Lesson 1 is a decision tree, isn't a little too advanced?
Shouldn't we start we something like linear regression?
Shouldn't we start we something like linear regression?
Decision trees are much easier to use correctly, and can be easily implemented from scratch without relying on any external libraries (as we do in a later lesson).
I actually wrote most of the machine learning content at Dataquest (where I work) and I started with k-nearest neighbors because it's way more approachable (https://www.dataquest.io/course/machine-learning-fundamental...). Little math, very visual, easy to program, etc. I used this easier-to-approach algorithm to teach the key other ideas in ML (test/train, cross validation, error metrics, etc).
Happy to chat more about different pedagogical approaches to teach machine learning!
That's a great idea. It's actually what I did in an earlier version of the USF course. It worked great. Especially because a decision tree is basically just KNN with a different distance measure (loosely defined) so it can flow well
However it turned out that jumping straight to decision trees worked out well too, so I'm happy with the change.
There is no need to know gradient descent to learn OLS. All you need to be able to is take derivatives (and "matrix" derivatives)