Practical Machine Learning

Instructor: Alejandro Correa Bahnsen

• email: al.bahnsen@gmail.com
• twitter: @albahnsen
• github: albahnsen

Requiriments

• Python version 3.5;
• Numpy, the core numerical extensions for linear algebra and multidimensional arrays;
• Scipy, additional libraries for scientific programming;
• Matplotlib, excellent plotting and graphing libraries;
• IPython, with the additional libraries required for the notebook interface.
• Pandas, Python version of R dataframe
• Seaborn, used mainly for plot styling
• scikit-learn, Machine learning library!

A good, easy to install option that supports Mac, Windows, and Linux, and that has all of these packages (and much more) is the Anaconda.

GIT!! Unfortunatelly out of the scope of this class, but please take a look at these tutorials

Sessions

Session	Notebook link	Exercises
1	Introduction to Python	01.1 - Finding digits of Pi, 01.2 - OLS and Numpy
2	Introduction to Machine Learning	02 - Churn Model