CW you might want to read http://greenteapress.com/wp/think-python/ (available as free pdf) (for basics of programming and python) and Python for Data Analysis Data Wrangling with Pandas, NumPy, and IPython, O'reilly
(for data analysis libraries: pandas, numpy, ipython...) On Sun, Jun 18, 2017 at 10:18 PM, C W <tmrs...@gmail.com> wrote: > 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 >> > > > _______________________________________________ > scikit-learn mailing list > scikit-learn@python.org > https://mail.python.org/mailman/listinfo/scikit-learn > >
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