Be a Machine Learning Whiz: Learn 10 Numerical Recipes
December 1, 2021
I like Cowboy Kent Rollins cooking videos on YouTube. His creations are presented in such a way that even the microwave-challenged chef can feed them thar hungry ranch hands. You can check out how to make a “real” tuna casserole at this link.
Now we have a similar approach to machine learning. Navigate to “All Machine Learning Algorithms You Should Know in 2022.” I love articles that assert the “all” thing; that is, the categorical affirmative. It’s like Milton’s definition of God’s power. Awe inspiring for sure.
First, these are the “popular” ones, which I think means commonly taught in university courses and endlessly recycled by the high school science club members who could remember the procedures, get an A, and go on to found an AI start up. Who documents popular? Why, silly goose, would you ask such a questions?
What are the 10 popular “all” algorithms? Herewith the listicle:
- Ensemble learning algorithms. There are a bunch of them, but let’s not quibble with the notion of 10.
- Explanatory algorithms. Again, there’s a pride of procedures here.
- Clustering algorithms. Yep, more than one to learn.
- Dimensionality algorithms. Yep, more than one to memorize.)
- Similarity algorithms. These will keep even a devoted math whiz busy. Why? Well, what is similarity in a particular case? Yeah, tricky.
Wait there are only five, not 10. What’s happened?
Yeah, counting may not be the core competency of a person who can identify “all” algorithms one needs to know for machine learning.
Stephen E Arnold, December 1, 2021
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