Linear Algebra is an important field of mathematics, and it’s essential for understanding how many machine learning algorithms actually work. In our Linear Algebra for machine learning course, you will learn the linear algebra concepts behind machine learning systems like neural networks and the backpropagation to train deep learning neural networks.
You'll learn concepts such as linear systems. You will learn how to represent a problem as a linear system as well as how to solve it by elimination. You will also build up an intuition for the geometry behind vectors and how to perform vector operations.
Then, you’ll dig into matrix algebra and how to perform matrix operations using NumPy. You will also learn how to calculate the inverse of a matrix as well as what it means to be the transpose of a matrix. To wrap up this linear algebra course, you will learn about the different types of solution sets for a linear system. Along with learning about solution sets, you will also learn the difference between a homogeneous and nonhomogeneous system.
After you complete this course, you can feel confident that you know the necessary calculus fundamentals for intermediate machine learning techniques.
By the end of this course, you'll be able to:
Learn Linear Algebra for Data Science
Linear Systems
Learn how to use matrices to solve systems of linear functions.
Vectors
Learn the visual intuition using vectors.
Matrix Algebra
Learn about the different matrix operations.
Solution Sets
Learn about the different types of solution sets for a linear system.