Meet your instructors and why you should study machine learning

Hi my name is Illya and I will be taking you through machine learning with tents or flow for business

intelligence.

I am a business graduate with a strong affinity for numbers.

I love mathematics and statistics.

In fact I enjoy the topic so much.

I have competed nationally and internationally and have won multiple awards due to these reasons.

I have developed a deep interest in machine learning.

Many people see machine learning as a path to artificial intelligence.

But for a statistician or a businessman it can also be a powerful tool allowing the achievement of unprecedented

predictive results.

I hope my business training combined with my fascination with machine learning will give this course

a business twist which will be extremely useful for you.

While I am the one taking you through the topics the content you will see was developed with a significant

contribution from is ran in my student years when I competed in mathematics.

I MIT is Crann who competed in mathematics physics and programming and has been my dear friend ever

since.

He has incredible achievements in his career.

Top of the class graduate from both Cal and university of Edinburg master in computer science from Oxford

Microsoft research award recipient published researcher in quantum computing.

He is definitely the ideal co instructor.

He has done all types of machine learning with a focus on algorithmic Forex trading and business analysis

throughout the years is and has been my go to person should I have any quantitative doubts.

That’s why it was a true pleasure to work together on this course.

Before we start learning we would like to spend a few minutes emphasizing my machine learning is so

important.

Everyone knows about artificial intelligence or AI in short.

Usually when we hear ai ai we imagine robots going around performing the same test as humans but we

have to understand that while some tasks are easy others are harder and we are a long way from having

a human like robot.

Machine learning however is very real and is already here.

It can be considered a part of AI as most of what we imagine when we think of an AI is machine learning

based in the past.

We believe these robots of the future would need to learn everything from us but the human brain is

sophisticated and not all actions and activities it coordinates can be easily described.

Arthur Samual in 1959 came up with the brilliant idea that we don’t need to teach computers but we should

rather make them learn on their own.

Samuel also coined the term machine learning and since then when we talk about a machine learning process

we refer to the ability of computers to learn autonomously while preparing the contents of this lecture.

We wrote down examples with no further explanation.

Presuming everyone is familiar with them and then I thought Do people know these are examples of machine

learning.

Let’s consider a few natural language processing such as translation if you thought Google Translate

is a really good dictionary.

Think again.

Oxford and Cambridge are dictionaries that are constantly improved.

Google Translate is essentially a set of machine learning algorithms.

Google doesn’t need to update Google Translate it is automatically updated based on the usage of different

words.

Oh wow.

What else.

While still on the topic Siri Alexa Cortana and recently Google’s assistant are all instances of speech

recognition and synthesis.

There are technologies that allow these assistance to recognize or pronounce words they have never heard

before.

It is incredible what they can do now but will be much more impressive in the near future.

What else.

Spam filtering.

Unimpressive but it is noteworthy that spam no longer follows a set of rules.

It has learned on its own what is spam and what isn’t recommendation systems.

Netflix Amazon Facebook everything that is recommended to you depends on your search activity likes

previous behavior and so on.

It is impossible for a person to come up with a recommendation that will suit you as well as these Web

sites do.

Most important they do that across platforms across devices and across apps.

While some people consider it intrusive.

Usually that data is not processed by humans.

Often it is so complicated that humans cannot grasp it.

Machines however met Sillars with Byars movies with prospective viewers photos with people who want

to see them.

This has improved our lives significantly.

If somebody annoys you you won’t see that person popping up in your Facebook feed boring movies rarely

make their way into your Netflix account.

Amazon is offering you products before you know you need them.

Speaking of which Amazon has such amazing machine learning algorithms in place they can predict with

high certainty what you’ll buy and when you’ll buy it.

So what do they do with that information.

They ship the product to the nearest warehouse so you can order it and receive it in the same day.

Incredible.

Next on our list is financial trading trading involves random behavior ever changing data all types

of factors from political to Judicial that are far away from traditional finance.

While financers cannot predict much of that behavior machine learning algorithms take care of that and

respond to changes in the market faster than a human can ever imagine.

These are all business implementations but there are even more.

You can predict if an employee will stay with your company or leave.

You can decide if a customer is worth your time if they’ll likely buy from a competitor or not buy at

all.

You can optimize processes predict sales discover hidden opportunities.

Machine learning opens a whole new world of opportunities which is a dream come true for the people

working in a company strategy department.

Anyhow these are uses already here.

Then we have the next level like Autonomy’s vehicles.

Self-driving cars were science fiction until recent years.

Well not anymore more millions if not billions of miles have been driven by Autonomy’s vehicles.

How did that happen.

Not by a set of rules.

It was rather a set of machine learning algorithms that made cars learn how to drive extremely safe

and efficiently.

We can go on for hours but I believe you got the gist of it.

Why machine learning.

So for you it is not a question of why but how.

That’s what this course will tackle how to create machine learning algorithms.

That’s really exciting.

Are you ready.

Let’s do it then.