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:
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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 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;
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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 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
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Syed Mohiuddin
Instructor
Rating Distribution
- 1 stars: 1 votes
- 2 stars: 0 votes
- 3 stars: 0 votes
- 4 stars: 5 votes
- 5 stars: 4 votes
Frequently Asked Questions
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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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