scikit-learn* is among the most useful and robust libraries for machine learning. It provides a selection of tools for machine learning and statistical modeling via a consistent interface in Python*, including classification, regression, clustering, and dimensionality reduction.

In this session, data scientist and AI expert Bob Chesebrough showcases the Intel® Extension for Scikit-learn*. Learn how to use it to speed up many standard machine learning algorithms for scikit-learn (such as kmeans, dbscan, and pca) on CPUs with only a few lines of code. He also addresses how changing a few lines of code can target these same kernels for use on GPUs.

This video shows:

• Where to get and how to install the extension, which is part of the AI Frameworks and Tools
• An example scikit-learn algorithm sped up over stock scikit-learn
• A demonstration of the single line of code that enumerates all Intel®-optimized scikit-learn functions
• How to apply the functional patch to activate Intel Extension for Scikit-learn
• How to apply the dpctl command to offload data and computation to an Intel® GPU
• Upcoming hands-on workshops for in-depth information

• Download Intel Extension for Scikit-learn as part of the AI Frameworks and Tools—eight tools and frameworks to accelerate end-to-end data science and analytics pipelines.
• Get the stand-alone Intel Extension for Scikit-learn on GitHub*.