Classification methods are among the most important in modern data science. Learn classification strategies and algorithms for machining learning and AI.
- [Keith] In my more than 20 years of building machine learning models, I've been involved in many kinds of projects. However, if you think of those same projects more broadly, as a series, culminating in a solution for a client, every single of them has involved predicting some kind of binary outcome. Bi or not bi, fraud or not fraud, getting illness or not, and many others. Nothing is more fundamental to what we do in predictive analytics than binary classification.
In this course, I'll carefully define what classification models are and why they're important. Then, we'll go on a journey together to explore dozen different algorithmic approaches for building these models. They include some famous techniques so you've probably heard some of the names. But there will likely be some that you haven't heard of. What they all have in common is that they represent the most important and most frequently used AI and machine learning algorithms in use today.
Since we'll be tool agnostic in the course, and with virtually no prerequisites, all can join me in exploring these concepts and techniques. You'll emerge a more knowledgeable data scientist and a better modeler. So please join me.
Note: These tutorials are focused on the theory and practical application of binary classification algorithms. No software is required to follow along with the course.
- Why do you need classification?
- Statistical algorithms versus machine learning algorithms
- Combining models using ensembles
- Classification modeling challenges