Original subtitles

Now if you've looked up machine learning before you know there's a lot out there some resources recommend

learning mathematics statistics probability and even more.

Before getting started with data science and machine learning and while these these topics they're important

trying to learn them all before getting started getting hands on is like trying to boil the ocean.

Instead what we're going to be doing is focusing on building practical solutions and writing machine

learning code to get insights out of data.

If you're a programmer now and had some experience with python by the end of this course you'll be able

to use your programming skills to build predictive machine learning models.

Machine learning comes in three parts data collection data modeling and deployment where you might take

a machine learning model after you've gone through these steps here.

Don't worry we're gonna cover this framework in depth shortly.

You might take this machine learning model and deploy it to users through your application or through

an API or some sort.

This is what we're going to cover.

We're going to cover data modeling which means you'll be able to take a dataset and apply machine learning

algorithms to find insights on that dataset the steps we're going to cover.

Number one create a framework.

You saw a little bit of an outline in that in the previous slide.

Number two were going to match that framework once we've gone through it to available data science and

machine learning tools.

This means that people have already had similar problems to what we're going to try and solve in the

future.

In the past and so they've created tools for those problems.

So we're gonna learn what tools are used for what machine learning projects and then to learn all of

this.

We're going to learn by doing by going through projects which involve step one and step two to build

a portfolio to show off your work and your skills the utmost care has been taken to focus on what matters.

When I started learning machine learning I found myself confused about what to do.

Too often I'd spend too much time thinking about something.

Instead of writing code and taking action.

So we've designed this course to avoid that.

Instead of doing anything and everything from scratch we're going to be using what works to build practical

solutions.

And at the same time learning about machine learning and data science.

Let's take a deeper look at the framework we're going to be using.