementation in R

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 R, where you will learn all about how to setup the R 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 R code demos and solved problems apart from the theoretical concepts of Linear Algebra.

You will also learn how to use the R’s pracma, matrixcalc 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 R

Lecture 1: Installing R Software

Lecture 2: Installing RStudio

Lecture 3: Look around RStudio Interface

Lecture 4: Help & Examples Facility for R Features and Functions

Lecture 5: Changing Look and Feel of RStudio

Lecture 6: Some General Functions Good to Know

Lecture 7: Writing R Program using RGui

Lecture 8: Writing R Program using RStudio

Lecture 9: Using Comments in R Scripts

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: R Code – Creating Vectors

Lecture 6: R Code – Create Vectors using Sequence Operator & Function

Lecture 7: R Code – Accessing & Modifying Vectors

Lecture 8: Zero and Ones Vectors

Lecture 9: R Code – Zero and Ones Vector

Lecture 10: Quiz Solutions

Chapter 4: Operations on Vectors

Lecture 1: Vector Addition

Lecture 2: R Code – Vector Addition

Lecture 3: Scalar Multiplication

Lecture 4: R Code – Scalar Multiplication

Lecture 5: Vector Properties

Lecture 6: Linear Combinations of Vectors

Lecture 7: R Code – Linear Combinations of Vectors

Lecture 8: Vector Transpose

Lecture 9: R Code – Vector Transpose

Lecture 10: Dot Product or Inner Product

Lecture 11: R Code – Dot Product

Lecture 12: Outer Product

Lecture 13: R Code – Outer Product

Lecture 14: Quiz Solutions

Chapter 5: Matrices

Lecture 1: Matrices – Context of Data Science

Lecture 2: Dimension or Size

Lecture 3: R Code – Creating Matrices

Lecture 4: R Code – Matrix Functions

Lecture 5: R Code – Naming Rows and Columns of Matrix

Lecture 6: R Code – Accessing and Modifying Elements of Matrix

Lecture 7: R Code – Appending Rows and Columns

Lecture 8: R Code – Deleting Rows and Columns of Matrix

Lecture 9: R Code – Creating Matrix using rbind() and cbind()

Lecture 10: Matrix Transpose

Lecture 11: R Code – Matrix Transpose

Lecture 12: Symmetric Matrix

Lecture 13: R Code – Symmetric Matrix

Lecture 14: Identity Matrices

Lecture 15: R Code – Identity Matrices

Lecture 16: Diagonal Matrix

Lecture 17: R Code – Diagonal Matrix

Lecture 18: Triangular Matrix

Lecture 19: Zero and Ones Matrix

Lecture 20: R Code – Zero and Ones Matrix

Lecture 21: Quiz Solutions

Chapter 6: Operations on Matrices

Lecture 1: Matrix Addition

Lecture 2: R Code – Matrix Addition

Lecture 3: Scalar Multiplication

Lecture 4: R Code – Scalar Multiplication

Lecture 5: Hadamard Product

Lecture 6: R Code – Hadamard Product

Lecture 7: Trace of Matrix

Lecture 8: R Code – Trace of Matrix

Lecture 9: Matrix Multiplication

Instructors

Syed Mohiuddin
Instructor

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5 stars: 3 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!