Machine Learning Advanced: Decision Trees in R

Simpliv

Course

Online

£ 10 VAT inc.

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Description

  • Type

    Course

  • Methodology

    Online

  • Start date

    Different dates available

The course is created on the basis of three pillars of learning:Know (Study)
Do (Practice)
Review (Self feedback)KnowWe have created a set of concise and comprehensive videos to teach you all the Excel related skills you will need in your professional career.DoWith 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.ReviewCheck 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

Facilities

Location

Start date

Online

Start date

Different dates availableEnrolment now open

About this course

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

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

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This centre's achievements

2021

All courses are up to date

The average rating is higher than 3.7

More than 50 reviews in the last 12 months

This centre has featured on Emagister for 6 years

Course programme

Setting up R Studio and R Crash Course 8 lectures 01:01:38 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 Setting up R Studio and R Crash Course 8 lectures 01:01:38 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 Installing R and R studio Installing R and R studio Installing R and R studio Installing R and R studio Basics of R and R studio Basics of R and R studio Basics of R and R studio Basics of R and R studio Packages in R Packages in R Packages in R Packages in R Inputting data part 1: Inbuilt datasets of R Inputting data part 1: Inbuilt datasets of R Inputting data part 1: Inbuilt datasets of R Inputting data part 1: Inbuilt datasets of R Inputting data part 2: Manual data entry Inputting data part 2: Manual data entry Inputting data part 2: Manual data entry Inputting data part 2: Manual data entry Inputting data part 3: Importing from CSV or Text files Inputting data part 3: Importing from CSV or Text files Inputting data part 3: Importing from CSV or Text files Inputting data part 3: Importing from CSV or Text files Creating Barplots in R Creating Barplots in R Creating Barplots in R Creating Barplots in R Creating Histograms in R Creating Histograms in R Creating Histograms in R Creating Histograms in R Machine Learning Basics 2 lectures 24:46 Introduction, Key concepts and Examples Steps in building an ML model Machine Learning Basics 2 lectures 24:46 Introduction, Key concepts and Examples Steps in building an ML model Introduction, Key concepts and Examples Introduction, Key concepts and Examples Introduction, Key concepts and Examples Introduction, Key concepts and Examples Steps in building an ML model Steps in building an ML model Steps in building an ML model Steps in building an ML model Simple Decision trees 10 lectures 01:15:31 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 Simple Decision trees 10 lectures 01:15:31 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 Basics of Decision Trees Basics of Decision Trees Basics of Decision Trees Basics of Decision Trees Understanding a Regression Tree Understanding a Regression Tree Understanding a Regression Tree Understanding a Regression Tree The stopping criteria for controlling tree growth The stopping criteria for controlling tree growth The stopping criteria for controlling tree growth The stopping criteria for controlling tree growth The Data set for the Course The Data set for the Course The Data set for the Course The Data set for the Course Importing the Data set into R Importing the Data set into R Importing the Data set into R Importing the Data set into R Splitting Data into Test and Train Set in R Splitting Data into Test and Train Set in R Splitting Data into Test and Train Set in R Splitting Data into Test and Train Set in R Building a Regression Tree in R Building a Regression Tree in R Building a Regression Tree in R Building a Regression Tree in R Pruning a tree Pruning a tree Pruning a tree Pruning a tree Pruning a Tree in R Pruning a Tree in R Pruning a Tree in R Pruning a Tree in R Building a classification Tree in R Building a classification Tree in R Building a classification Tree in R Building a classification Tree in R Ensemble technique 1 - Bagging 1 lecture 06:20 Bagging in R Ensemble technique 1 - Bagging 1 lecture 06:20 Bagging in R Bagging in R Bagging in R Bagging in R Bagging in R Ensemble technique 2 - Random Forest 1 lecture 03:58 Random Forest in R Ensemble technique 2 - Random Forest 1 lecture 03:58 Random Forest in R Random Forest in R Random Forest in R Random Forest in R Random Forest in R Ensemble technique 3 - Boosting 3 lectures 33:03 Gradient Boosting in R AdaBoosting in R XGBoosting in R Ensemble technique 3 - Boosting

Additional information

Students will need to install R Studio software but we have a separate lecture to help you install the same

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Machine Learning Advanced: Decision Trees in R

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