ML Explained

Welcome to ML Explained, an educational website to sharpen your Machine Learning knowledge!

Background

Hi! We, Jordan and Akshil are co-authors of ML Explained, a resource that aims to educate those new to Data Science - specifically Machine Learning and Reinforcement Learning. We both are in the process of completing our PhDs at the University of Bath. During our time as PhD students we found that there was not a centralised pool of knowledge online. This has motivated us to attempt to fill in the gaps with tutorial-like posts where we implement various concepts and algorithms all here!

We have a few tutorial channels, each with a different theme or purpose.

How to: Machine Learning

A beginner’s guide to machine learning designed for those that know a little Python and some key terms. Suited for those in education who want to understand the algorithms. A from scratch attitude is adopted here whereby most things will be built using numpy and scipy instead of importing off-the-shelf algorithms from sklearn (scikit-learn).

How to: Reinforcement Learning

An introduction to Reinforcement Learning starting from what it is, and going from the basics with Markov Decision Processes to function approximation with Neural Networks. Tabular methods are implemented in numpy whilst function approximation is in PyTorch.

Advanced Applications

For those that are already well-versed in the field, this channel builds further on the How to tutorials series, applying ML and RL techniques on more interesting and more realistic problems.

Theories

For those that want the more formal derivations, this channel aims to equip you with the Mathematical knowledge to understand the main assumptions behind these Data Science concepts.

Installation

If you would like to access our datasets or execute our code on your problems, first make sure you have git and pip readily available within your terminal then execute the following

# optionally create a virtual environment
python3 -m venv env

# activate virtual environment
# linux / macos
source env/bin/activate

# windows
.\env\Scripts\activate

# install from our GitHub repository
pip install -e git+https://github.com/ml-explained/ml-explained.git#egg=ml_explained