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.

This course is part of the Mathematics for Machine Learning Specialization

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Offered By

## About this Course

## Skills you will gain

- Linear Regression
- Vector Calculus
- Multivariable Calculus
- Gradient Descent

## Offered by

## Syllabus - What you will learn from this course

**4 hours to complete**

### What is calculus?

**4 hours to complete**

**3 hours to complete**

### Multivariate calculus

**3 hours to complete**

**3 hours to complete**

### Multivariate chain rule and its applications

**3 hours to complete**

**3 hours to complete**

### Taylor series and linearisation

**3 hours to complete**

## Reviews

- 5 stars76.74%
- 4 stars19.09%
- 3 stars3.17%
- 2 stars0.65%
- 1 star0.33%

### TOP REVIEWS FROM MATHEMATICS FOR MACHINE LEARNING: MULTIVARIATE CALCULUS

Just a great course for getting you ready to understand machine learning algorithms. The chapter on backpropagation is simply outstanding and the programming assignments are awesome!

Great course. It is clear and accessible, giving a lot of the intuition of why things are done. Some important topics in calculus are missing, such as Integration, but overall very good course.

Great course to develop some understanding and intuition about the basic concepts used in optimization. Last 2 weeks were a bit on a lower level of quality then the rest in my opinion but still great.

Very well thought out course. Concepts covered in this course are very well explained. You would need to have at least a high school foundation in Calculus to appreciate the content in this course.

## About the Mathematics for Machine Learning Specialization

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