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K-Means for Cluster Analysis and Unsupervised Learning in R

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K-Means for Cluster Analysis and Unsupervised Learning in R, available at $59.99, has an average rating of 4.3, with 32 lectures, based on 49 reviews, and has 5119 subscribers.

You will learn about Understand unsupervised learning and clustering using R-programming language It covers both theoretical background of K-means clustering analysis as well as practical examples in R and R-Studio Fully understand the basics of Machine Learning, Cluster Analysis & Unsupervised Machine Learning How the K-Means algorithm is defined mathematically and how it is derived. How to implement K-Means very fast with R coding: examples of real data will be provided How the K-Means algorithm works in general. Get an intuitive explanation with graphics that are easy to understand Different types of K-meas; Fuzzy K-means, Weighted K-means and visualization of K-Means results in R Evaluate Model Performance & Learn The Best Practices For Evaluating Machine Learning Model Accuracy Implementing the K-Means algorithm in R from scratch. Get a really profound understanding of the working principle Learn R-programming from scratch: R crash course is included that you could start R-programming for machine learning This course is ideal for individuals who are The course is ideal for professionals who need to use cluster analysis, unsupervised machine learning and R in their field. or Everyone who would like to learn Data Science Applications In The R & R Studio Environment or Everyone who would like to learn theory and implementation of Unsupervised Learning On Real-World Data It is particularly useful for The course is ideal for professionals who need to use cluster analysis, unsupervised machine learning and R in their field. or Everyone who would like to learn Data Science Applications In The R & R Studio Environment or Everyone who would like to learn theory and implementation of Unsupervised Learning On Real-World Data.

Enroll now: K-Means for Cluster Analysis and Unsupervised Learning in R

Summary

Title: K-Means for Cluster Analysis and Unsupervised Learning in R

Price: $59.99

Average Rating: 4.3

Number of Lectures: 32

Number of Published Lectures: 32

Number of Curriculum Items: 32

Number of Published Curriculum Objects: 32

Original Price: $22.99

Quality Status: approved

Status: Live

What You Will Learn

  • Understand unsupervised learning and clustering using R-programming language
  • It covers both theoretical background of K-means clustering analysis as well as practical examples in R and R-Studio
  • Fully understand the basics of Machine Learning, Cluster Analysis & Unsupervised Machine Learning
  • How the K-Means algorithm is defined mathematically and how it is derived.
  • How to implement K-Means very fast with R coding: examples of real data will be provided
  • How the K-Means algorithm works in general. Get an intuitive explanation with graphics that are easy to understand
  • Different types of K-meas; Fuzzy K-means, Weighted K-means and visualization of K-Means results in R
  • Evaluate Model Performance & Learn The Best Practices For Evaluating Machine Learning Model Accuracy
  • Implementing the K-Means algorithm in R from scratch. Get a really profound understanding of the working principle
  • Learn R-programming from scratch: R crash course is included that you could start R-programming for machine learning

Who Should Attend

  • The course is ideal for professionals who need to use cluster analysis, unsupervised machine learning and R in their field.
  • Everyone who would like to learn Data Science Applications In The R & R Studio Environment
  • Everyone who would like to learn theory and implementation of Unsupervised Learning On Real-World Data

Target Audiences

  • The course is ideal for professionals who need to use cluster analysis, unsupervised machine learning and R in their field.
  • Everyone who would like to learn Data Science Applications In The R & R Studio Environment
  • Everyone who would like to learn theory and implementation of Unsupervised Learning On Real-World Data

Mastering K-Means Clustering in R: Theory and Practice

K-Means clustering is a fundamental technique in the field of machine learning, especially in unsupervised machine learning. If you want to delve into cluster analysis, there’s no better place to start than with the K-means algorithm.

Course Highlights:

Unlike other courses, this comprehensive program not only provides guided demonstrations of R-scripts but also delves into the theoretical background, enabling you to fully comprehend and apply unsupervised machine learning (K-means) in R.

