Learning Random Forest and It's Nuances

Everything about Random Forest Model

Language: English

Instructors: AI Monks

Validity Period: 365 days

₹499 50.1% OFF

₹249

40% Cashback as Credits

 

Why this course?

Description

Do you know that the Random Forest model is the most commonly applied ensemble machine learning technique for classification and regression? This is also one of the most popular techniques to be asked in data analytics and data scientist job interviews.

In this course on the Random Forest model, we have covered the fundamental concept of Random Forest and tried to understand the implementation and usage in the real world.

Who should opt for this course?

  • Anyone who wishes to develop a theoretical and practical understanding of the random forest model
  • Any student or professional preparing for a job interview and wish to learn the random forest model (or any predictive machine learning technique)        
  • Any professional willing to implement the random forest model at her work
  • Any student willing to work on academic projects using the random forest model

Comprehensive Course Coverage

This course covers Random Forest in great depth and covers the following aspects:

  • Basics of the random forest model
  • Decision trees and mathematics behind the random forest model
  • Terminologies associated with the random forest model
  • Building a random forest model in Python
  • Analyze and interpret the results of the random forest model
  • Applications of random forest in a real-world context
  • Case study to implement and understand the relevance of the random forest model

In case of any query, please reach out to us at info@aimonks.com

Course Curriculum

Introduction to Random Forest Model
Introduction to Random Forest Model (3 pages)
Understanding the Random Forest Model
Decision Trees (3 pages)
Information Gain and Gini Index (4 pages)
Random Forest (3 pages)
Interpreting and Tuning the Random Forest Model
Variable Importance (3 pages)
Model Checks (2 pages)
Tuning the Model (2 pages)
Building Random Forest Model in Python
Data Preparation (3 pages)
Extracting the Variables (5 pages)
Training and Evaluating the Model (3 pages)
Tuning and Improving the Model (5 pages)
Learning Random Forest Through Case Study
Learning Random Forest Through Case Study (5 pages)

How to Use

After successful purchase, this item would be added to your courses.You can access your courses in the following ways :

  • From the computer, you can access your courses after successful login
  • For other devices, you can access your library using this web app through browser of your device.

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