So I wanted to get into ML using Python recently and I was wondering about which ML library I should learn as a ML beginner first. I’ve been using Python for a few years now.
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Linear and logistic regression are much easier (and less error prone) to implement from scratch than neural network training with backpropagation.
That way you can still follow the progression I suggested: implement those regressions by hand using numpy -> compare against (and appreciate) sklearn -> implement SVMs by hand using cvxpy -> appreciate sklearn again.
If you get the hang of “classical” ML, then deep learning becomes easy as it’s still machine learning, just with more complicated models and no closed-form solutions.
Aight thanks.
