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:
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We have a discussion on what is Linear Algebra and Why we need Linear Algebra
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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;
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Vectors & Operations on Vectors
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Matrices & Operations on Matrices
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Determinant and Inverse
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Solving Systems of Linear Equations
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Norms & Basis Vectors
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Linear Independence
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Matrix Factorization
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Orthogonality
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Eigenvalues and Eigenvectors
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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
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Syed Mohiuddin
Instructor
Rating Distribution
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- 4 stars: 0 votes
- 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!
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