Decision Tree Modelling using R Online Training | Edureka

Decision Tree Modeling Using R Certification Training

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Become a Decision Tree Modeling expert using R platform by mastering concepts like Data design, Regression Tree, Pruning and various algorithms like CHAID, CART, ID3, GINI and Random forest.
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Online Self Learning Courses are designed for self-directed training, allowing participants to begin at their convenience with structured training and review exercises to reinforce learning. You'll learn through videos, PPTs and complete assignments, projects and other activities designed to enhance learning outcomes, all at times that are most convenient to you.
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Learning Objectives - In this module, you will understand What is a Decision Tree and what are the benefits. What are the core objectives of Decision Tree modelling, How to understand the gains from the Decision Tree and How does one apply the same in business scenarios

Topics - Decision Tree modeling Objective, Anatomy of a Decision Tree, Gains from a decision tree (KS calculations), and Definitions related to objective segmentations

Learning Objectives - In this module, you will learn how to design the data for modelling

Topics - Historical window, Performance window, Decide performance window horizon using Vintage analysis, General precautions related to data design

Learning Objectives - In this module, you will learn how to ensure Data Sanity check and you will also learn to perform the necessary checks before modelling 

Topics - Data sanity check-Contents, View, Frequency Distribution, Means / Uni-variate, Categorical variable treatment, Missing value treatment guideline, capping guideline

Learning Objectives - In this module, you will learn to use R and the Algorithm to develop the Decision Tree. 

Topics - 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

Learning Objectives - In this module you will understand how Classification trees are Developed, Validated and Used in the industry 

Topics - 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.

Learning Objectives - In this module you will understand the Advance stopping criteria of a decision tree. You will also learn to develop Decision Trees for numerous outcomes.

Topics - 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

Learning Objectives - In this module you will learn what is Chi square and CHAID and their working and also the difference between CHAID and CART etc.. 

Topics - Key features of CART, Chi square statistics, Implement Chi square for decision tree development, Syntax for CHAID using R, and CHAID vs CART.

Learning Objectives - In this module you will learn about ID3, Entropy, Random Forest and Random Forest using R 

Topics - Entropy in the context of decision tree, ID3, Random Forest Method and Using R for Random forest method, Project work 

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Gagan Maheshwari

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Sunil Bhardwaj link Solution Architect

The courses are too good.These courses are providing live instruction so-that any time , we can play and learn. Also it provides labs so-that we can practice and implement live.I have joined AWS , R and Tableau which provides me great learning as they provides previous videos sessions , which provides great help before going in main session.Edureka courses provides lifetime access for the courses so that any time we can start playback session and learn and if any problem in any courses then we can also take live class without giving any fees further.

Michael Harkins link Solution Engineer-Open Source Analytics at IBM, former Systems Architect at Hortonworks

The courses are top rate. The best part is live instruction, with playback. You get all the presentations and labs. Great instructions. But my favorite feature is viewing a previous class. They provide a set of videos from a previous session, so you can watch the course before you participate. This way you can get the most out of the course. Also, they are always there to answer questions, and prompt when you open an issue if you are having any trouble. Added bonus ~ you get lifetime access to the course you took!!! I have taken so many courses and then not really gotten to work with a technology until I forgot most of what was taught. Edureka lets you go back later, when your boss says I want this ASAP!" ~ This is the killer education app... I've take two courses and I'm taking two more. Love these guys."

Sudhiranjan Sarma link Analytics and Information management, Cognizant

My experience with edureka about Python is much impressed, with such quality of the training. We get access to each day class recorded sessions after the live class. Edureka is very quick reactive towards the queries of each single user, they answer and follow-up to ensure you get the correct response to resolve your query. Online recording of the course is very useful as you can go back and refer at any time. You have access to course and materials for ever, which is really helpful. More over 24/7 quality support, which is very much important to any of us. I must thank to Edureka for giving such support. Thanks guys, cheer…

Karunakar Reddy link Senior System Analyst at Santander UK

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Shilpa Chutake link Financial Analyst, JPMorgan Chase & Co.

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Amit Vij link HRSSC HRIS Senior Advisor at DLA Piper

I am not a big fan of online courses and also opted for class room based training sessions in past. Out of surprise, I had a WoW factor when I attended first session of my MSBI course with Edureka. Presentation - Check, Faculty - Check, Voice Clarity - Check, Course Content - Check, Course Schedule and Breaks - Check, Revisting Past Modules - Awesome with a big check. I like the way classes were organised and faculty was far above beyond expectations. I will recommend Edureka to everyone and will personally revisit them for my future learnings.

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For your practical work, we will help you setup Edureka's Virtual Machine in your System. This will be a local access for you. The required installation guide is present in LMS.

Course Duration

You will undergo self-paced learning where you will get an in-depth knowledge of various concepts that will be covered in the course.

Real-life Case Studies

Towards the end of the course, you will be working on a project where you are expected to implement the techniques learnt during the course.

Assignments

Each module will contain practical assignments, which can be completed before going to next module.

Lifetime Access

You will get lifetime access to all the videos,discussion forum and other learning contents inside the Learning Management System.

Certification

edureka certifies you as an expert in Decision Tree Modeling in R based on the project reviewed by our expert panel.

Forum

We have a community forum for all our customers that further facilitates learning through peer interaction and knowledge sharing.
As soon as you enrol into the course, your LMS (The Learning Management System) access will be functional. You will immediately get access to our course content in the form of a complete set of Videos, PPTs, PDFs and Assignments. You can start learning right away.
To help you in this endeavor, we have added a resume builder tool in your LMS. Now, you will be able to create a winning resume in just 3 easy steps. You will have unlimited access to use these templates across different roles and designations. All you need to do is, log in to your LMS and click on the "create your resume" option.
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