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Seems like the kind of stuff you get at by combining sklearn's official docs with some material from one of the moocs (like udacity's machine learning class, co
by nnq 10y ago
Seems like the kind of stuff you get at by combining sklearn's official docs with some material from one of the moocs (like udacity's machine learning class, coursera's etc.) + some wikipedia. This is my default style of learning: picking a "track" (whatever mooc or a cool side project idea) + lots of small specific resources.
I'm really curious why would you recommend and actual book?
It has all the chances to get outdated in a few months, while the separate resources will get updated for sure. yeah, there's some googling effort, but still...
- StClaire 10y agoMost (all? I can't remember exactly) of it doesn't use Scikit-learn. You actually implement the algorithms yourself and see what happens under the hood
- nnq 10y agoI randomly browsed and got to some sklearn code snippet. great, then I'll definitely dig deeper into it!
- dandersh 10y agoI just got this book ~2 weeks ago and am using it in conjunction with udacity's course. Python Machine Learning (PML) is more low-level, math intensive, and hands on than udacity and other resources I'm using. Thus far Udacity's course fits your explanation of using sklearn docs, tweaking inputs, etc. while PML provides a technical explanation of weighting with perceptrons and then you hand-write a perceptron learning algorithm.
- WoodenChair 10y agoBooks are able to dig into a single topic deeply in a connected way with a single author's voice who can assume at any point all the given knowledge from the previous chapters. It is possible to learn from many shallow or specific sources, but it's not as easy/much more effort for most.