Now each one of these elements was specifically picked out because it has a direct mapping to a machine learning and data science algorithm in the way that we're going to traverse through the course material is that each mathematical formula and each process is going to have its own section.

Inside of that section we're going to have a case study that we walk through because I personally have a hard time understanding concepts when they're very abstract and instead of just going straight into writing symbols on the board and going through the various math type problems that you need to have in order to build out these types of programs what I'd rather do is look at a real-world machine learning problem and then see how statistical analysis can help solve that problem.

In each one of these case studies, I pick out a machine learning problem that I've personally worked on. So these are not high level kind of just out of nowhere problems that you're never going to face in industry. Instead each one of these is a direct mapping to a problem and a system that I personally built out.

So there are going to be five different concepts that we're going to walk through in this course. The first is going to be probability. Now in probability, you may think that you have a pretty good understanding of what probability is, it simply is the study of trying to discover how probable an event is to occur.

But we're going to take it a step further and we're going to take a case study of a baseball team and many of the examples that I'm going to give are related to sports because sports have been one of the industries that have embraced statistical analysis. And I've also performed a number of consulting jobs with athletic teams and so the very first case study we're going to go through is going to be a baseball team trying to discover where they should position their players on the field and we're going to leverage probability in order to help give us that answer.

Extending our knowledge of probability the next term that we're going to walk through is hypothesis testing. So we're going to also extend the case study and we're going to see how we can leverage probability and build out our own hypothesis and then go through the types of successful types of analysis we can perform. But just as important as the successful analysis is we're also going to dive into the multiple types of failures that can occur because understanding how your program can fail from a logical perspective is just as important as understanding how it can succeed.

Next on the list of terms is distribution and we're also going to change up our case study in distribution. We're going to analyze a sales cycle and we're going to try to see how we can predict if a certain type of lead is going to have a good chance or a poor chance of converting into a customer.

Next on the list of terms is Basine logic now this one is one of my favorite because there's actually a direct mapping straight into one of the most popular machine learning algorithms out there called The Naive Bayes algorithm. And in Bayesian logic what we're going to be able to walk through is understanding how spam filters can work. So that's going to be our case study and The Naive Bayes algorithm is one of the most popular algorithms to pick for e-mail services that try to analyze if an e-mail is spam or if it's legitimate.

The last statistical analysis concept that we're going to dive into is regression. Now regression is the parent concept behind some of the world's most popular machine learning and data science algorithms. So in that case study we're going to walk through our job candidate example. So we're going to see how we can leverage regression in order to see a timeline in order to build a graph that allows us to see if a job candidate is a good candidate for the job or if they don't fit in with the organization.

At the end of the course, I'm going to build an entire section dedicated to the key terms that are used in statistical analysis. I know that one of the more intimidating concepts when working with any kind of math is the ability to understand what some of the big words mean they can be very intimidating and they can actually stop people from learning because they think if they don't understand a specific term they're not going to understand the larger concept.

And so that entire section is a reference point for you. So I'm going to go through some important concepts such as standard deviation or integrals and these are the types of concepts that are used throughout all of the different statistical analysis algorithms and the machine learning and data science concepts.

I want you to be able to have that section as a reference point. So every time you hear one of those big scary key words you can simply go and reference that video or that written guide and then have a refresher on what it means.

Now that we've walked through the five main mathematical concepts that we're going to cover in this course let's get into the material.