Instructor: Bala Subrahmanyam Varanasi

• email: [email protected]
• twitter: @vabasu
• github: Balu-Varanasi

This repository will contain files and other info associated with PyCon India 2013 scikit-learn tutorial.

• Introduce the basics of Machine Learning, and some skills useful in practice.
• Introduce the syntax of scikit-learn, so that you can make use of the rich toolset available.

This tutorial requires latest versions of the following packages:

• Python version
• numpy: http://www.numpy.org/
• scipy: http://www.scipy.org/
• matplotlib: http://matplotlib.org/
• scikit-learn: http://scikit-learn.org
• ipython with notebook support: http://ipython.org

The last one is important, you should be able to type:

ipython notebook

in your terminal window and see the notebook panel load in your web browser.

For users who do not yet have these packages installed, a relatively painless way to install all the requirements is to use a package such as Anaconda CE, which can be downloaded and installed for free.

I would highly recommend using git, not only for this tutorial, but for the general betterment of your life. Once git is installed, you can clone the material in this tutorial by using the git address shown above:

git clone [email protected]:Balu-Varanasi/pycon_2013_india.git

If you can't or don't want to install git, there is a link above to download the contents of this repository as a zip file. I may make minor changes to the repository in the days before the tutorial, however, so cloning the repository is a much better option.

The data for this tutorial is not included in the repository. We will be using several data sets during the tutorial: most are built-in to scikit-learn, which includes code which automatically downloads and caches these data. Because the wireless network at conferences can often be spotty, it would be a good idea to download these data sets before arriving at the conference.

These notebooks in this repository can be statically viewed using the excellent nbviewer site. They will not be able to be modified within nbviewer. To modify them, first download the tutorial repository, change to the notebooks directory, and type ipython notebook. You should see the list in the ipython notebook launch page in your web browser.

• 9:30 - 11:00: Part 1

  • An Introduction to Machine Learning
  • Representation of data and Feature extraction
  • Basics priciples of Machine Learning
  • Supervised learning
    • regression
    • classification
  • Unsupervised learning
    • clustering
    • dimensionality reduction

• 11:00 - 11:10: short break

• 11:10 - 12:00: part 2

  • Validation and testing of models