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

Q: Why is mathematics for machine learning and data science important?

With out-of-the-box tools, it’s easier than ever to begin a career as a machine learning engineer or data scientist. But to advance deeper in your career, create efficient models, troubleshoot algorithms, and incorporate creative thinking, a deeper understanding of the mathematics behind the models is needed.

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

Instructor: Luis Serrano

205,773 already enrolled

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4 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

3 weeks to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

4 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

3 weeks to complete

at 10 hours a week

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

Skills you'll gain

Tools you'll learn

Details to know

Shareable certificate

Add to your LinkedIn profile

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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Learn new concepts from industry experts
Gain a foundational understanding of a subject or tool
Develop job-relevant skills with hands-on projects
Earn a shareable career certificate

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 videosTotal 78 minutes

Specialization introduction8 minutes
Course introduction1 minute
What to expect and how to succeed1 minute
A note on programming experience1 minute
Linear Algebra Applied I6 minutes
Linear Algebra Applied II7 minutes
System of sentences5 minutes
System of equations13 minutes
System of equations as lines and planes12 minutes
A geometric notion of singularity3 minutes
Singular vs non-singular matrices5 minutes
Linear dependence and independence7 minutes
The determinant8 minutes
Conclusion0 minutes

8 readingsTotal 72 minutes

Join the DeepLearning.AI Forum to ask questions, get support, or share amazing ideas!2 minutes
Notations10 minutes
Learning Python: Recommended Resources10 minutes
Check your knowledge10 minutes
Interactive Tool: Graphical Representation of Linear Systems with 2 variables10 minutes
Interactive Tool: System of Equations as Planes (3x3)10 minutes
(Optional) Downloading your Notebook and Refreshing your Workspace10 minutes
Week 1 - Slides10 minutes

3 assignmentsTotal 210 minutes

Practice Quiz 160 minutes
Practice Quiz 230 minutes
Graded quiz120 minutes

1 app itemTotal 1 minute

Intake Survey1 minute

2 ungraded labsTotal 120 minutes

Introduction to NumPy Arrays60 minutes
Linear Systems as Matrices60 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 videosTotal 49 minutes

Solving non-singular system of linear equations7 minutes
Solving singular system of linear equations3 minutes
Solving system of equations with more variables3 minutes
Matrix row-reduction4 minutes
Row operations that preserve singularity4 minutes
The rank of a matrix6 minutes
The rank of a matrix in general2 minutes
Row echelon form3 minutes
Row echelon form in general4 minutes
Reduced row echelon form4 minutes
The Gaussian Elimination Algorithm9 minutes
Conclusion1 minute

5 readingsTotal 45 minutes

Check your knowledge10 minutes
Interactive Tool: Graphical Representation of Linear Systems with 3 variables10 minutes
(Optional) Assignment Troubleshooting Tips10 minutes
(Optional) Partial Grading for Assignments10 minutes
Week 2 - Slides5 minutes

2 assignmentsTotal 150 minutes

Practice Quiz30 minutes
Graded Quiz120 minutes

1 programming assignmentTotal 240 minutes

Gaussian Elimination240 minutes

1 ungraded labTotal 30 minutes

Introduction to the Numpy.linalg sub-library30 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 videosTotal 54 minutes

Machine Learning Motivation8 minutes
Vectors and their properties5 minutes
Vector operations3 minutes
The dot product4 minutes
Geometric Dot Product3 minutes
Multiplying a matrix by a vector3 minutes
Matrices as linear transformations3 minutes
Linear transformations as matrices2 minutes
Matrix multiplication6 minutes
The identity matrix1 minute
Matrix inverse3 minutes
Which matrices have an inverse?2 minutes
Neural networks and matrices9 minutes
Conclusion1 minute

3 readingsTotal 25 minutes

Check your knowledge10 minutes
Interactive Tool: Linear Transformations10 minutes
Week 3 - Slides5 minutes

2 assignmentsTotal 90 minutes

Practice Quiz30 minutes
Graded Quiz60 minutes

1 programming assignmentTotal 240 minutes

Linear Transformations and Neural Networks240 minutes

3 ungraded labsTotal 180 minutes

Vector Operations: Scalar Multiplication, Sum and Dot Product of Vectors60 minutes
Matrix Multiplication60 minutes
Linear Transformations60 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 videosTotal 99 minutes

Week 4 Introduction5 minutes
Singularity and rank of linear transformations4 minutes
Determinant as an area3 minutes
Determinant of a product4 minutes
Determinants of inverses3 minutes
Bases in Linear Algebra3 minutes
Span in Linear Algebra9 minutes
Eigenbases3 minutes
Eigenvalues and Eigenvectors6 minutes
Calculating Eigenvalues and Eigenvectors9 minutes
On the Number of Eigenvectors9 minutes
Dimensionality Reduction and Projection8 minutes
Motivating PCA3 minutes
Variance and Covariance7 minutes
Covariance Matrix8 minutes
PCA - Overview5 minutes
PCA - Why It Works4 minutes
PCA - Mathematical Formulation2 minutes
Discrete Dynamical Systems5 minutes
Conclusion0 minutes

7 readingsTotal 65 minutes

Check your knowledge10 minutes
Interactive Tool: Linear Span10 minutes
Week 4 - Slides5 minutes
How is your course experience so far?10 minutes
Reading: Textbooks and resources10 minutes
References10 minutes
Acknowledgments10 minutes

2 assignmentsTotal 121 minutes

Practice Quiz1 minute
Graded Quiz120 minutes

1 programming assignmentTotal 120 minutes

Application of Eigenvalues and Eigenvectors: Webpage navigation model and PCA120 minutes

1 ungraded labTotal 60 minutes

Interpreting Eigenvalues and Eigenvectors60 minutes

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Instructor

Luis Serrano

DeepLearning.AI

4 Courses252,689 learners

Offered by

DeepLearning.AI

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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.