I’m not good at math, is this course still for me?

<text variant="body1">Yes! We want to break down the barriers that hold people back from advancing their math skills. In this course, we flip the traditional mathematics pedagogy for teaching math, starting with the real world use-cases and working back to theory. <text variant="body1">Most people who are good at math simply have more practice doing math, and through that, more comfort with the mindset needed to be successful. This course is the perfect place to start or advance those fundamental skills, and build the mindset required to be good at math.

Do I need to take the courses in a specific order?

No. Most learners would benefit from taking courses one and two together, as they introduce concepts that build upon each other, but course three is independent from the other courses in this specialization.

Will I earn university credit for completing the Specialization?

No, this Specialization is not for college credit.

Is this a standalone course or a Specialization?

Linear Algebra for Machine Learning and Data Science can be taken as a standalone course or the first course in the Mathematics for Machine Learning and Data Science Specialization.

Can I apply for financial aid?

Yes, Coursera provides financial aid to learners who cannot afford the fee.

What will I get if I subscribe to this Specialization?

When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.

Is financial aid available?

Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

Linear Algebra for Machine Learning and Data Science

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Linear Algebra for Machine Learning and Data Science

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

Instructor: Luis Serrano

197,491 already enrolled

4 modules

Gain insight into a topic and learn the fundamentals.

2,315 reviews

Intermediate level

Recommended experience

Flexible schedule

3 weeks at 10 hours a week

Learn at your own pace

95%

Most learners liked this course

4 modules

Gain insight into a topic and learn the fundamentals.

2,315 reviews

Intermediate level

Recommended experience

Flexible schedule

3 weeks at 10 hours a week

Learn at your own pace

95%

Most learners liked this course

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

Skills you'll gain

Tools you'll learn

Details to know

Shareable certificate

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Assessments

9 assignments

Taught in English

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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

Newly updated for 2024! Mathematics for Machine Learning and Data Science is a foundational online program created by DeepLearning.AI and taught by Luis Serrano. In machine learning, you apply math concepts through programming. And so, in this specialization, you’ll apply the math concepts you learn using Python programming in hands-on lab exercises. As a learner in this program, you'll need basic to intermediate Python programming skills to be successful.

After completing this course, you will be able to: • Represent data as vectors and matrices and identify their properties using concepts of singularity, rank, and linear independence, etc. • Apply common vector and matrix algebra operations like dot product, inverse, and determinants • Express certain types of matrix operations as linear transformations • Apply concepts of eigenvalues and eigenvectors to machine learning problems Many machine learning engineers and data scientists need help with mathematics, and even experienced practitioners can feel held back by a lack of math skills. This Specialization uses innovative pedagogy in mathematics to help you learn quickly and intuitively, with courses that use easy-to-follow visualizations to help you see how the math behind machine learning actually works. We recommend you have a high school level of mathematics (functions, basic algebra) and familiarity with programming (data structures, loops, functions, conditional statements, debugging). Assignments and labs are written in Python but the course introduces all the machine learning libraries you’ll use.

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 videos8 readings3 assignments1 app item2 ungraded labs

14 videos Total 78 minutes

Specialization introduction 8 minutes
Course introduction 1 minute
What to expect and how to succeed 1 minute
A note on programming experience 1 minute
Linear Algebra Applied I 6 minutes
Linear Algebra Applied II 7 minutes
System of sentences 5 minutes
System of equations 13 minutes
System of equations as lines and planes 12 minutes
A geometric notion of singularity 3 minutes
Singular vs non-singular matrices 5 minutes
Linear dependence and independence 7 minutes
The determinant 8 minutes
Conclusion 0 minutes

8 readings Total 72 minutes

Join the DeepLearning.AI Forum to ask questions, get support, or share amazing ideas! 2 minutes
Notations 10 minutes
Learning Python: Recommended Resources 10 minutes
Check your knowledge 10 minutes
Interactive Tool: Graphical Representation of Linear Systems with 2 variables 10 minutes
Interactive Tool: System of Equations as Planes (3x3) 10 minutes
(Optional) Downloading your Notebook and Refreshing your Workspace 10 minutes
Week 1 - Slides 10 minutes

3 assignments Total 210 minutes

Practice Quiz 1 60 minutes
Practice Quiz 2 30 minutes
Graded quiz 120 minutes

1 app item Total 1 minute

Intake Survey 1 minute

2 ungraded labs Total 120 minutes

Introduction to NumPy Arrays 60 minutes
Linear Systems as Matrices 60 minutes

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 assignments1 programming assignment1 ungraded lab

12 videos Total 49 minutes

Solving non-singular system of linear equations 7 minutes
Solving singular system of linear equations 3 minutes
Solving system of equations with more variables 3 minutes
Matrix row-reduction 4 minutes
Row operations that preserve singularity 4 minutes
The rank of a matrix 6 minutes
The rank of a matrix in general 2 minutes
Row echelon form 3 minutes
Row echelon form in general 4 minutes
Reduced row echelon form 4 minutes
The Gaussian Elimination Algorithm 9 minutes
Conclusion 1 minute

