Will I earn university credit for completing the Specialization?

This is a non-credit Specialization.

Is this course really 100% online? Do I need to attend any classes in person?

This course is completely online, so there’s no need to show up to a classroom in person. You can access your lectures, readings and assignments anytime and anywhere via the web or your mobile device.

Can I just enroll in a single course?

Yes! To get started, click the course card that interests you and enroll. You can enroll and complete the course to earn a shareable certificate. When you subscribe to a course that is part of a Specialization, you’re automatically subscribed to the full Specialization. Visit your learner dashboard to track your progress.

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.

Can I take the course for free?

No, you cannot take this course for free. 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. If you cannot afford the fee, you can apply for financial aid.

Mathematics for Machine Learning Specialization

Mathematics for Machine Learning Specialization

Q: What will I be able to do upon completing the Specialization?

At the end of this Specialization you will have gained the prerequisite mathematical knowledge to continue your journey and take more advanced courses in machine learning.

Mathematics for Machine Learning. Learn about the prerequisite mathematics for applications in data science and machine learning

Instructors: David Dye

Included with Coursera Plus

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3 course series

Get in-depth knowledge of a subject

4.6

(13,272 reviews)

Beginner level

No prior experience required

1 month at 10 hours a week

Flexible schedule

Earn a career credential

Share your expertise with employers

3 course series

Get in-depth knowledge of a subject

4.6

(13,272 reviews)

Beginner level

No prior experience required

1 month at 10 hours a week

Flexible schedule

Earn a career credential

Share your expertise with employers

What you'll learn

For a lot of higher level courses in Machine Learning and Data Science, you find you need to freshen up on the basics in mathematics - stuff you may have studied before in school or university, but which was taught in another context, or not very intuitively, such that you struggle to relate it to how it’s used in Computer Science. This specialization aims to bridge that gap, getting you up to speed in the underlying mathematics, building an intuitive understanding, and relating it to Machine Learning and Data Science.

In the first course on Linear Algebra we look at what linear algebra is and how it relates to data. Then we look through what vectors and matrices are and how to work with them. The second course, Multivariate Calculus, builds on this to look at how to optimize fitting functions to get good fits to data. It starts from introductory calculus and then uses the matrices and vectors from the first course to look at data fitting. The third course, Dimensionality Reduction with Principal Component Analysis, uses the mathematics from the first two courses to compress high-dimensional data. This course is of intermediate difficulty and will require Python and numpy knowledge. At the end of this specialization you will have gained the prerequisite mathematical knowledge to continue your journey and take more advanced courses in machine learning.

Skills you'll gain

Tools you'll learn

What’s included

Shareable certificate

Add to your LinkedIn profile

Taught in English

51 practice exercises

Advance your subject-matter expertise

Learn in-demand skills from university and industry experts
Master a subject or tool with hands-on projects
Develop a deep understanding of key concepts
Earn a career certificate from Imperial College London

Specialization - 3 course series

Through the assignments of this specialisation you will use the skills you have learned to produce mini-projects with Python on interactive notebooks, an easy to learn tool which will help you apply the knowledge to real world problems. For example, using linear algebra in order to calculate the page rank of a small simulated internet, applying multivariate calculus in order to train your own neural network, performing a non-linear least squares regression to fit a model to a data set, and using principal component analysis to determine the features of the MNIST digits data set.

Mathematics for Machine Learning: Linear Algebra

Course 118 hours4.7 (12,466 ratings)View course

What you'll learn

In this course on Linear Algebra we look at what linear algebra is and how it relates to vectors and matrices. Then we look through what vectors and matrices are and how to work with them, including the knotty problem of eigenvalues and eigenvectors, and how to use these to solve problems. Finally we look at how to use these to do fun things with datasets - like how to rotate images of faces and how to extract eigenvectors to look at how the Pagerank algorithm works.

Since we're aiming at data-driven applications, we'll be implementing some of these ideas in code, not just on pencil and paper. Towards the end of the course, you'll write code blocks and encounter Jupyter notebooks in Python, but don't worry, these will be quite short, focussed on the concepts, and will guide you through if you’ve not coded before. At the end of this course you will have an intuitive understanding of vectors and matrices that will help you bridge the gap into linear algebra problems, and how to apply these concepts to machine learning.

