Python Workshop Series #5 - Machine Learning Algorithms

This year, we are excited to announce a partnership with the Head of Analytics at Kellogg Joel Shapiro to provide students with more in-depth resources to learn popular coding and data analysis tools! This workshop will be a 6-part series designed for students to learn Python for data analysis basics, visualization, and machine learning basics (regression, clustering, etc.) Many of these skills are used across a variety of different corporate functions and industries today, and will be in high demand as more and more data gets analyzed.

We will be hosting Mohammad Ahamadpoor, a PhD student in the Strategy department. It is HIGHLY recommended that you attend session #1 before attending session #5 if you don't have any experience w/ Python. This fifth session will cover popular machine learning algorithms such as linear and logistic regression, clustering, and PCA. It will be VERY similar to session #3 but use a different data set. This is intended for students who weren't able to make session #3.

CLASS PREPWORK
1) Ensure you have Python 2.7 installed

Python 2.7: (https://www.python.org/downloads/release/python-2714/)

The students will need scroll down until they see Files and click to download the package for their system. If students have Mac OS X 10.6 or later, they will need to download the Mac OS X 64-bit/32-bit installer package. If they have Windows, they will need to download the Windows x86-64 MSI installer package.

I recommend going through the Getting Started page as well. The wiki has a wealth of information if you ever get stuck on something:
Python 2.x Wiki: https://docs.python.org/2.7/

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