Want to get started with machine learning in Python? I'll discuss the pros and cons of the scikit-learn library, show how to install my preferred Python distribution, and demonstrate the basic functionality of the IPython Notebook. If you don't yet know any Python, I'll also provide four recommended resources for learning Python. This is the second video in the series: "Introduction to machine learning with scikit-learn". Read more about this video here: blog.kaggle.com/2015/04/15/scikit-learn-video-2-setting-up-python-for-machine-learning/ The IPython notebook shown in the video is available on GitHub: github.com/justmarkham/scikit-learn-videos == RESOURCES == Six reasons why I recommend scikit-learn: radar.oreilly.com/2013/12/six-reasons-why-i-recommend-scikit-learn.html API design for machine learning software: arxiv.org/pdf/1309.0238v1.pdf Should you teach Python or R for data science?: www.dataschool.io/python-or-r-for-data-science/ scikit-learn installation: scikit-learn.org/stable/install.html Anaconda installation: store.continuum.io/cshop/anaconda/ IPython installation: ipython.org/install.html nbviewer: nbviewer.ipython.org/ IPython documentation: ipython.org/ipython-doc/stable/index.html IPython Notebook tutorials: nbviewer.ipython.org/github/ipython/ipython/blob/master/examples/Notebook/Index.ipynb Mastering Markdown: guides.github.com/features/mastering-markdown/ Codecademy's Python course: www.codecademy.com/en/tracks/python DataQuest: dataquest.io/missions Google's Python class: developers.google.com/edu/python/ Python for Informatics: www.pythonlearn.com/ == SUBSCRIBE! == www.youtube.com/user/dataschool?sub_confirmation=1 == LET'S CONNECT! == Blog: www.dataschool.io Newsletter: www.dataschool.io/subscribe/ Twitter: twitter.com/justmarkham GitHub: github.com/justmarkham

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