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Course Skill Level:

Foundational to Intermediate

Course Duration:

2 day/s

  • Course Delivery Format:

    Live, instructor-led.

  • Course Category:

    AI / Machine Learning

  • Course Code:


Who should attend & recommended skills:

Those with Python experience and basic IT and Linux skills

Who should attend & recommended skills

  • This course is geared for Python experienced developers, analysts or others 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.
  • Skill-level: Foundation-level Applied Supervised Learning with R skills for Intermediate skilled team members. This is not a basic class.
  • IT skills: Basic to Intermediate (1-5 years’ experience)
  • Linux: Basic (1-2 years’ experience), including familiarity with command-line options such as ls, cd, cp, and su
  • Machine Learning and advanced math skills: Not required

About this course

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.

Skills acquired & topics covered

  • Working in a hands-on learning environment, led by our Applied Supervised Learning with R expert instructor, participants will learn about and explore:
  • Supervised learning algorithms by using real-world datasets
  • Fine-tuning optimal parameters with hyperparameter optimization
  • Selecting the best algorithm using the model evaluation framework
  • Developing analytical thinking to precisely identify a business problem
  • Wrangling data with dplyr, tidyr, and reshape2
  • Visualizing data with ggplot2
  • Validating your supervised machine learning model using k-fold
  • Optimizing hyperparameters with grid and random search, and Bayesian optimization
  • Deploying your model on Amazon Web Services (AWS) Lambda with plumber
  • Improving your models performance with feature selection and dimensionality reduction

Course breakdown / modules

  • 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

  • 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

  • Introduction
  • Summary of the Beijing PM2.5 Dataset
  • Regression and Classification Problems
  • Machine Learning Workflow
  • Regression
  • Exploratory Data Analysis (EDA)
  • Classification
  • Evaluation Metrics

  • 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

  • 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

  • Introduction
  • Feature Engineering
  • One-Hot Encoding
  • Log Transformation
  • Feature Selection
  • Highly Correlated Variables
  • Feature Reduction
  • Variable Clustering
  • Linear Discriminant Analysis for Feature Reduction

  • 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

  • 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

  • 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