Join Keith McCormick for an in-depth discussion in this video Statistics vs. machine learning, part of Machine Learning and AI Foundations: Classification Modeling.
- [Instructor] So let's talk some more about…statistical techniques and contrast them…with machine learning techniques.…Some folks, especially if they are coming from…computer science backgrounds,…and/or they might be new to the field,…wonder if it makes sense to use these older techniques…that come from the statistical tradition.…It's not an irrational reaction.…These techniques are decades old…and why would we have invented newer techniques…if the old techniques were working?…Also, a lot of them have complex rules,…and seem hard to learn.…
There are several reasons why…you should keep the classification…algorithms that come from the statistical tradition…on the table.…First, predictive analytic software vendors…consistently keep them in their work benches.…And the data scientists that use them…continue to use these techniques.…So our colleagues are voting with their feet,…and there must be a reason.…In short, these algorithms continue to work.…Second, as we've seen, they tend to be…among the more transparent of the algorithms…
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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