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Ensemble Learning with R

  • Course Code: Artificial Intelligence - Ensemble Learning with R
  • Course Dates: Contact us to schedule.
  • Course Category: Big Data & Data Science Duration: 3 Days Audience: This course is geared for Python experienced developers, analysts or others who are intending to Explore powerful R packages to create predictive models using ensemble methods

Course Snapshot 

  • Duration: 3 days 
  • Skill-level: Foundation-level Ensemble Learning with R skills for Intermediate skilled team members. This is not a basic class. 
  • Targeted Audience: This course is geared for Python experienced developers, analysts or others who are intending to Explore powerful R packages to create predictive models using ensemble methods 
  • Hands-on Learning: This course is approximately 50% hands-on lab to 50% lecture ratio, combining engaging lecture, demos, group activities and discussions with machine-based student labs and exercises. Student machines are required. 
  • Delivery Format: This course is available for onsite private classroom presentation, or remote instructor led delivery, or CBT/WBT (by request). 
  • Customizable: This course may be tailored to target your specific training skills objectives, tools of choice and learning goals. 

Ensemble techniques are used for combining two or more similar or dissimilar machine learning algorithms to create a stronger model. Such a model delivers superior prediction power and can give your datasets a boost in accuracy. Ensemble Learning with R begins with the important statistical resampling methods. You will then walk through the central trilogy of ensemble techniques – bagging, random forest, and boosting – then you’ll learn how they can be used to provide greater accuracy on large datasets using popular R packages. You will learn how to combine model predictions using different machine learning algorithms to build ensemble models. In addition to this, you will explore how to improve the performance of your ensemble models. By the end of this course, you will have learned how machine learning algorithms can be combined to reduce common problems and build simple efficient ensemble models with the help of real-world examples. 

Working in a hands-on learning environment, led by our Ensemble Learning with R instructor, students will learn about and explore: 

  • Implement machine learning algorithms to build ensemble-efficient models 
  • Explore powerful R packages to create predictive models using ensemble methods 
  • Learn to build ensemble models on large datasets using a practical approach. 

Topics Covered: This is a high-level list of topics covered in this course. Please see the detailed Agenda below 

  • Carry out an essential review of re-sampling methods, bootstrap, and jackknife 
  • Explore the key ensemble methods: bagging, random forests, and boosting 
  • Use multiple algorithms to make strong predictive models 
  • Enjoy a comprehensive treatment of boosting methods 
  • Supplement methods with statistical tests, such as ROC 
  • Walk through data structures in classification, regression, survival, and time series data 
  • Use the supplied R code to implement ensemble methods 
  • Learn stacking method to combine heterogeneous machine learning models 

Audience & Pre-Requisites 

This course is geared for attendees with Python skills who wish to Explore powerful R packages to create predictive models using ensemble methods 

Pre-Requisites:  Students should have  

  • Basic to Intermediate IT Skills. Attendees without a programming background like Python may view labs as follow along exercises or team with others to complete them. 
  • Good foundational mathematics or logic skills 
  • Basic Linux skills, including familiarity with command-line options such as ls, cd, cp, and su 

Course Agenda / Topics 

  1. Introduction to Ensemble Techniques 
  • Introduction to Ensemble Techniques 
  • Datasets 
  • Statistical/machine learning models 
  • The right model dilemma! 
  • An ensemble purview 
  • Complementary statistical tests 
  1. Bootstrapping 
  • Bootstrapping 
  • Technical requirements 
  • The jackknife technique 
  • Bootstrap – a statistical method 
  • The boot package 
  • Bootstrap and testing hypotheses 
  • Bootstrapping regression models 
  • Bootstrapping survival models* 
  • Bootstrapping time series models* 
  1. Bagging 
  • Bagging 
  • Technical requirements 
  • Classification trees and pruning 
  • Bagging 
  • k-NN classifier 
  • k-NN bagging 
  1. Random Forests 
  • Random Forests 
  • Technical requirements 
  • Random Forests 
  • Variable importance 
  • Proximity plots 
  • Random Forest nuances 
  • Comparisons with bagging 
  • Missing data imputation 
  • Clustering with Random Forest 
  1. The Bare Bones Boosting Algorithms 
  • The Bare Bones Boosting Algorithms 
  • Technical requirements 
  • The general boosting algorithm 
  • Adaptive boosting 
  • Gradient boosting 
  • Using the adabag and gbm packages 
  • Variable importance 
  • Comparing bagging, random forests, and boosting 
  1. Boosting Refinements 
  • Boosting Refinements 
  • Technical requirements 
  • Why does boosting work? 
  • The gbm package 
  • The xgboost package 
  • The h2o package 
  1. The General Ensemble Technique 
  • The General Ensemble Technique 
  • Technical requirements 
  • Why does ensembling work? 
  • Ensembling by voting 
  • Ensembling by averaging 
  • Stack ensembling 
  1. Ensemble Diagnostics 
  • Ensemble Diagnostics 
  • Technical requirements 
  • What is ensemble diagnostics? 
  • Ensemble diversity 
  • Pairwise measure 
  • Interrating agreement 
  1. Ensembling Regression Models 
  • Ensembling Regression Models 
  • Technical requirements 
  • Pre-processing the housing data 
  • Visualization and variable reduction 
  • Regression models 
  • Bagging and Random Forests 
  • Boosting regression models 
  • Stacking methods for regression models 
  1. Ensembling Survival Models 
  • Ensembling Survival Models 
  • Core concepts of survival analysis 
  • Nonparametric inference 
  • Regression models – parametric and Cox proportional hazards models 
  • Survival tree 
  • Ensemble survival models 
  1. Ensembling Time Series Models 
  • Ensembling Time Series Models 
  • Technical requirements 
  • Time series datasets 
  • Time series visualization 
  • Core concepts and metrics 
  • Essential time series models 
  • Bagging and time series 
  • Ensemble time series models 
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