Join Keith McCormick for an in-depth discussion in this video What you should know, part of Machine Learning and AI Foundations: Classification Modeling.
- Let's talk about some things you should know before watching this course. One of the things I'm really excited about is that this course has been designed as a gateway course. A gateway to all kinds of machine learning topics and data science more broadly. Specifically we're going to be building predictive models. Also its software agnostic so I wanna welcome all of you to the course whether or not you happen to use a commercial package like SAS or SPSS or a data science language like R or Python or one of the numerous open source options like Weka or RapidMiner or Knime or others.
Everyone can benefit from this course. Also I want to mention that if you really want a big-picture view and pull the lens back even more, my Essential Elements of Predictive Analytics and Data Mining is that kind of course. However you absolutely could watch this course followed by that one or start with Essential Elements and watch this one. Neither of them have prerequisites so they're an interesting pair but you can watch them in either order.
Finally I will be mentioning statistical concepts. Terms like statistical significance. Also a couple of the techniques do draw upon regression. However you don't have to take a deep dive into regression to understand what I'm discussing in this course. You may be tempted to explore statistics and regression in more detail perhaps after taking this course.
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