DeepLearning.AI
Linear Algebra for Machine Learning and Data Science
DeepLearning.AI

Linear Algebra for Machine Learning and Data Science

This course is part of Mathematics for Machine Learning and Data Science Specialization

Taught in English

Some content may not be translated

Luis Serrano

Instructor: Luis Serrano

94,253 already enrolled

Course

Gain insight into a topic and learn the fundamentals

4.5

(1,214 reviews)

|

94%

Beginner level

Recommended experience

34 hours (approximately)
Flexible schedule
Learn at your own pace

What you'll learn

  • Represent data as vectors and matrices and identify their properties using concepts of singularity, rank, and linear independence

  • Apply common vector and matrix algebra operations like dot product, inverse, and determinants

  • Express certain types of matrix operations as linear transformation, and apply concepts of eigenvalues and eigenvectors to machine learning problems

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Assessments

9 quizzes

Course

Gain insight into a topic and learn the fundamentals

4.5

(1,214 reviews)

|

94%

Beginner level

Recommended experience

34 hours (approximately)
Flexible schedule
Learn at your own pace

See how employees at top companies are mastering in-demand skills

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This course is part of the Mathematics for Machine Learning and Data Science Specialization
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There are 4 modules in this course

Matrices are commonly used in machine learning and data science to represent data and its transformations. In this week, you will learn how matrices naturally arise from systems of equations and how certain matrix properties can be thought in terms of operations on system of equations.

What's included

14 videos7 readings3 quizzes1 app item2 ungraded labs

In this week, you will learn how to solve a system of linear equations using the elimination method and the row echelon form. You will also learn about an important property of a matrix: the rank. The concept of the rank of a matrix is useful in computer vision for compressing images.

What's included

12 videos5 readings2 quizzes1 programming assignment1 ungraded lab

An individual instance (observation) of data is typically represented as a vector in machine learning. In this week, you will learn about properties and operations of vectors. You will also learn about linear transformations, matrix inverse, and one of the most important operations on matrices: the matrix multiplication. You will see how matrix multiplication naturally arises from composition of linear transformations. Finally, you will learn how to apply some of the properties of matrices and vectors that you have learned so far to neural networks.

What's included

14 videos3 readings2 quizzes1 programming assignment3 ungraded labs

In this final week, you will take a deeper look at determinants. You will learn how determinants can be geometrically interpreted as an area and how to calculate determinant of product and inverse of matrices. We conclude this course with eigenvalues and eigenvectors. Eigenvectors are used in dimensionality reduction in machine learning. You will see how eigenvectors naturally follow from the concept of eigenbases.

What's included

20 videos8 readings2 quizzes1 programming assignment1 ungraded lab

Instructor

Instructor ratings
4.7 (454 ratings)
Luis Serrano
DeepLearning.AI
3 Courses109,284 learners

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DeepLearning.AI

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