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Course: Machine Learning Advanced: Decision Trees in R

Machine Learning Advanced: Decision Trees in R

  • Life Time Access
  • Certificate on Completion
  • Access on Android and iOS App
  • Self-Paced
About this Course

The course is created on the basis of three pillars of learning:

  • Know (Study)
  • Do (Practice)
  • Review (Self feedback)

Know

We have created a set of concise and comprehensive videos to teach you all the Excel related skills you will need in your professional career.

Do

With each lecture, we have provide a practice sheet to complement the learning in the lecture video. These sheets are carefully designed to further clarify the concepts and help you with implementing the concepts on practical problems faced on-the-job.

Review

Check if you have learnt the concepts by comparing your solutions provided by us. Ask questions in the discussion board if you face any difficulty.

Who this course is for:

  • People pursuing a career in data science
  • Working Professionals beginning their Data journey
  • Statisticians needing more practical experience
  • Anyone curious to master Decision Tree technique from Beginner to Advanced in short span of time
Basic knowledge
  • Students will need to install R Studio software but we have a separate lecture to help you install the same
What you will learn
  • Solid understanding of decision tree
  • Understand the business scenarios where decision tree is applicable
  • Tune a machine learning model's hyperparameters and evaluate its performance
  • Use decision trees to make predictions
  • Use R to manipulate data and make statistical computations
Curriculum
Number of Lectures: 25
Total Duration: 03:25:19
Setting up R Studio and R Crash Course
  • Installing R and R studio  
  • Basics of R and R studio  
  • Packages in R  
  • Inputting data part 1: Inbuilt datasets of R  
  • Inputting data part 2: Manual data entry  
  • Inputting data part 3: Importing from CSV or Text files  
  • Creating Barplots in R  
  • Creating Histograms in R  
Machine Learning Basics
  • Introduction, Key concepts and Examples  
  • Steps in building an ML model  
Simple Decision trees
  • Basics of Decision Trees  
  • Understanding a Regression Tree  
  • The stopping criteria for controlling tree growth  
  • The Data set for the Course  
  • Importing the Data set into R  
  • Splitting Data into Test and Train Set in R  
  • Building a Regression Tree in R  
  • Pruning a tree  
  • Pruning a Tree in R  
  • Building a classification Tree in R  
Ensemble technique 1 - Bagging
  • Bagging in R  
Ensemble technique 2 - Random Forest
  • Random Forest in R  
Ensemble technique 3 - Boosting
  • Gradient Boosting in R  
  • AdaBoosting in R  
  • XGBoosting in R  
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