Udemy - K-Means for Cluster Analysis and Unsupervised Learning in R
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Files
| 1. BONUS.mp4 | 5.9 MB | ||
| 1. BONUS.srt | 1.3 KB | ||
| 1. How to assess a Clustering Tendency of the dataset.mp4 | 42.3 MB | ||
| 1. How to assess a Clustering Tendency of the dataset.srt | 5.5 KB | ||
| 1. Introduction to Your Project based on a case study.mp4 | 58.1 MB | ||
| 1. Introduction to Your Project based on a case study.srt | 7.8 KB | ||
| 1. Introduction.mp4 | 16.2 MB | ||
| 1. Introduction.srt | 2.5 KB | ||
| 1. Lab Installing Packages and Package Management in R.mp4 | 24.1 MB | ||
| 1. Lab Installing Packages and Package Management in R.srt | 4.5 KB | ||
| 1. Overview of Machine Leraning in R.mp4 | 5.7 MB | ||
| 1. Overview of Machine Leraning in R.srt | 2.1 KB | ||
| 1. Starting with Fuzzy K-means in R.mp4 | 102.1 MB | ||
| 1. Starting with Fuzzy K-means in R.srt | 14.5 KB | ||
| 1. What is R and RStudio.mp4 | 12.2 MB | ||
| 1. What is R and RStudio.srt | 3.1 KB | ||
| 1.1 010_FuzzyKMenas.R | 1.4 KB | ||
| 1.1 013_ClusteringTendency.R | 1.2 KB | ||
| 1.1 R Crash Course I_udemy_script.R | 12.9 KB | ||
| 1.1 samsungData.rda | 27.3 MB | ||
| 2. Assessing the performance of unsupervised learning (clustering) algorithms.mp4 | 38.4 MB | ||
| 2. Assessing the performance of unsupervised learning (clustering) algorithms.srt | 7.2 KB | ||
| 2. Entropy Weighted K-Means in R.mp4 | 61.9 MB | ||
| 2. Entropy Weighted K-Means in R.srt | 8.9 KB | ||
| 2. How to install R and RStudio in 2020.mp4 | 38.7 MB | ||
| 2. How to install R and RStudio in 2020.srt | 6.2 KB | ||
| 2. Lab Variables in R and assigning Variables in R.mp4 | 7.6 MB | ||
| 2. Lab Variables in R and assigning Variables in R.srt | 1.7 KB | ||
| 2. Project Assignment.mp4 | 68.3 MB | ||
| 2. Project Assignment.srt | 9.5 KB | ||
| 2. Unsupervised Learning & Clustering theory.mp4 | 19 MB | ||
| 2. Unsupervised Learning & Clustering theory.srt | 7.2 KB | ||
| 2. What is Machine Leraning and it's main types.mp4 | 46.9 MB | ||
| 2. What is Machine Leraning and it's main types.srt | 11.2 KB | ||
| 2.1 011_WeigthedKMenas.R | 921 B | ||
| 2.1 014_ClusteringPerformance.R | 819 B | ||
| 3. How to compare the performance of different unsupervised clustering algoritms.mp4 | 27.2 MB | ||
| 3. How to compare the performance of different unsupervised clustering algoritms.srt | 5.6 KB | ||
| 3. K-Means Clustering Theory.mp4 | 17.5 MB | ||
| 3. K-Means Clustering Theory.srt | 5.6 KB | ||
| 3. Lab Get started with R in RStudio.mp4 | 47.7 MB | ||
| 3. Lab Get started with R in RStudio.srt | 9.5 KB | ||
| 3. Overview of data types and data structures in R.mp4 | 27.2 MB | ||
| 3. Overview of data types and data structures in R.srt | 8.2 KB | ||
| 3. Selecting the number of clusters for unsupervised Clustering methods (K-Means).mp4 | 59.3 MB | ||
| 3. Selecting the number of clusters for unsupervised Clustering methods (K-Means).srt | 9.2 KB | ||
| 3.1 009_KMenas_part3_cluster_assign.R | 2.3 KB | ||
| 3.1 015_ClusteringSelect.R | 409 B | ||
| 4. Example K-Means Clustering in R Lab.mp4 | 22.8 MB | ||
| 4. Example K-Means Clustering in R Lab.srt | 5.3 KB | ||
| 4. Lab data types and data structures in R.mp4 | 48.1 MB | ||
| 4. Lab data types and data structures in R.srt | 8.8 KB | ||
| 4.1 009_KMenas.R | 921 B | ||
| 5. K-means clustering Application to email marketing.mp4 | 110.6 MB | ||
| 5. K-means clustering Application to email marketing.srt | 14.5 KB | ||
| 5. Vectors' operations in R.mp4 | 36 MB | ||
| 5. Vectors' operations in R.srt | 7.2 KB | ||
| 5.1 009_KMenas_part2.R | 1.3 KB | ||
| 6. Data types and data structures Factors.mp4 | 9.3 MB | ||
| 6. Data types and data structures Factors.srt | 2.7 KB | ||
| 6. Heatmaps to visualize K-Means Results in R Examplery Lab.mp4 | 24.7 MB | ||
| 6. Heatmaps to visualize K-Means Results in R Examplery Lab.srt | 4.2 KB | ||
| 6.1 009_KMenas.R | 921 B | ||
| 7. Dataframes overview.mp4 | 16.7 MB | ||
| 7. Dataframes overview.srt | 4 KB | ||
| 8. Functions in R - overview.mp4 | 32.2 MB | ||
| 8. Functions in R - overview.srt | 4.7 KB | ||
| 9. Lab For Loops in R.mp4 | 24.8 MB | ||
| 9. Lab For Loops in R.srt | 4 KB | ||
| TutsNode.com.txt | 102 B | ||
| [TGx]Downloaded from torrentgalaxy.to .txt | 614 B | ||
| ▲ 71 total files | |||
Description
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 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 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.
JOIN MY COURSE NOW!
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
Requirements
Availabiliy computer and internet
R-programming skills is NOT a requirement, but would be a plus
Last Updated 11/2020
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