## Course overview

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.

## Key skills

- Defining linear systems using linear algebra
- Employing intermediate machine learning techniques

## Course outline

### Linear Algebra For Machine Learning [4 lessons]

### Linear Systems 1h

Lesson Objectives- Represent a problem as a linear system
- Solve linear systems by elimination

### Vectors 1h

Lesson Objectives- Define vectors using geometry
- Perform vector operations
- Identify the link between linear combinations and solutions to linear systems

### Matrix Algebra 1h

Lesson Objectives- Perform matrix operations in NumPy
- Define the matrix inverse and transpose
- Solve the matrix inverse in higher dimensions

### Solution Sets 1h

Lesson Objectives- Identify the different solution sets to linear systems
- Define homogenous and nonhomogenous systems

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## Master skills faster with Dataquest

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