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 are

welcome 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 whine 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 an 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 PageRank

algorithm just use for deciding the

order of webpages in search results

hopefully if you find this course useful

you'll stick around for a follow-on

course where Sam and I will introduce

you to multivariate calculus then in our

other course dr. Mark Dyson Roth will

introduce principal component and now

so welcome we really hope that it costs

be productive and useful for you but

also quite a lot of fun and I look

forward to hearing from you in the forms