English | Size: 2.04 GB
Genre: eLearning
Vectors, Matrices, Systems of Linear Equations, Factorization, Eigenvectors, Least Squares, SVD
What you’ll 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
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;
Vectors & Operations on Vectors
Matrices & Operations on Matrices
Determinant and Inverse
Solving Systems of Linear Equations
Norms & Basis Vectors
Linear Independence
Matrix Factorization
Orthogonality
Eigenvalues and Eigenvectors
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….
Who this course is for:
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
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