Mathematics for Machine Learning Specialization

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Mathematics for Machine Learning Specialization

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

Instructors: David Dye

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3 course series

Get in-depth knowledge of a subject

4.6

(13,244 reviews)

Beginner level

No prior experience required

1 month at 10 hours a week

Flexible schedule

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3 course series

Get in-depth knowledge of a subject

4.6

(13,244 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

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Taught in English

51 practice exercises

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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 (12,444 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

Category: Linear Algebra

Category: NumPy

Category: Python Programming

Category: Machine Learning

Category: Algorithms

Category: Jupyter

Category: Applied Mathematics

Category: Data Science

Mathematics for Machine Learning: Multivariate Calculus

Course 217 hours4.7 (5,731 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

Category: Calculus

Category: Linear Algebra

Category: Regression Analysis

Category: Artificial Neural Networks

Category: Derivatives

Category: Advanced Mathematics

Category: Machine Learning Algorithms

Category: Statistical Analysis

Category: Python Programming

Mathematics for Machine Learning: PCA

Course 320 hours4.0 (3,155 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

Category: Linear Algebra

Category: NumPy

Category: Python Programming

Category: Dimensionality Reduction

Category: Jupyter

Category: Machine Learning

Category: Probability & Statistics

Category: Data Science

Category: Advanced Mathematics

Category: Calculus

Category: Statistics

Category: Feature Engineering

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Instructors

David Dye

Imperial College London

2 Courses467,506 learners

Samuel J. Cooper

Imperial College London

2 Courses467,506 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.