The following repo is a collection of machine learning algorithms that I have implemented myself using numpy.

The algorithms are written up in Jupyter notebooks along side notes for the maths behind each algorithm (written inline using Mathjax).

To view the notebooks yourself simply clone the repo, set up a virtual env, then run

pip install -r requirements.txt
jupyter notebook

So far I have implemented the following algorithms:

• Linear Regression with one variable using gradient descent;
• Multi-variable Linear Regression using gradient descent;
• K-means Clustering using Euclidean distance metric.

• Visualise the output for multilinear regression
• Decision Trees (ID3)
• Support Vector Machines
• Logistic Regression
• k-Nearest Neighbours