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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