Applied Machine Learning in Python

This is a highly applied session that is better explored out using your own computer and the code provided. This report is just a quikc intro for the code you can find on our GitHub. There you can find everything you need to do some applied Machine Learning. You can download this repository to your computer using

git clone https://github.com/dtc-coding-dojo/main/tree/master/week_7_ml

Applied Machine Learning in Python

This is our applied machine learning code. Here you will find a little demo script walking you through the basics of unsupervised and supervised Machine learning. I have annotated the code pretty thoroughly to make it digestible. If there are any additional questions please direct them at nicolas.arning@bdi.ox.ac.uk. If you have no experience in ML I highly recommend reading our report here.

All the coding done here is done in scikit-learn which is an easy to handle Machine Learning library which has myriads of features. If you want to know more about the specific algorithms applied here I would recommend looking them up on the sklearn user guide

When you go through the code of ML_demo.py it’s probably a good idea to comment out most of the lines and just run a bit of the code to see what it does. So with all that said just go ahead and dive right in.

Written by Nicolas Arning