Snap ML is a library for accelerated training and inference of Machine Learning models such as linear models, decision trees, random forests and boosting machines. It’s a library developed and maintained by IBM Research. The library binaries are freely available on PyPi. It supports Linux/x86, Linux/Power, MacOS, Windows, Linux/Z. GPU support is also available for Linux. If you are curious, you can find detailed documentation here and usage examples here.

We will focus on training acceleration in particular. You will consolidate your machine learning (ML) modelling skills by using two popular classification models to recognize fraudulent credit card transactions. They are the Decision Tree and Support Vector Machine. You will use a real-world dataset to train such models. 

You will find out that a Scikit-learn application can be seamlessly optimized by using Snap ML. The seamless integration of the Snap ML library is possible due to its Scikit-learn Python API compatibility.

To complete this guided project, you will need a basic understanding of the working mechanics of the Decision Tree and Support Vector Machine models. You will also need some prior experience working with Scikit-learn APIs to be able to follow our data preprocessing steps easily.

This course mainly uses Python and JupyterLabs. Although these skills are recommended prerequisites, no prior experience is required as this Guided Project is designed for complete beginners.

Frequently Asked Questions

Do I need to install any software to participate in this project?
Everything you need to complete this project will be provided to you via the Skills Network Labs and it will all be available via a standard web browser.

What web browser should I use?
The Skills Network Labs platform works best with current versions of Chrome, Edge, Firefox, Internet Explorer, or Safari.