In this course, you’ll learn the linear algebra concepts behind machine learning systems like neural networks and backpropagation to train deep learning neural networks.
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Linear Algebra is a key branch of mathematics that is concerned with vectors, matrices, planes, and lines, and it helps to build blocks of machine learning algorithms.
In this course, you’ll learn how to define linear systems using linear algebra, how to represent a problem as a linear system, and how to solve linear systems by elimination.
You’ll learn how to define vectors using geometry, as well as how to perform vector operations and identify the link between linear combinations and solutions to linear systems. You’ll also learn how to perform matrix operations in NumPy, how to define the matrix inverse and transpose, and how to solve the matrix inverse in higher dimensions.
Finally, you’ll learn to identify the different solution sets to linear systems and define homogeneous and nonhomogeneous systems.
Best of all, you’ll learn by doing — you’ll practice and get feedback directly in the browser.
Defining linear systems using linear algebra
Employing intermediate machine learning techniquesLoading lessons...
Dataquest has helped thousands of people start new careers in data. If you put in the work and follow our course, you'll master data skills and grow your career.
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