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Udemy - K-Means For Cluster Analysis And Unsupervised Learning In R
TORRENT SUMMARY
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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
Requirements
Availabiliy computer and internet
R-programming skills is NOT a requirement, but would be a plus
Description
Learn why and where K-Means is a powerful tool
Clustering is a very important part of machine learning. Especially unsupervised machine learning is a rising topic in the whole field of artificial intelligence. If we want to learn about cluster analysis, there is no better method to start with, than the k-means algorithm.
Unlike other courses, it offers NOT ONLY the guided nstrations of the R-scripts but also covers theoretical background that will allow you to FULLY UNDERSTAND & APPLY UNSUPERVISED MACHINE LEARNING (K-means) in R.
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