About this Course

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Learner Career Outcomes

35%

started a new career after completing these courses

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got a tangible career benefit from this course
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Flexible deadlines
Reset deadlines in accordance to your schedule.
Beginner Level
Approx. 18 hours to complete
English
Subtitles: English, Greek, Spanish

Skills you will gain

Linear RegressionVector CalculusMultivariable CalculusGradient Descent

Learner Career Outcomes

35%

started a new career after completing these courses

26%

got a tangible career benefit from this course
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Flexible deadlines
Reset deadlines in accordance to your schedule.
Beginner Level
Approx. 18 hours to complete
English
Subtitles: English, Greek, Spanish

Offered by

Imperial College London logo

Imperial College London

Syllabus - What you will learn from this course

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Week
1

Week 1

4 hours to complete

What is calculus?

4 hours to complete
10 videos (Total 46 min), 4 readings, 6 quizzes
10 videos
Welcome to Module 1!1m
Functions4m
Rise Over Run4m
Definition of a derivative10m
Differentiation examples & special cases7m
Product rule4m
Chain rule5m
Taming a beast5m
See you next module!39s
4 readings
About Imperial College & the team5m
How to be successful in this course5m
Grading Policy5m
Additional Readings & Helpful References5m
6 practice exercises
Matching functions visually20m
Matching the graph of a function to the graph of its derivative20m
Let's differentiate some functions20m
Practicing the product rule20m
Practicing the chain rule20m
Unleashing the toolbox20m
Week
2

Week 2

3 hours to complete

Multivariate calculus

3 hours to complete
9 videos (Total 41 min)
9 videos
Variables, constants & context7m
Differentiate with respect to anything4m
The Jacobian5m
Jacobian applied6m
The Sandpit4m
The Hessian5m
Reality is hard4m
See you next module!23s
5 practice exercises
Practicing partial differentiation20m
Calculating the Jacobian20m
Bigger Jacobians!20m
Calculating Hessians20m
Assessment: Jacobians and Hessians20m
Week
3

Week 3

3 hours to complete

Multivariate chain rule and its applications

3 hours to complete
6 videos (Total 19 min)
6 videos
Multivariate chain rule2m
More multivariate chain rule5m
Simple neural networks5m
More simple neural networks4m
See you next module!34s
3 practice exercises
Multivariate chain rule exercise20m
Simple Artificial Neural Networks20m
Training Neural Networks25m
Week
4

Week 4

3 hours to complete

Taylor series and linearisation

3 hours to complete
9 videos (Total 41 min)
9 videos
Building approximate functions3m
Power series3m
Power series derivation9m
Power series details6m
Examples5m
Linearisation5m
Multivariate Taylor6m
See you next module!28s
5 practice exercises
Matching functions and approximations20m
Applying the Taylor series15m
Taylor series - Special cases30m
2D Taylor series15m
Taylor Series Assessment20m

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About the Mathematics for Machine Learning Specialization

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....
Mathematics for Machine Learning

Frequently Asked Questions

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