‫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 that are 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 modelling and deployment where you might take your machine learning model after
‫you've gone through these steps here.
‫Don't worry, we're going to cover this framework in depth shortly.
‫You might take this machine learning model and deploy it to 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 modelling, which means you'll be able to take a data set and apply machine
‫learning algorithms to find insights on that data set.
‫The steps we're going to cover is, number one, create a framework.
‫You saw a little bit of an outline on that in the previous slide.
‫Number two, we're 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 going to 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.
‫Now let's take a deeper look at the framework we're going to be using.