How to use Scikit-learn for Machine Learning with Python? – Mastering Scikit

Scikit-learn is one of the common libraries for Machine Learning in Python. It provides a wide range of what I call traditional Machine Learning Algorithms – such as Support Vector Machines, Decision Trees and simple Neural Networks. It is very accessible and can produce reusable models. What’s best is that it is open source!

In other words, if you do not need Deep Neural Networks – and hence TensorFlow or PyTorch – Scikit-learn can be a good choice for your predictive modelling project. On this page, you will find a wide range of tutorials for Scikit-learn. With them, you can both learn to code Machine Learning models, and learn how they work at the same time.

Let’s get started! 😎



Data Preprocessing

Feature Scaling

Feature Extraction and Selection

Other techniques


Classification

  • Overview of Classification Algorithms in Scikit-Learn and Python

Support Vector Machines


Regression

Linear Regression

Support Vector Machines


Clustering


Model evaluation


Model visualization