Join Keith McCormick for an in-depth discussion in this video Linear SVM, part of Machine Learning and AI Foundations: Classification Modeling.
- [Instructor] All the algorithms…that we're going to discuss support vector machines…is among the most abstract conceptually.…It was one of the really hot algorithms…just a few years ago.…No, I don't want to diminish its importance now,…it's still considered to be an important…and popular technique.…But for a while it was really considered the best one.…And then it got displaced to a degree by ensembles of trees.…First, random forests, and then techniques like XGBoost.…
And now it seems like the hot algorithms are deep learning.…But of course, deep learning is used in application areas…like visual recognition and speech recognition.…So it's still considered to be one of the more complex…but also one of the more accurate algorithms out there.…Unfortunately of course, a lot of times…the really accurate algorithms and the more complex ones,…are black box.…So for certain projects that are off the table…because they're not gonna give us a narrative…of what's going on in terms of…variable importance and so on.…
Pretty much any algorithm will give you…
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
Skill Level Intermediate
SPSS Statistics Essential Trainingwith Barton Poulson4h 57m Beginner
Machine Learning and AI Foundations: Recommendationswith Adam Geitgey58m 7s Intermediate
1. The Big Picture: Defining Your Classification Strategy
2. How Do I Choose a "Winner"?
3. Algorithms on Parade
4. Common Modeling Challenges
Next steps3m 17s
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