Udemy - K-Means for Cluster Analysis and Unsupervised Learning in R

[ DevCourseWeb.com ] K-Means for Cluster Analysis and Unsupervised Learning in R



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Genre: eLearning | MP4 | Video: h264, 1280x720 | Audio: aac, 44100 Hz

Language: English | VTT | Size: 1.05 GB | Duration: 3h 2m

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



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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