Machine Learning – Bagging and Random Forest

This course introduces Ensemble techniques and apply the concepts of Bagging and Random Forest to find solutions to real-world problems. and predictability of the machine learning model.

  • icon-videos 1 Video
  • icon-data 2 Data Cases
  • icon-reading 1 Reading
  • icon-quiz 1 Quiz
Machine Learning - Bagging and Random Forest
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    Difficulty: Advanced

    Prior education or professional experience required

  • Asset 1
    Duration: Approximately 5 hours

Course Overview

  • The course familiarizes the learner to understand the working principle of Bagging ensemble technique
  • The course deep dive into the concept of Random Forest, an algorithm built on the Bagging ensemble technique.
  • Learn the concept of variance tradeoff in Bagging algorithms.
  • Gain in-depth information on pros and cons of Bagging algorithms.

What’s included

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Lifetime Access

Access this project for life once completed

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Flexible Scheduling

Start learning online immediately, at your own pace

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Desktop Only

We recommend completing this project on a desktop

Skills you will learn

Bagging Ensemble Technique

Random Forest Algorithm

Variance Tradeoff with Bagging Techniques

Variance Tradeoff with Bagging Techniques

Machine Learning

Data Analysis

Bagging

Random Forest Javascript

Associated Learning Tracks

Syllabus

This module discuss bagging ensemble technique and random forest algorithm.

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