Introduction to Clustering using R

Introduction to Clustering using R, available at $44.99, has an average rating of 4.2, with 42 lectures, based on 15 reviews, and has 95 subscribers.

You will learn about Basics of clustering and R, various clustering techniques, Machine Learning This course is ideal for individuals who are Anyone that wants to make a career in machine learning, research and data analytics It is particularly useful for Anyone that wants to make a career in machine learning, research and data analytics.

Enroll now: Introduction to Clustering using R

Summary

Title: Introduction to Clustering using R

Price: $44.99

Average Rating: 4.2

Number of Lectures: 42

Number of Published Lectures: 42

Number of Curriculum Items: 42

Number of Published Curriculum Objects: 42

Original Price: $39.99

Quality Status: approved

Status: Live

What You Will Learn

Basics of clustering and R, various clustering techniques, Machine Learning

Who Should Attend

Anyone that wants to make a career in machine learning, research and data analytics

Target Audiences

Anyone that wants to make a career in machine learning, research and data analytics

This course would get you started with clustering, which is one of the most well known machine learning algorithm, Anyone looking to pursue a career in data science can use the clustering concepts and techniques taught in this course to gain the necessary skill for processing and clustering any form of data.  In addition, the course would familiarize you with R, which is becoming the default programming language for processing data among the global companies.

Course Curriculum

Chapter 1: Course Preview, Understanding the Basics of Clustering

Lecture 1: Preview, Machine Learning, clustering/classification, supervised/unsupervised

Lecture 2: clustering benefits, meaning of unlabeled data

Lecture 3: understand clustering using some examples

Lecture 4: going through the course content, target audience

Lecture 5: Different types of data – continuous/interval & binary

Lecture 6: Different types of data – ordinal & nominal data, Scaling the data

Lecture 7: create random dataset in R, some datasets used in the course

Chapter 2: Popular distance Measure – Euclidean & Hamming/ Kmeans Clustering in R

Lecture 1: Using small subset to understand euclidean distance measure

Lecture 2: Manually calculating euclidean distance

Lecture 3: Randomness in the clustering process

Lecture 4: Make two clusters using randomly chosen cluster centroids

Lecture 5: make a scatterplot in R using the identified clusters

Lecture 6: Introduction to K means clustering, k means function in R, Understanding the out

Lecture 7: imdb dataset, Output of k means function, Understanding R codes

Lecture 8: recap k means clustering process, Understand R codes

Lecture 9: Making clusters using automobile data, Understand R codes, make scatterplot in R

Lecture 10: Introduction to manhattan distance, use automobile dataset to calculate manhatta

Lecture 11: Formula manhattan distance, visualize the clusters in R

Chapter 3: Understand Partitioning Around Medoids, Hierarchical form of Clustering

Lecture 1: Understand Partitioning Around Medoids Clustering

Lecture 2: Introduction to Hierarchical Clustering

Lecture 3: Introduction to Single Linkage form of Hierarchical clustering

Lecture 4: Complete linkage form of hierarchical clustering

Lecture 5: Average linkage form of hierarchical clustering (first method)

Lecture 6: Average linkage form of hierarchical clustering (second method)

Lecture 7: hclust function in R, Represent clusters using ggplot function

Lecture 8: Ward method of Hierarchical clustering, Difference between ward.D and ward.D2

Chapter 4: Understand clustering process of binary data, Kmodes clustering

Lecture 1: Cluster Binary data, Simple Matching, Jaccard & Dice coefficient

Lecture 2: Convert single Nominal column to multiple Binary column

Lecture 3: Convert single Nominal column to multiple Binary column (part 2)

Lecture 4: Clustering process of Mixed data

Lecture 5: Introduction to Kmodes clustering

Lecture 6: Kmodes Clustering, Simple matching dissimilarity

Lecture 7: Kmodes clustering- Understanding the process

Chapter 5: Density-based clustering, cluster ordinal data, find replacement for empty cell

Lecture 1: Introduction to Density based clustering

Lecture 2: cluster ordinal data

Lecture 3: Replace Missing data to improve clustering outcome

Chapter 6: Determine ideal number of clusters, daisy function to cluster mixed data

Lecture 1: What would be the right number of clusters, Elbow method

Lecture 2: example elbow method, nbclust function in R

Lecture 3: Silhouette method, Using silhouette method in R, visualize identified clusters

Lecture 4: Example to understand Silhouette method

Lecture 5: Daisy function to cluster mixed data, Gower coefficient, Some Examples

Chapter 7: Goodbye

Lecture 1: Goodbye

Instructors

Rajat Raj Aggarwal
data science expert

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4 stars: 10 votes

5 stars: 3 votes

Frequently Asked Questions

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