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Decision Tree Modeling using R Certification Training

4.5( 4 REVIEWS )
144 STUDENTS
Overview Getting a crystal clear understanding of Decision Tree Modeling Using R Certification Training is vital to apply the most …

Overview

Getting a crystal clear understanding of Decision Tree Modeling Using R Certification Training is vital to apply the most in-demand technique in the analytics industry. This training will enable you to use specialised skills in a number of business scenarios such as telecom, automobile, and manufacturing industry. If you are interested in learning the Decision Tree algorithm, this is the right course to get started. 

The Decision Tree Modeling Using R Certification Training will allow you to learn what decision tree is, where to apply it, what the benefits are, what different algorithms behind it are, and how you can develop a decision tree using R. This course covers rich material through videos in HD format, PDF files, downloadable Excel files, and R Software. 

The Decision Tree Modeling Using R Certification Training will also teach you advanced concepts such as Meta and Graph Patterns, Input-Output Patterns, Graph Patterns etc. Learning valuable concepts such as Pruning, CHAID, CART, and Regression Tree will set you on a course of continued success in the challenging industry. Consider taking this course as soon as possible if you want to excel as an in-demand Decision Tree professional. 

Why You Should Take This Training at Study365? 

Study365 is a leading online provider for several accrediting bodies and provides learners with the opportunity to take this exclusive course awarded by Edureka. At Study365, we give our fullest attention to our learners’ needs and ensure they have the necessary information to proceed with the Course. 

Learners who register will be given excellent support, discounts for future purchases and be eligible for a TOTUM Discount card and Student ID card with amazing offers and access to retail stores, the library, cinemas, gym memberships, and their favourite restaurants.

  • Access Duration
  • Who Is This Course For? 
  • Entry Requirements
  • Certification
  • Awarding Body
  • Method of Assessment
  • Career Path & Progression

The course will be directly delivered to you, and you have 12 months of access to the online learning platform from the date you joined the course. The course is self-paced and you can complete it in stages, revisiting the lessons at any time. 

This course is recommended for flourishing your career as a:  

  • Decision Tree Expert
  • Decision Tree Analyst 
  • Business Manager 
  • Consultant and Trainer 
  • Learners should have a basic know-how of English, ICT, and numeracy. 
  • A sound educational background is preferable for this course.
  • Basic R programming skills are desirable.  

Upon successfully completing the course, you will be awarded the 'Decision Tree Modelling using R Certification Training' by Edureka.

Edureka is the fastest growing online learning platform with a trusted name in the industry. The platform has the highest course completion rate and turns beliefs into realities by ridiculously committing to their students. Edureka collaborates with Study365 and many other educational bodies to provide guaranteed learning and success to global students & professionals.

To successfully obtain this Edureka certification, learners will have to submit an assignment that proves their worth and skill related to this particular course. 

If you want to become seasoned analytics professional in the challenging IT industry, the Decision Tree Modeling Using R Certification Training will give you the skills and knowledge to make an impact. You will apply techniques to perform actionable analytics and become an influential data scientist or strategist. The top organisations will recognise your skills and consider you a viable candidate for the most challenging roles. 

Given below are job titles you can compete for, along with the average UK salary per annum, according to https://www.glassdoor.com.

  • Decision Tree Analyst - £95,000 (Approximately)

Course Curriculum

1. Introduction to Decision Tree
Decision Tree modeling Objective
Anatomy of a Decision Tree
Gains from a decision tree (KS calculations)
Definitions related to objective segmentations
2. Data design for Modelling
Historical window
Performance window
Decide performance window horizon using Vintage analysis
General precautions related to data design
3. Data treatment before Modelling
Data sanity check-Contents
View
Frequency Distribution
Means / Uni-variate
Categorical variable treatment
Missing value treatment guideline
capping guideline
4. Classification of Tree development and Algorithm details
Preamble to data
Installing R package and R studio
Developing first Decision Tree in R studio
Find strength of the model
Algorithm behind Decision Tree
How is a Decision Tree developed?
First on Categorical dependent variable
GINI Method
Steps taken by software programs to learn the classification (develop the tree)
Assignment on decision tree
5. Industry practice of Classification tree-Development, Validation and Usage
Discussion on assignment
Find Strength of the model
Steps taken by software program to implement the learning on unseen data
learning more from practical point of view
Model Validation and Deployment.
6. Regression Tree and Auto Pruning
Introduction to Pruning
Steps of Pruning
Logic of pruning
Understand K fold validation for model
Implement Auto Pruning using R
Develop Regression Tree
Interpret the output
How it is different from Linear Regression
Advantages and Disadvantages over Linear Regression
Another Regression Tree using R
7. CHAID Algorithm
Key features of CART
Chi square statistics
Implement Chi square for decision tree development
Syntax for CHAID using R
CHAID vs CART
8. Other Algorithms
Entropy in the context of decision tree
ID3
Random Forest Method and Using R for Random forest method
Project work

Students feedback

4.5

Avarage rating (4)
4.5
5 Star
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1 Star
    A B

    Ali Brooks

    August 20, 2021
    Exceptional course

    The course has covered everything that you need to know when learning about the Decision tree modeling and its various applications in the real world.

    C P

    Cory Palmer

    July 17, 2021
    Course devised for me

    This is how courses should be devised. I feel as if this course was prepared especially for me.

    R P

    Reed Porter

    May 04, 2021
    Hard concepts made simple

    I had no problems at all understanding the various concepts explained in the course which I didn’t understand before

    G H

    Glenn Hart

    January 15, 2021
    Becoming an expert

    I feel as if my knowledge base has grown and I can expertly use MapReduce.

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