5 readings Total 45 minutes

Check your knowledge 10 minutes
Interactive Tool: Graphical Representation of Linear Systems with 3 variables 10 minutes
(Optional) Assignment Troubleshooting Tips 10 minutes
(Optional) Partial Grading for Assignments 10 minutes
Week 2 - Slides 5 minutes

2 assignments Total 150 minutes

Practice Quiz 30 minutes
Graded Quiz 120 minutes

1 programming assignment Total 240 minutes

Gaussian Elimination 240 minutes

1 ungraded lab Total 30 minutes

Introduction to the Numpy.linalg sub-library 30 minutes

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 assignments1 programming assignment3 ungraded labs

14 videos Total 54 minutes

Machine Learning Motivation 8 minutes
Vectors and their properties 5 minutes
Vector operations 3 minutes
The dot product 4 minutes
Geometric Dot Product 3 minutes
Multiplying a matrix by a vector 3 minutes
Matrices as linear transformations 3 minutes
Linear transformations as matrices 2 minutes
Matrix multiplication 6 minutes
The identity matrix 1 minute
Matrix inverse 3 minutes
Which matrices have an inverse? 2 minutes
Neural networks and matrices 9 minutes
Conclusion 1 minute

3 readings Total 25 minutes

Check your knowledge 10 minutes
Interactive Tool: Linear Transformations 10 minutes
Week 3 - Slides 5 minutes

2 assignments Total 90 minutes

Practice Quiz 30 minutes
Graded Quiz 60 minutes

1 programming assignment Total 240 minutes

Linear Transformations and Neural Networks 240 minutes

3 ungraded labs Total 180 minutes

Vector Operations: Scalar Multiplication, Sum and Dot Product of Vectors 60 minutes
Matrix Multiplication 60 minutes
Linear Transformations 60 minutes

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 videos7 readings2 assignments1 programming assignment1 ungraded lab

20 videos Total 99 minutes

Week 4 Introduction 5 minutes
Singularity and rank of linear transformations 4 minutes
Determinant as an area 3 minutes
Determinant of a product 4 minutes
Determinants of inverses 3 minutes
Bases in Linear Algebra 3 minutes
Span in Linear Algebra 9 minutes
Eigenbases 3 minutes
Eigenvalues and Eigenvectors 6 minutes
Calculating Eigenvalues and Eigenvectors 9 minutes
On the Number of Eigenvectors 9 minutes
Dimensionality Reduction and Projection 8 minutes
Motivating PCA 3 minutes
Variance and Covariance 7 minutes
Covariance Matrix 8 minutes
PCA - Overview 5 minutes
PCA - Why It Works 4 minutes
PCA - Mathematical Formulation 2 minutes
Discrete Dynamical Systems 5 minutes
Conclusion 0 minutes

7 readings Total 65 minutes

Check your knowledge 10 minutes
Interactive Tool: Linear Span 10 minutes
Week 4 - Slides 5 minutes
How is your course experience so far? 10 minutes
Reading: Textbooks and resources 10 minutes
References 10 minutes
Acknowledgments 10 minutes

2 assignments Total 121 minutes

Practice Quiz 1 minute
Graded Quiz 120 minutes

1 programming assignment Total 120 minutes

Application of Eigenvalues and Eigenvectors: Webpage navigation model and PCA 120 minutes

1 ungraded lab Total 60 minutes

Interpreting Eigenvalues and Eigenvectors 60 minutes

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Instructor

Instructor ratings

(741 ratings)

Luis Serrano

DeepLearning.AI

4 Courses 240,937 learners

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DeepLearning.AI

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Reviewed on Jun 21, 2023

A bit more detail into the complex topics of eigen values and eigen vectors would have been helpful. Also notebooks could have been oriented more towards the practical use of the concepts.

Reviewed on Aug 26, 2024

While people focus on teaching how to solve problems basically, It is very good to see people speak about maths like science as a concept with good visualization!. Great work guys.

Reviewed on May 27, 2023

Best Visual Explanation, I've got new thinking of the same things which I had learned in the Past. It great Course Thanks for making Such Amazing Content.

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Frequently asked questions

This is a beginner-friendly course, aiming to teach the concepts covered with minimal background knowledge necessary. If you're familiar with the concepts of linear algebra, you'll find this course a good review for the next course in the specialization, Calculus for Machine Learning and Data Science.

Yes! We want to break down the barriers that hold people back from advancing their math skills. In this course, we flip the traditional mathematics pedagogy for teaching math, starting with the real world use-cases and working back to theory.

Most people who are good at math simply have more practice doing math, and through that, more comfort with the mindset needed to be successful. This course is the perfect place to start or advance those fundamental skills, and build the mindset required to be good at math.

Linear algebra (matrices, vectors, and their applications)

To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. You can try a Free Trial instead, or apply for Financial Aid. The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.