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

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learning mathematics statistics probability and even more.

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

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

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

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learning code to get insights out of data.

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

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

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

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

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

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

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an API or some sort.

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This is what we're going to cover.

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

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

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Number one create a framework.

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

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

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machine learning tools.

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

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future.

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

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

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this.

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

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

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

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

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Instead of writing code and taking action.

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So we've designed this course to avoid that.

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

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solutions.

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

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