I've been in the development space for over 15 years. And in the past five or six years I've started to dive deep into machine learning and data science and what this course is going to focus on is really me imparting what I've learned as I've built out my own machine learning applications.

When I was researching for the build out of this course I went and I looked at many other courses online and that were taught at universities and I saw a few common patterns. I saw the instructors would take two approaches. They would either start out right away diving into the code or they would go straight into the mathematical formulations and both of those options are fine.

However, from my experience and also how I learned machine learning myself I thought I would take a very different approach with this course.

Throughout these guides, you are not going to see any math and you're also not going to see any code because I don't want you to feel like you have to use one specific programming language. And I also don't want you to get bogged down too quickly in the mathematical formula. Instead what I want to you to do is to be able to build a mental framework for understanding machine learning data science and the algorithm that really run these types of systems.

The reason why I took this approach in building this course and structuring it in the way that we are going to do is that there is a dirty little secret in the machine learning and data science space that a lot of people don't like to talk about but that is that the most critical type of knowledge that you're going to have is not coding and it's not statistics it's actually understanding the right fit for an algorithm to the type of behavior that you're trying to model.

And so that's what this course is all about. We're going to start off with talking about what machine learning and data science are, how they fit into the umbrella of technologies that is artificial intelligence and then we're going to do something pretty cool.

We are going to go through every major machine learning algorithm that is out there and we're not going to look at the code we're not going to build the programs. But instead what we're going to do is we're going to analyze specific case studies that fit in with what those algorithms do the very best.

Because once you get out in industry or once you start building these algorithms into your own applications what you're going to find is typically there are some pretty good code libraries out there and there are services that will perform some of the critical tasks. But what is going to make your machine learning algorithms succeed or fail is if you're able to pick out the best one for your situation.

After we've walked through each one of those case studies and we've analyzed all of the key algorithms that are out there what we're going to do is have an entire module dedicated to the key terms and you can use that as a reference point and kind of like an appendix as you go through the rest of the machine learning track.

But also when you're out in industry one of the more intimidating concepts when it comes to working in the machine learning space is that there are so many terms with some very large intimidating words but once you actually understand what they mean I think you'll be pleasantly surprised that most of them have a very real world type of reification which means that it is a big concept with something in the real world that you can apply it to.

After we've walked through all of the popular machine learning algorithms and analyze those industry case studies we're going to dedicate an entire module to the key terms and concepts in machine learning.

Part of the reason why I wanted to dedicate an entire module just two terms is because one thing I've noticed with students is many of the key terms and concepts that are taught in the machine learning space are pretty big words and they can be intimidating if you've never heard them before and also it's hard to memorize all of them.

What I wanted to do was to create an entire set of guides where we analyze and dissect each one of those terms. So later on in other machine learning courses or even when you're building out your own machine learning algorithms later on in your job you can use this course as a reference so you can go and check a case study, you can go and check whenever you forget what a term means and you can see exactly how you can implement that in your own programs.