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One thing that I really like doing in my courses is to actually understand the why of everything.

Everything that we learn there should be a reason we're learning it right.

And you might be asking yourself a why do we even care about machine learning how is that useful and

how do we get here.

Well if you think about a business because most technology evolves from business needs we have the advent

of computers and the ability for businesses to use computers to do things really really fast and efficiently

so that they gain an edge.

And then we got spreadsheets spreadsheets like Excel files and CSP files were amazing because we can

store data that businesses generate such as maybe customer data into an excel file and then people got

really really good at analyzing these CSP files these spreadsheets to make business decisions.

Maybe forecasting that December sales are going to be high because while the past two years we've had

really high December sales because of Christmas and then as companies got more and more data we started

getting this idea of the relational databases spreadsheets were great CSP files were great but we started

getting more and more information and data and we needed a better way to organize things to understand

things from our data.

That's when we got things like my askew well which allowed us instead of using spreadsheets to use a

language called ASCII well to read information from our database right information to our database but

similar to spreadsheets use the data that we gathered from the business to make business decisions so

that our business becomes even more profitable.

And then in 2000 we had this fancy term of big data we had big companies like Facebook Amazon Twitter

Google that started accumulating more and more data an insane amount of data that you simply couldn't

contain in a spreadsheet.

User actions user likes user purchasing histories this idea of big data meant that we had so much data

these companies had so much data and sometimes unlike relational databases which had to be a structured

form of data sometimes we got really messy unstructured data and that's where we started getting this

idea of no Eskew well where things like Mongo D.B. came into existence where you can store unstructured

data and hopefully make business decisions out of that.

Maybe if you were Amazon you can use customers purchasing history to recommend different products.

And ever since then this idea of data getting more and more data has turned us into using machine learning

because at some point we have so much data that as humans we can't just look like we did at spreadsheets

and look at columns and rows and make business decisions I mean we still could but then we'd be wasting

all this data that we've been getting over the years.

So companies like Facebook and Google that collect massive amounts of data every single day are turning

to things like machine learning so that instead of humans looking at the data and trying to figure things

out we give this data to machines so that they're better able even better than humans to make business

decisions.

And this idea of machine learning really came to be because of this growth in data that we received

from businesses as well as the improvements in CPE use GP use that is graphical processing units and

computer advancements.

So using the massive amounts of data and massive improvements in computation we can use these machines

to give them this big data and make a decision for us just like we used to with spreadsheets.

Now this is a simplified version of how we got here but I hope it gives you a reason as to why businesses

like this idea of machine learning now in this course we're going to be using this framework and don't

worry.

Don't get intimidated.

You're gonna get really familiar with this framework because well we're going to talk about it a lot

but looking at this just a brief overview.

What do you think the hardest part is.

Can you guess it's this first bar right here.

Grabbing the data is the amount of data is doubling every two years in our world with the Internet all

the mobile phones and connected devices.

We're creating more and more data but this data doesn't mean anything unless we understand it.

Yes we are producing data but a lot of this data that we generate is unused and that's what data science

is.

How can we use this massive quantity of data that is completely useless right now to something that

is useful and not all data is made equal right.

Some are noisy some are messy.

Where do we grab this data from.

How do we find it.

How do we clean it so we can actually learn from it.

We need to understand what data is and then apply machine learning to it.

And the industry is now evolving into these people that we want to be data scientists that is people

that can turn data from use less to use for.