Hi Sebastian, I looked through your book. I think it is great if you already know Python, and looking to learn machine learning.
For me, I have some sense of machine learning, but none of Python. Unlike R, which is specifically for statistics analysis. Python is broad! Maybe some expert here with R can tell me how to go about this. :) On Sun, Jun 18, 2017 at 12:53 PM, Sebastian Raschka <se.rasc...@gmail.com> wrote: > Hi, > > > I am extremely frustrated using this thing. Everything comes after a > dot! Why would you type the sam thing at the beginning of every line. It's > not efficient. > > > > code 1: > > y_sin = np.sin(x) > > y_cos = np.cos(x) > > > > I know you can import the entire package without the "as np", but I see > np.something as the standard. Why? > > Because it makes it clear where this function is coming from. Sure, you > could do > > from numpy import * > > but this is NOT!!! recommended. The reason why this is not recommended is > that it would clutter up your main name space. For instance, numpy has its > own sum function. If you do from numpy import *, Python's in-built `sum` > will be gone from your main name space and replaced by NumPy's sum. This is > confusing and should be avoided. > > > In the code above, sklearn > linear_model > Ridge, one lives inside the > other, it feels that there are multiple layer, how deep do I have to dig in? > > > > Can someone explain the mentality behind this setup? > > This is one way to organize your code and package. Sklearn contains many > things, and organizing it by subpackages (linear_model, svm, ...) makes > only sense; otherwise, you would end up with code files > 100,000 lines or > so, which would make life really hard for package developers. > > Here, scikit-learn tries to follow the core principles of good object > oriented program design, for instance, Abstraction, encapsulation, > modularity, hierarchy, ... > > > What are some good ways and resources to learn Python for data analysis? > > I think baed on your questions, a good resource would be an introduction > to programming book or course. I think that sections on objected oriented > programming would make the rationale/design/API of scikit-learn and Python > classes as a whole more accessible and address your concerns and questions. > > Best, > Sebastian > > > On Jun 18, 2017, at 12:02 PM, C W <tmrs...@gmail.com> wrote: > > > > Dear Scikit-learn, > > > > What are some good ways and resources to learn Python for data analysis? > > > > I am extremely frustrated using this thing. Everything comes after a > dot! Why would you type the sam thing at the beginning of every line. It's > not efficient. > > > > code 1: > > y_sin = np.sin(x) > > y_cos = np.cos(x) > > > > I know you can import the entire package without the "as np", but I see > np.something as the standard. Why? > > > > Code 2: > > model = LogisticRegression() > > model.fit(X_train, y_train) > > model.score(X_test, y_test) > > > > In R, everything is saved to a variable. In the code above, what if I > accidentally ran model.fit(), I would not know. > > > > Code 3: > > from sklearn import linear_model > > reg = linear_model.Ridge (alpha = .5) > > reg.fit ([[0, 0], [0, 0], [1, 1]], [0, .1, 1]) > > > > In the code above, sklearn > linear_model > Ridge, one lives inside the > other, it feels that there are multiple layer, how deep do I have to dig in? > > > > Can someone explain the mentality behind this setup? > > > > Thank you very much! > > > > M > > _______________________________________________ > > scikit-learn mailing list > > scikit-learn@python.org > > https://mail.python.org/mailman/listinfo/scikit-learn > > _______________________________________________ > scikit-learn mailing list > scikit-learn@python.org > https://mail.python.org/mailman/listinfo/scikit-learn >
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