Let us help you find the training program you are looking for.

If you can't find what you are looking for, contact us, we'll help you find it. We have over 800 training programs to choose from.

Applied Supervised Learning with R

  • Course Code: Artificial Intelligence - Applied Supervised Learning with R
  • Course Dates: Contact us to schedule.
  • Course Category: AI / Machine Learning Duration: 2 Days Audience: This course is geared for Python experienced developers, analysts or others who are intending to Learn the ropes of supervised machine learning with R by studying popular real-world use cases, and understand how it drives object detection in driverless cars, customer churn, and loan default prediction

Course Snapshot 

  • Duration: 2 days 
  • Skill-level: Foundation-level Applied Supervised 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 Learn the ropes of supervised machine learning with R by studying popular real-world use cases, and understand how it drives object detection in driverless cars, customer churn, and loan default prediction  
  • 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. 
  • Customizable: This course may be tailored to target your specific training skills objectives, tools of choice and learning goals. 

R provides excellent visualization features that are essential for exploring data before using it in automated learning. Applied Supervised Learning with R helps you cover the complete process of employing R to develop applications using supervised machine learning algorithms for your business needs. The course starts by helping you develop your analytical thinking to create a problem statement using business inputs and domain research. You will then learn different evaluation metrics that compare various algorithms, and later progress to using these metrics to select the best algorithm for your problem. After finalizing the algorithm you want to use, you will study the hyperparameter optimization technique to fine-tune your set of optimal parameters. The course demonstrates how you can add different regularization terms to avoid overfitting your model. By the end of this course, you will have gained the advanced skills you need for modeling a supervised machine learning algorithm that precisely fulfills your business needs. 

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

  • Study supervised learning algorithms by using real-world datasets 
  • Fine-tune optimal parameters with hyperparameter optimization 
  • Select the best algorithm using the model evaluation framework 

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

  • Develop analytical thinking to precisely identify a business problem 
  • Wrangle data with dplyr, tidyr, and reshape2 
  • Visualize data with ggplot2 
  • Validate your supervised machine learning model using k-fold 
  • Optimize hyperparameters with grid and random search, and Bayesian optimization 
  • Deploy your model on Amazon Web Services (AWS) Lambda with plumber 
  • Improve your model’s performance with feature selection and dimensionality reduction 

Audience & Pre-Requisites 

This course is geared for attendees with Python skills who wish to Learn the ropes of supervised machine learning with R by studying popular real-world use cases, and understand how it drives object detection in driverless cars, customer churn, and loan default prediction 

Pre-Requisites:  Students should have  

  • Basic to Intermediate IT Skills.  
  • Good foundational mathematics or logic skills 
  • No machine learning experience or advanced math skills necessary. 
  • Basic Linux skills, including familiarity with command-line options such as ls, cd, cp, and su 

Course Agenda / Topics 

  1. R for Advanced Analytics 
  • R for Advanced Analytics 
  • Introduction 
  • Working with Real-World Datasets 
  • Reading Data from Various Data Formats 
  • Write R Markdown Files for Code Reproducibility 
  • Data Structures in R 
  • DataFrame 
  • Data Processing and Transformation 
  • The Apply Family of Functions 
  • Useful Packages 
  • Data Visualization 
  • Line Charts 
  • Histogram 
  • Boxplot 
  1. Exploratory Analysis of Data 
  • Exploratory Analysis of Data 
  • Introduction 
  • Defining the Problem Statement 
  • Understanding the Science Behind EDA 
  • Exploratory Data Analysis 
  • Univariate Analysis 
  • Exploring Categorical Features 
  • Bivariate Analysis 
  • Studying the Relationship between Two Numeric Variables 
  • Studying the Relationship between a Categorical and a Numeric Variable 
  • Studying the Relationship Between Two Categorical Variables 
  • Multivariate Analysis 
  • Validating Insights Using Statistical Tests 
  • Categorical Dependent and Numeric/Continuous Independent Variables 
  • Categorical Dependent and Categorical Independent Variables 
  1. Introduction to Supervised Learning 
  • Introduction to Supervised Learning 
  • Introduction 
  • Summary of the Beijing PM2.5 Dataset 
  • Regression and Classification Problems 
  • Machine Learning Workflow 
  • Regression 
  • Exploratory Data Analysis (EDA) 
  • Classification 
  • Evaluation Metrics 
  1. Regression 
  • Regression 
  • Introduction 
  • Linear Regression 
  • Model Diagnostics 
  • Residual versus Fitted Plot 
  • Normal Q-Q Plot 
  • Scale-Location Plot 
  • Residual versus Leverage 
  • Improving the Model 
  • Quantile Regression 
  • Polynomial Regression 
  • Ridge Regression 
  • LASSO Regression 
  • Elastic Net Regression 
  • Poisson Regression 
  • Cox Proportional-Hazards Regression Model 
  • NCCTG Lung Cancer Data 
  1. Classification 
  • Classification 
  • Introduction 
  • Getting Started with the Use Case 
  • Classification Techniques for Supervised Learning 
  • Logistic Regression 
  • How Does Logistic Regression Work? 
  • Evaluating Classification Models 
  • What Metric Should You Choose? 
  • Evaluating Logistic Regression 
  • Decision Trees 
  • XGBoost 
  • Deep Neural Networks 
  • Choosing the Right Model for Your Use Case 
  1. Feature Selection and Dimensionality Reduction 
  • Feature Selection and Dimensionality Reduction 
  • Introduction 
  • Feature Engineering 
  • One-Hot Encoding 
  • Log Transformation 
  • Feature Selection 
  • Highly Correlated Variables 
  • Feature Reduction 
  • Variable Clustering 
  • Linear Discriminant Analysis for Feature Reduction 
  1. Model Improvements 
  • Model Improvements 
  • Introduction 
  • Bias-Variance Trade-off 
  • Underfitting and Overfitting 
  • Defining a Sample Use Case 
  • Cross-Validation 
  • Holdout Approach/Validation 
  • K-Fold Cross-Validation 
  • Hold-One-Out Validation 
  • Hyperparameter Optimization 
  • Grid Search Optimization 
  • Random Search Optimization 
  • Bayesian Optimization 
  1. Model Deployment 
  • Model Deployment 
  • Introduction 
  • What is an API? 
  • Introduction to plumber 
  • A Brief History of the Pre-Docker Era 
  • Docker 
  • Amazon Web Services 
  • Introducing AWS SageMaker 
  • What is Amazon Lambda? 
  • What is Amazon API Gateway? 
  • Building Serverless ML Applications 
  • Deleting All Cloud Resources to Stop Billing 
  1. Capstone Project – Based on Research Papers 
  • Capstone Project – Based on Research Papers 
  • Introduction 
  • Exploring Research Work 
  • The mlr Package 
  • Problem Design from the Research Paper 
  • Features in Scene Dataset 
  • Implementing Multilabel Classifier Using the mlr and OpenML Packages 
  • Constructing a Learner 
  • Predictions 
View All Courses

    Course Inquiry

    Fill in the details below and we will get back to you as quickly as we can.

    Interested in any of these related courses?