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, 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 CSV files where amazing, because we
can store data that businesses generate such as maybe customers customer data into an Excel file.
And then people got really, really good at analyzing these CSV 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 relational databases.
Spreadsheets were great.
CSV 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 MySQL.
Which allowed us, instead of using spreadsheets, to use a language called SQL, to read information
from our database, write 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 2000s, 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 when we started getting this idea of NoSQL, where things like MongoDB 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 CPUs, GPUs, 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 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.
I'll see you in the next video.
Bye bye.