Home

Supplementary information for the course

Tutorials

• [http://nbviewer.ipython.org/gist/rpmuller/5920182 A Crash Course in Python for Scientists]
• [https://docs.scipy.org/doc/numpy-dev/user/quickstart.html Numpy]
• [http://nbviewer.jupyter.org/github/jrjohansson/scientific-python-lectures/blob/master/Lecture-4-Matplotlib.ipynb Matplotlib]
• [http://www.scipy-lectures.org/ Scipy Lecture Notes]
• [http://pandas.pydata.org/pandas-docs/stable/10min.html Pandas]
• [http://scikit-learn.org/stable/tutorial/basic/tutorial.html Scikit-learn]

More information

• A classical free book on machine learning. [http://web.stanford.edu/~hastie/local.ftp/Springer/ESLII_print10.pdf Elements of Statistical Learning (Trevor Hastie, Robert Tibshirani, Jerome Friedman)]
• [http://python.org Official python site]
• Necessary libraries: [http://www.numpy.org/ NumPy], [http://pandas.pydata.org/ Pandas], [http://scikit-learn.org/stable/ SciKit-Learn], [http://matplotlib.org/ Matplotlib].
• [https://github.com/ipython/ipython/wiki/A-gallery-of-interesting-IPython-Notebooks A gallery of ineresting IPython notebooks]