Skip To Main Content
Intel logo - Return to the home page
My Tools

Select Your Language

  • Bahasa Indonesia
  • Deutsch
  • English
  • Español
  • Français
  • Português
  • Tiếng Việt
  • ไทย
  • 한국어
  • 日本語
  • 简体中文
  • 繁體中文
Sign In to access restricted content

Using Intel.com Search

You can easily search the entire Intel.com site in several ways.

  • Brand Name: Core i9
  • Document Number: 123456
  • Code Name: Emerald Rapids
  • Special Operators: “Ice Lake”, Ice AND Lake, Ice OR Lake, Ice*

Quick Links

You can also try the quick links below to see results for most popular searches.

  • Product Information
  • Support
  • Drivers & Software

Recent Searches

Sign In to access restricted content

Advanced Search

Only search in

Sign in to access restricted content.

The browser version you are using is not recommended for this site.
Please consider upgrading to the latest version of your browser by clicking one of the following links.

  • Safari
  • Chrome
  • Edge
  • Firefox

Ignite Your AI Solutions on CPUs and GPUs

Ignite Your AI Solutions on CPUs and GPUs

@IntelDevTools

Subscribe Now

Stay in the know on all things CODE. Updates are delivered to your inbox.

Sign Up

Overview

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

Jump to:

Featured Software

You May Also Like
 


 

Featured Software

  • 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*.

 

   

You May Also Like

Related Articles

Accelerate Linear Regression Models for Machine Learning

Speed Up Databricks* Runtime for Machine Learning with Intel®-optimized Libraries

Software AI Accelerators: AI Performance Boost for Free

Scale Your pandas Workflow with Modin*—No Rewrite Required

Related Videos

Drive 2x Performance into Your scikit-learn Machine Learning Tasks

Machine Learning 101 with Python and daal4py

Fast, Scalable Data Analytics & Machine Learning with Python

  • Company Overview
  • Contact Intel
  • Newsroom
  • Investors
  • Careers
  • Corporate Responsibility
  • Inclusion
  • Public Policy
  • © Intel Corporation
  • Terms of Use
  • *Trademarks
  • Cookies
  • Privacy
  • Supply Chain Transparency
  • Site Map
  • Recycling
  • Your Privacy Choices California Consumer Privacy Act (CCPA) Opt-Out Icon
  • Notice at Collection

Intel technologies may require enabled hardware, software or service activation. // No product or component can be absolutely secure. // Your costs and results may vary. // Performance varies by use, configuration, and other factors. Learn more at intel.com/performanceindex. // See our complete legal Notices and Disclaimers. // Intel is committed to respecting human rights and avoiding causing or contributing to adverse impacts on human rights. See Intel’s Global Human Rights Principles. Intel’s products and software are intended only to be used in applications that do not cause or contribute to adverse impacts on human rights.

Intel Footer Logo