Hi there, I'm David Dye,

and welcome to the Mathematics

for Machine Learning specialization.

Before we get stuck in,

let's set the scene.

Machine learning is a set of

powerful mathematical tools that enable us,

to represent, interpret, and

control the complex world around us.

However, even just the word mathematics makes

some people feel uneasy and

unwelcome to explore the topic.

The purpose of this specialization is to take you on

a tour through the basic maths underlying these methods,

focusing in particular on building

your intuition rather than

worrying too much about the details.

Thanks to the amazing machine learning community,

it's actually possible to apply

many powerful machine learning methods

without understanding very much

about the underpinning mathematics,

by using open source libraries.

This is great, but problems can arise and

without some sense of the language

and meaning of the relevant maths,

you can struggle to work out

what's gone wrong or how to fix it.

The ideal outcome of

this specialization is that it will

give you the confidence

and motivation to immediately

dive into one of the hundreds of

boolean applied machine learning courses

already available online,

and not be intimidated by

the matrix notation or the calculus.

We want to open up machine learning to

as many people as possible,

and not just leave all the fun to computer scientists.

This first course offers

the introduction to linear algebra which

is essentially a set of notational conventions

and handy operations,

that allow you to manipulate

large systems of equations conveniently.

Over the next five modules,

we'll be focusing on building

your intuition about vectors and translations

through the use of quizzes and

interactive widgets as well as

occasionally asking you to fill in

the gaps in some Python coding examples.

In the final module,

Dr. Sam Cooper will bring it all

together by showing you how

linear algebra is at the heart of

Google's famous page rank algorithm,

which is used for deciding the order

of web pages in search results.

Hopefully, if you find this course useful,

you'll stick around for a follow-on course by

Sam and I who will introduce

you to multivariate calculus.

Then, in our other course

Dr. Mark Dyes and I will introduce

principal component analysis. So welcome.

We really hope that the course will be productive and

useful for you but also

quite a lot of fun and I look

forward to hearing from you in the forums.