K-Means for Cluster Analysis and Unsupervised Learning in R
The powerful K-Means Clustering Algorithm for Cluster Analysis and Unsupervised Machine 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
Requirements
-
Availabiliy computer and internet
-
R-programming skills is NOT a requirement, but would be a plus
Who this course is for:
- 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