Gain Intuition:

You will develop a deep understanding of the K-Means algorithm. We will begin by explaining its core mechanics without resorting to complex mathematical formulas, relying instead on visual observations of data points and clustering behavior. Afterward, we will delve into the mathematical foundations of the algorithm.

Hands-On Implementation:

Learn how to implement K-Means from scratch. This is essential for gaining a strong grasp of how the algorithm functions. Additionally, you’ll discover how to quickly implement the algorithm with just a single line of code. We’ll also explore different variations of K-Means algorithms and how to visualize their results using real-world data.

Understand the Caveats:

While K-Means is a powerful tool, it has its limitations. You’ll discover when and where to use the algorithm effectively, as well as situations where it may not be suitable. We’ll cover methods for evaluating K-Means models in R.

No Prior Knowledge Required:

This course is designed for beginners with no prior experience in R or statistics/machine learning. You will start by mastering the fundamentals of R Data Science, and the course progresses with easy-to-follow instructions and hands-on exercises.

Practical and Applicable:

This course sets itself apart by focusing on practical applications. Each lecture is geared toward enhancing your data science and clustering skills (including K-means, weighted-K means, heat mapping, etc.) and offers solutions that can be readily implemented. By the end, you’ll be prepared to analyze various datasets for your projects and impress your future employers with your advanced machine learning skills and knowledge of cutting-edge data science methods.

Ideal for Professionals:

Professionals who require knowledge of cluster analysis, unsupervised machine learning, and R in their fields will find this course immensely valuable.

Hands-On Practice:

The course includes practical exercises that provide precise instructions and datasets for running machine learning algorithms using R and R tools.

Join the Course Today!

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Lecture 2: What is Machine Leraning and it's main types?

Chapter 2: Software used in this course

Lecture 1: Introduction

Lecture 2: What is R and RStudio?

Lecture 3: How to install R and RStudio in 2020

Lecture 4: Lab: Get started with R in RStudio

Chapter 3: R Crash Course – get started with R-programming in R-Studio

Lecture 1: Introduction

Lecture 2: Lab: Installing Packages and Package Management in R

Lecture 3: Lab: Variables in R and assigning Variables in R

Lecture 4: Overview of data types and data structures in R

Lecture 5: Lab: data types and data structures in R

Lecture 6: Vectors' operations in R

Lecture 7: Data types and data structures: Factors

Lecture 8: Dataframes: overview

Lecture 9: Functions in R – overview

Lecture 10: Lab: For Loops in R

Lecture 11: Read Data into R

Chapter 4: Unsupervised learning: K-Means in R: Theory & Practise

Lecture 1: Overview of Machine Leraning in R

Lecture 2: Unsupervised Learning & Clustering: theory

Lecture 3: K-Means Clustering: Theory

Lecture 4: Example K-Means Clustering in R: Lab

Lecture 5: K-means clustering: Application to email marketing

Lecture 6: Heatmaps to visualize K-Means Results in R: Examplery Lab

Chapter 5: Advanced K-Means Clustering Analysis

Lecture 1: Starting with Fuzzy K-means in R

Lecture 2: Entropy Weighted K-Means in R

Lecture 3: Selecting the number of clusters for unsupervised Clustering methods (K-Means)

Chapter 6: Performance Evaluation of Unsupervised Learning CLustering Algorithms in R

Lecture 1: How to assess a Clustering Tendency of the dataset

Lecture 2: Assessing the performance of unsupervised learning (clustering) algorithms

Lecture 3: How to compare the performance of different unsupervised clustering algoritms?

Chapter 7: Your Independent Project in K-Means CLuster Analysis

Lecture 1: Introduction to Your Project based on a case study

Lecture 2: Project Assignment

Chapter 8: BONUS

Lecture 1: BONUS

Instructors

Rating Distribution

  • 1 stars: 3 votes
  • 2 stars: 1 votes
  • 3 stars: 4 votes
  • 4 stars: 7 votes
  • 5 stars: 34 votes

Frequently Asked Questions


How long do I have access to the course materials?


You can view and review the lecture materials indefinitely, like an on-demand channel.


Can I take my courses with me wherever I go?


Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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