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
- Availabiliy computer and internet
- R-programming skills is NOT a requirement, but would be a plus
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 demonstrations of the R-scripts but also covers theoretical background that will allow you to FULLY UNDERSTAND & APPLY UNSUPERVISED MACHINE LEARNING (K-means) in R.
Get a good intuition of the algorithm
The K-Means algorithm is explained in detail. We will first cover the principle mechanics without any mathematical formulas, just by visually observing data points and clustering behavior. After that, the mathematical background of the method is explained in detail.
Learn how to implement the algorithm in R
First, we will learn how to implement K-Means from scratch. This is important to get a really good grip on the functioning of the algorithm.
You will of course also learn how to implement the algorithm really quickly by using only one line of code as well as we will learn different types of K-Means algorithms and how to visualize the results of K-means.
The examples will be based on real data that you could get a real feeling of the data science tasks.
Learn where you should pay attention
K-Means is a powerful tool but it definitely has its drawbacks! You will learn where you have to be careful and when you should use the algorithm, and also when it is a bad idea to use the algorithm. We will learn how to perform the model’s evaluation for K-Means in R.
NO PRIOR R OR STATISTICS/MACHINE LEARNING / R KNOWLEDGE REQUIRED
You’ll start by absorbing the most valuable R Data Science basics and techniques. I use easy-to-understand, hands-on methods to simplify and address even the most difficult concepts in R.
This course is different from other training resources. Each lecture seeks to enhance your data science and clustering skills (K-means, weighted-K means, Heat mapping, etc) in a demonstrable and easy-to-follow manner and provide you with practically implementable solutions. You’ll be able to start analyzing different streams of data for your projects and gain appreciation from your future employers with your improved machine learning skills and knowledge of the cutting edge data science methods.
The course is ideal for professionals who need to use cluster analysis, unsupervised machine learning, and R in their field.
One important part of the course is the practical exercises. You will be given some precise instructions and datasets to run Machine Learning algorithms using the R and R tools.
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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