Skills you'll gain

Linear Algebra, NumPy, Algorithms, Applied Mathematics, Data Manipulation, Python Programming, Data Science, and Machine Learning Algorithms

Mathematics for Machine Learning: Multivariate Calculus

Course 217 hours4.7 (5,739 ratings)View course

What you'll learn

This course offers a brief introduction to the multivariate calculus required to build many common machine learning techniques. We start at the very beginning with a refresher on the “rise over run” formulation of a slope, before converting this to the formal definition of the gradient of a function. We then start to build up a set of tools for making calculus easier and faster. Next, we learn how to calculate vectors that point up hill on multidimensional surfaces and even put this into action using an interactive game. We take a look at how we can use calculus to build approximations to functions, as well as helping us to quantify how accurate we should expect those approximations to be. We also spend some time talking about where calculus comes up in the training of neural networks, before finally showing you how it is applied in linear regression models. This course is intended to offer an intuitive understanding of calculus, as well as the language necessary to look concepts up yourselves when you get stuck. Hopefully, without going into too much detail, you’ll still come away with the confidence to dive into some more focused machine learning courses in future.

Skills you'll gain

Calculus, Regression Analysis, Artificial Neural Networks, Linear Algebra, Derivatives, Advanced Mathematics, Python Programming, Machine Learning Algorithms, and Statistical Analysis

Mathematics for Machine Learning: PCA

Course 320 hours4.0 (3,158 ratings)View course

What you'll learn

Implement mathematical concepts using real-world data
Derive PCA from a projection perspective
Understand how orthogonal projections work
Master PCA

Skills you'll gain

Linear Algebra, NumPy, Dimensionality Reduction, Python Programming, Calculus, Machine Learning, Statistics, Data Science, Probability & Statistics, and Jupyter

Earn a career certificate

Add this credential to your LinkedIn profile, resume, or CV. Share it on social media and in your performance review.

Instructors

David Dye

Imperial College London

2 Courses472,559 learners

Samuel J. Cooper

Imperial College London

2 Courses472,559 learners

Offered by

Imperial College London

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Frequently asked questions

3/4 hours a week for 3 to 4 months

High school maths knowledge is required. Basic knowledge of Python can come in handy, but it is not necessary for courses 1 and 2. For course 3 (intermediate difficulty) you will need basic Python and numpy knowledge to get through the assignments.

We recommend taking the courses in the order in which they are displayed on the main page of the Specialization.

Mathematics for Machine Learning Specialization

What you'll learn

Skills you'll gain

Tools you'll learn

What’s included

Advance your subject-matter expertise

Specialization - 3 course series

Mathematics for Machine Learning: Linear Algebra

What you'll learn

Skills you'll gain

Mathematics for Machine Learning: Multivariate Calculus

What you'll learn

Skills you'll gain

Mathematics for Machine Learning: PCA

What you'll learn

Skills you'll gain

Earn a career certificate

Instructors

Offered by

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Why people choose Coursera for their career

Open new doors with Coursera Plus

Advance your career with an online degree

Join over 3,400 global companies that choose Coursera for Business

Frequently asked questions

More questions

Mathematics for Machine Learning Specialization

What you'll learn

Skills you'll gain

Tools you'll learn

What’s included

Advance your subject-matter expertise

Specialization - 3 course series

Mathematics for Machine Learning: Linear Algebra

What you'll learn

Skills you'll gain

Mathematics for Machine Learning: Multivariate Calculus

What you'll learn

Skills you'll gain

Mathematics for Machine Learning: PCA

What you'll learn

Skills you'll gain

Earn a career certificate

Instructors

Offered by

Compare with similar products

You might also like

Why people choose Coursera for their career

Open new doors with Coursera Plus

Advance your career with an online degree

Join over 3,400 global companies that choose Coursera for Business

Frequently asked questions

How long does it take to complete the Specialization?

What background knowledge is necessary?

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

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