Introduction to Machine Learning with Python

Come out this Friday to learn some basic Machine learning! We'll be teaching some simple classifiers to help you get started with Machine Learning, and help you understand why it's used and its applications. Python is a great choice because it comes with scikit-learn, which is one of the more powerful libraries in Python for Machine Learning. Some of the simple classifiers we'll go through are: The perceptron algorithm, decision trees, and some specific and interesting algorithms built into scikit-learn. Come having installed scikit-learn in Python (we'll have some guides to help you).

If you're a student it's quite easy for you to get all of these packages using the Enthought Canopy applications that are built for scientific computing - https://store.enthought.com/#canopy-academic. You can also look at Anaconda, which is very similar - https://store.continuum.io/cshop/anaconda/. You should install sci-kit learn after you download this application, and play around with it (if not we'll help you on the day).

If you're not a student then you can install it using the Python Package Manger (pip), and install scikit-learn. It's great to have these environments as they take care of the dependencies and allow you to run your code in a environment with everything installed and taken care of. Scikit-learn also comes with a couple datasets that you're able to play with out of the box, which helps!

Snow Pettersen will be teaching this class - he's a senior at New York University who has experience with Machine Learning, and has experience teaching.