Extreme Gradient Boosting with XGBoost

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Course Features

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Duration

4 hours

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Delivery Method

Online

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Available on

Limited Access

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Accessibility

Mobile, Desktop, Laptop

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Language

English

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Subtitles

English

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Level

Intermediate

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Teaching Type

Self Paced

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Video Content

4 hours

Course Description

Are you familiarized with supervised learning? Are you looking to apply state of the art models to real-world data using state-of-the-art techniques? Gradient boosting is one the most popular methods for efficiently modeling tabular data of any size. XGboost is a fast, scalable solution for gradient boosting that has won numerous online data science contests. XGBoost models are widely used in different industries. This course will show you how to use scikit and pandas to create and tune supervised learning models. Real-world data will be used to solve classification and regression problems.

Course Overview

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Virtual Labs

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International Faculty

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Case Based Learning

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Post Course Interactions

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Case Studies,Hands-On Training,Instructor-Moderated Discussions

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Case Studies, Captstone Projects

Skills You Will Gain

Prerequisites/Requirements

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Supervised Learning with scikit-learn

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Case Study: School Budgeting with Machine Learning in Python

What You Will Learn

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In this course, you'll learn how to use this powerful library alongside pandas and scikit-learn to build and tune supervised learning models

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You'll work with real-world datasets to solve classification and regression problems

Course Instructors

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Sergey Fogelson

VP of Analytics and Measurement Sciences, Viacom

Sergey loves applying his quantitative skills to large-scale data intensive problems, mentoring junior colleagues, and is an avid learner who is always trying to refine his programming chops and to a...

Course Reviews

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Course Cover

$12

$6

49% OFF

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