Original subtitles

Here are the big words that are taking the industry by storm machine learning now.

Throughout the course we're going to explore this topic as well as data science and how it all fits

into the tech space.

But first things first you need to be able to explain this topic to your co-workers and not look like

you don't know what you're talking about.

So let's start with this.

You see machines or computers.

In this case are really really good at certain things right.

Machines can perform tasks really really fast and we can control these machines and get them to do tasks

for us which is what programming is we programmed computers.

We give them instructions to do tasks and they do it for us.

You see computers came into this world because it allowed humans to do certain tasks really fast.

That may have required hundreds of workers before.

I mean computers used to actually mean people who do tasks that compute.

They computed things.

So for example let's say I wanted to find out how to get to Danielle's house using Google maps.

Well one option is I could pull out a map and with a ruler measure each of the routes that I could take

to Daniel's house and then do some math do some addition and find the shortest path or I could ask a

computer to do this.

Imagine we had ten different routes instead of me measuring each route one by one I could simply program

and ask a computer Hey can you tell me how to get to Daniel's house.

And through programming we tell the computer can you calculate really quickly these 10 routes and find

the shortest one.

That's what programming is instead of me taking 10 minutes to figure out how to get to Daniel's house

and the fastest route I just click a button and the computer tells me what to do.

Obviously we have to program it and give it instructions but once we give it a set of instructions it

just like that finds the solution for us it computes solutions and you know why computers became so

popular because computers saved companies lots of money instead of hiring lots of workers.

Well let's just buy a computer that does the task of one hundred workers.

And there you go.

We saved a lot of money and computers don't complain they'll work 24/7 for you.

Now these machines or computers are really good things that we can describe.

Right.

That we can write in code if else blocks to do something by the way if you're new to programming don't

worry.

We're actually going to start you off from scratch as well with Python so don't worry.

This part is just theoretical.

So for example let's say we talk about roots.

I can say to the computer hey if Route 1 is shorter than route 2 and if Route 1 a shorter than Route

3 and if Route 1 is shorter then Route 4 and also Route 5 well then pick Route 1 as the best option

I can describe that through programming to computers and computers are really good at working on tasks

that have these defined rules all the way up to a game of chess when you play a game of chess against

a computer.

We could technically have these A if else if then blocks lots of them to see how to move each piece

and what the pros and cons are.

And because computers are fast we can just do a ton of these calculations.

But then there's a problem.

What if instead of just trying to get to Daniel's house we have to ask is this person angry let's say

somebody left a review on Amazon or we're building a product that detects human emotion.

How can I describe to a computer what angry means can you do that.

If an if else block.

What about this.

What is a cat.

How do we tell a computer what a cat is.

Let's say I ask you to program a computer to detect if this picture is a cat or not.

Can you program that.

I mean sure you can technically say a yes.

This cat has fur.

This cat has whiskers.

This cat meows.

But then the computers comes back to you and asks what is a meow or what are whiskers.

What is Hair.

You see the harder things become to describe the harder it is for us to tell machines what to do.

So we hire humans to do these things that are harder for us.

So we let machines take care of the easier part of which is you know things that we can describe and

the harder things that are hard to just give instructions to.

Well we just let humans do it like being a salesperson or being an artist.

Computers aren't good.

So we hire humans.

But then there's this new idea of machine learning and you've definitely heard of it because it is a

big buzzword right now in our industry because machine learning has a lot of applications.

We could have self-driving cars robots vision processing language processing recommendation engines

translation services stock price predictions.

There's so many applications and this is all because of machine learning you see things that computers

couldn't do before and only humans can can now be done by computers with machine learning.

Well kind of sometimes it gets it right sometimes it doesn't.

And we'll get to that but the idea is this.

The goal of machine learning is to make machines act more and more like humans because the smarter they

get the more they help us humans accomplish our goals.

Now in this section we're going to go over some theory to get us familiar with this topic to understand

this topic a little bit more but don't get worried.

Don't get intimidated.

We're going to try and simplify things and have fun along the way.

And in later sections we're actually going to code build our own machine learning models and do that

exciting stuff.

But we have to learn the foundations first.

So let's take a break and I'll see you in the next video by.