Unlocking the Secrets of Data: Unsupervised Learning with R

Unlocking the Secrets of Data: Unsupervised Learning with R, available at $54.99, has an average rating of 4.55, with 23 lectures, 2 quizzes, based on 23 reviews, and has 51 subscribers.

You will learn about Apply clustering algorithms to college scorecard data Apply association rule mining to a set of products that customers have subscribed to Apply dimensionality reduction techniques in preparation for clustering analyses Use the R programming language to accomplish unsupervised machine learning tasks This course is ideal for individuals who are Data analysts and data scientists with interest in expanding their toolbets to include a variety of unsupervised learning techniques. This course is great for anyone who is curious about efficient ways of accomplishing unsupervised learning techniques in R. or Any learner interested in learning how some unsupervised machine learning techniques can be applied to a variety of problem scenarios and projects It is particularly useful for Data analysts and data scientists with interest in expanding their toolbets to include a variety of unsupervised learning techniques. This course is great for anyone who is curious about efficient ways of accomplishing unsupervised learning techniques in R. or Any learner interested in learning how some unsupervised machine learning techniques can be applied to a variety of problem scenarios and projects.

Enroll now: Unlocking the Secrets of Data: Unsupervised Learning with R

Summary

Title: Unlocking the Secrets of Data: Unsupervised Learning with R

Price: $54.99

Average Rating: 4.55

Number of Lectures: 23

Number of Quizzes: 2

Number of Published Lectures: 23

Number of Published Quizzes: 2

Number of Curriculum Items: 27

Number of Published Curriculum Objects: 27

Original Price: $22.99

Quality Status: approved

Status: Live

What You Will Learn

• Apply clustering algorithms to college scorecard data
• Apply association rule mining to a set of products that customers have subscribed to
• Apply dimensionality reduction techniques in preparation for clustering analyses
• Use the R programming language to accomplish unsupervised machine learning tasks

Who Should Attend

• Data analysts and data scientists with interest in expanding their toolbets to include a variety of unsupervised learning techniques. This course is great for anyone who is curious about efficient ways of accomplishing unsupervised learning techniques in R.
• Any learner interested in learning how some unsupervised machine learning techniques can be applied to a variety of problem scenarios and projects

Target Audiences

• Data analysts and data scientists with interest in expanding their toolbets to include a variety of unsupervised learning techniques. This course is great for anyone who is curious about efficient ways of accomplishing unsupervised learning techniques in R.
• Any learner interested in learning how some unsupervised machine learning techniques can be applied to a variety of problem scenarios and projects