Hi, and welcome to Machine Learning Immersive for advanced developers. Some of you may already know me, but for those that don’t, my name is Jordan Hudgens and I'm going to be your instructor throughout the course. Before we get started, I would like to address the prerequisites for this course. You will definitely need working knowledge of Python programming, CLI experience and a firm grasp of undergraduate mathematics. Specifically, probability & statistics and linear algebra.

When I was researching for the build out of this course, I went and I looked at many other courses: online and in person at Universities. From this, 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. However, from my experience and also how I learned machine learning myself, I thought it would be best to take a  different approach.

Throughout these guides, you are not going to see a ton of math and you're also not going to see a lot of code. The reason behind this, is that I want you to be able to build a strong mental framework and understanding of the algorithms that bring machine learning systems to life. I truly believe the most critical type of knowledge needed is not coding, or statistics. It's actually understanding the right fit for an algorithm and applying it 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 by talking about what machine learning is, along with some of the more interesting real world applications. Then, we're going to do something pretty cool. We will create an in depth path, from start to finish, mirroring that of the machine learning industry. We will develop a high-level understanding of data, how it is handled, and applied. From there, we will go through every major machine learning algorithm out there, and apply specific case studies that fit in with what those algorithms do the very best.  It’s also important to keep in mind that 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 ultimately 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. With all that being said, I’m very excited to begin this machine learning journey with you, and wish you nothing but success.