K-Means for Cluster Analysis and Unsupervised Learning in R

K-Means for Cluster Analysis and Unsupervised Learning in R

What you’ll 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

How to Enroll K-Means for Cluster Analysis and Unsupervised Learning in R course?

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