This page organizes all of the machine learning articles you will find on this website.


In Python, it’s perfectly possible to implement a working maching learning model without knowing what that model does. Here is some background. Some models appear under both regressor and classifier headings as they can be used for both.

Linear Regression
Random Forest
Gradient Boosted Trees
KNearest Neighbours

Random Forest
Gradient Boosted Trees
Support Vector Machines
Naive Bayes
KNearest Neighbours

KMeans Clustering


Tuning a model is like adjusting an old school TV – you make small adjustments to the dials until you get the perfect picture. Remember, it’s easier to correct bad data going into the model than it is to tune the model at the end.

Methods of measuring model accuracy
Methods for increasing model accuracy
Dimensionality reduction with Principal Component Analysis
Tuning hyperparameters with gridsearch and random search