Machine Learning Essentials with R

Machine Learning Essentials with R is an essentials-level, three-day hands-on course that teaches students core skills and concepts in modern ML practices. This course is geared for attendees new to machine learning who need introductory level coverage of these topics, rather than a deep dive of the math and statistics behind Machine Learning. Students will learn basic algorithms from scratch. For each machine learning concept, students will first learn about and discuss the foundations, its applicability and limitations, and then explore the implementation and use, reviewing and working with specific use cases.

Retail Price: $2,195.00

Next Date: Request Date

Course Days: 3


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Course Objectives

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

  • Popular machine learning algorithms, their applicability and limitations
  • Practical application of these methods in a machine learning environment
  • Practical use cases and limitations of algorithms

 

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

  • Machine Learning (ML) Overview
  • Machine Learning Environment
  • Machine Learning Concepts
  • Feature Engineering (FE)
  • Linear regression
  • Logistic Regression
  • Classification : SVM (Supervised Vector Machines)
  • Classification : Decision Trees & Random Forests
  • Classification : Naive Bayes
  • Clustering (K-Means)
  • Principal Component Analysis (PCA)
  • Recommendation (Collaborative filtering)
  • Time Permitting: Capstone Project

 

Course Prerequisites

This in an introductory-level course is geared for experienced developers or others (with prior Python, R or Scala experience, depending on the course flavor) intending to start using learning about and working with basic machine learning algorithms and concepts.

 

Pre-Requisites:  Students should have

-Basic R programming skills.  Attendees without R programming background may view labs as follow along exercises or team with others to complete them. (NOTE: This course is also offered in Python or Scala – please inquire for details)

-Good foundational mathematics skills in Linear Algebra and Probability, to start learning about and using basic machine learning algorithms and concepts

-Basic Linux skills, including familiarity with command-line options such as ls, cd, cp, and su

 

Take Before: Attendees should have incoming experience aligned with the topics in one of the courses below, or should have attended one of these as a pre-requisite:

-TTDS6680     R Programming JumpStart – 3 days

-TTDS6681     R Essentials for Data Science – 2 days

-TTDS6682     R Programming in Data Science – 3 days


Course Agenda

Please note that this list of topics is based on our standard course offering, evolved from typical industry uses and trends. We’ll work with you to tune this course and level of coverage to target the skills you need most.

 

  1. Machine Learning (ML) Overview
  • Machine Learning landscape
  • Machine Learning applications
  • Understanding ML algorithms & models (supervised and unsupervised)

 

  1. Machine Learning Environment
  • Introduction to Jupyter notebooks / R-Studio
  • Exercise: Getting familiar with ML environment

 

  1. Machine Learning Concepts
  • Statistics Primer
  • Covariance, Correlation, Covariance Matrix
  • Errors, Residuals
  • Overfitting / Underfitting
  • Cross validation, bootstrapping
  • Confusion Matrix
  • ROC curve, Area Under Curve (AUC)
  • Exercise: Working with Basic Statistics

 

  1. Feature Engineering (FE)
  • Preparing data for ML
  • Extracting features, enhancing data
  • Data cleanup
  • Visualizing Data
  • Exercise: data cleanup
  • Exercise: visualizing data
  1. Linear regression
  • Simple Linear Regression
  • Multiple Linear Regression
  • Running LR
  • Evaluating LR model performance
  • Exercise / Use case: House price estimates

 

  1. Logistic Regression
  • Understanding Logistic Regression
  • Calculating Logistic Regression
  • Evaluating model performance
  • Use case: credit card application, college admissions

 

  1. Classification : SVM (Supervised Vector Machines)
  • SVM concepts and theory
  • SVM with kernel
  • Use case: Customer churn data

 

  1. Classification : Decision Trees & Random Forests
  • Theory behind trees
  • Classification and Regression Trees (CART)
  • Random Forest concepts
  • Exercise / Use case: predicting loan defaults, estimating election contributions

 

  1. Classification : Naive Bayes
  • Theory behind Naive Bayes
  • Running NB algorithm
  • Evaluating NB model
  • Exercise / Use case: spam filtering

 

  1. Clustering (K-Means)
  • Theory behind K-Means
  • Running K-Means algorithm
  • Estimating the performance
  • Exercise / Use case: grouping cars data, grouping shopping data

 

  1. Principal Component Analysis (PCA)
  • Understanding PCA concepts
  • PCA applications
  • Running a PCA algorithm
  • Evaluating results
  • Exercise / Use case: analyzing retail shopping data

 

  1. Recommendation (Collaborative filtering)
  • Recommender systems overview
  • Collaborative Filtering concepts
  • Use case: movie recommendations, music recommendations

 

Time Permitting: Capstone Project

  • Hands-on guided workshop utilizing skills learned throughout the course


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