Applied Unsupervised Learning with R

Starting with the basics, Applied Unsupervised Learning with R explains clustering methods, distribution analysis, data encoders, and all features of R that enable you to understand your data better and get answers to all your business questions.

Starting with the basics, Applied Unsupervised Learning with R explains clustering methods, distribution analysis, data encoders, and features of R that enable you to understand your data better and get answers to your most pressing business questions.


This course begins with the most important and commonly used method for unsupervised learning - clustering - and explains the three main clustering algorithms - k-means, divisive, and agglomerative. Following this, you'll study market basket analysis, kernel density estimation, principal component analysis, and anomaly detection. You'll be introduced to these methods using code written in R, with further instructions on how to work with, edit, and improve R code. To help you gain a practical understanding, the course also features useful tips on applying these methods to real business problems, including market segmentation and fraud detection. By working through interesting activities, you'll explore data encoders and latent variable models.


By the end of this course, you will have a better understanding of different anomaly detection methods, such as outlier detection, Mahalanobis distances, and contextual and collective anomaly detection.


035452
2 days
This course is beneficial for individuals having prior programming knowledge R. Basic knowledge of mathematical concepts, including exponents, square roots, means, and medians is expected.

After completing this course, you will be able to:

  • Implement clustering methods such as k-means, agglomerative, and divisive
  • Write code in R to analyze market segmentation and consumer behavior
  • Estimate distribution and probabilities of different outcomes
  • Implement dimension reduction using principal component analysis
  • Apply anomaly detection methods to identify fraud
  • Design algorithms with R and learn how to edit or improve code
Applied Unsupervised Learning with R is designed for business professionals who want to learn about methods to understand their data better, and developers who have an interest in unsupervised learning. Although the course is for beginners, it will be beneficial to have some basic, beginner-level familiarity with R. This includes an understanding of how to open the R console, how to read data, and how to create a loop. To easily understand the concepts of this course, you should also know basic mathematical concepts, including exponents, square roots, means, and medians.
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  • OS: Windows 7 SP1 64-bit, Windows 8.1 64-bit or Windows 10 64-bit, Linux (Ubuntu, Debian, Red Hat, or Suse), or the latest version of OS X
  • R (3.0.0 or more recent, available for free at https://cran.r-project.org/)

For the optimal student experience, we recommend the following hardware configuration:

  • Processor: Intel Core i5 or equivalent
  • Memory: 4 GB RAM
  • Storage: 5 GB available space
  • An internet connection
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Lesson 1: Introduction to Clustering Methods

Introduction

Introduction to Clustering

Introduction to the Iris Dataset

Introduction to k-means Clustering

Introduction to k-means Clustering with Built-In Functions

Introduction to Market Segmentation

Introduction to k-medoids Clustering

Lesson 2: Advanced Clustering Methods

Introduction

Introduction to k-modes Clustering

Introduction to Density-Based Clustering (DBSCAN)

Lesson 3: Probability Distributions

Introduction

Basic Terminology of Probability Distributions

Introduction to Kernel Density Estimation

Introduction to the Kolmogorov-Smirnov Test

Lesson 4: Dimension Reduction

Introduction

Market Basket Analysis

Lesson 5: Data Comparison Methods

Introduction

Analytic Signatures

Latent Variable Models – Factor Analysis

Lesson 6: Anomaly Detection

Introduction

Univariate Outlier Detection

Kernel Density

$144.00 USD

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