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Extreme Gradient Boosting with XGBoost

IntermediateSkill Level
4.7+
57 reviews
Updated 09/2024
Learn the fundamentals of gradient boosting and build state-of-the-art machine learning models using XGBoost to solve classification and regression problems.
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PythonMachine Learning4 hours16 videos49 Exercises3,750 XP55,438Statement of Accomplishment

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Course Description

Do you know the basics of supervised learning and want to use state-of-the-art models on real-world datasets? Gradient boosting is currently one of the most popular techniques for efficient modeling of tabular datasets of all sizes. XGboost is a very fast, scalable implementation of gradient boosting, with models using XGBoost regularly winning online data science competitions and being used at scale across different industries. In this course, you'll learn how to use this powerful library alongside pandas and scikit-learn to build and tune supervised learning models. You'll work with real-world datasets to solve classification and regression problems.

Prerequisites

Supervised Learning with scikit-learn
1

Classification with XGBoost

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2

Regression with XGBoost

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3

Fine-tuning your XGBoost model

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4

Using XGBoost in pipelines

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Extreme Gradient Boosting with XGBoost
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Don’t just take our word for it

*4.7
from 57 reviews
81%
16%
4%
0%
0%
  • Foster
    about 21 hours

  • David Jesus
    1 day

  • Karthick
    2 days

  • Carmine
    10 days

  • Hadeel
    15 days

    it was great, more complex examples and more data practice will be fine,

  • Mark
    16 days

    Best teacher so far. So clear in his speaking!

Foster

David Jesus

Karthick

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