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

Course Description:

Welcome to “Unlocking the Secrets of Data: Unsupervised Learning with R”, a comprehensive and engaging journey into the world of unsupervised machine learning using the powerful R programming language.

Who This Course Is For:

This course is meticulously designed for a wide range of learners – whether you are stepping into the realm of data science, seeking to enhance your programming skills in R, or a professional looking to delve into the specifics of unsupervised learning algorithms.

What You Will Learn:

Fundamentals of Unsupervised Learning: Grasp the core concepts and different approaches of unsupervised learning in data science.

R Programming Deep Dive: Whether you’re starting fresh or brushing up, you’ll gain a strong command of R, a language pivotal in data analysis and machine learning.

Key Algorithms and Techniques: Explore essential algorithms like hierarchical clustering, association rules, and Principal Component Analysis (PCA).

Real-world Data Projects: Apply your knowledge to real-world datasets, uncovering hidden patterns and gaining practical, hands-on experience.

Interactive Learning Experience: Engage with coding challenges, enhancing your learning experience.

Community and Support: Join a vibrant community of learners and experts. Participate in discussions, share insights, and get the support you need to excel.

Why Choose This Course:

Tailored Content: Content designed to cater to both beginners and those with prior knowledge, ensuring a comprehensive learning curve.

Practical and Theoretical Balance: A well-balanced blend of theoretical knowledge and practical application.

Video Lectures: Unique video based learning that demonstrates live coding sessions.

Flexible Learning: Learn at your own pace with access to all course materials and community support.

Embark on this journey to master unsupervised learning with R and transform the way you understand and leverage data. Whether it’s for career advancement, academic pursuits, or personal interest, “Unlocking the Secrets of Data: Unsupervised Learning with R” is your key to unlocking the potential of data science.

Enroll now and start your journey towards gaining expertise in unsupervised learning using R!

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Lecture 2: Instructor Welcome

Lecture 3: Prerequisites

Lecture 4: Why use R and R Studio for this?

Lecture 5: Getting Data Sets

Chapter 2: Unsupervised Learning: Clustering

Lecture 1: Introduction to Clustering

Lecture 2: Example of using College Scorecard Data – from Industry

Lecture 3: Getting and Loading the College Scorecard Data

Lecture 4: Scaling The Data – Required for Clustering Analyses

Lecture 5: Using Hierarchical Clustering in R

Lecture 6: Running a kMeans Clustering Analysis in R

Lecture 7: Cluster Validity

Chapter 3: Unsupervised Learning: Dimensionality Reduction

Lecture 1: Introduction to Dimensionality Reduction

Lecture 2: Feature Removal of Highly Correlated Features

Lecture 3: PCA in R – Part 1

Lecture 4: PCA in R – Part 2

Chapter 4: Unsupervised Learning: Association Rule Mining (aka Market Basket Analysis)

Lecture 1: Introduction to Frequent Itemset Mining and Association Rule Mining – Part 1

Lecture 2: Introduction to Frequent Itemset Mining and Association Rule Mining – Part 2

Lecture 3: Measuring Results of Association Rules

Lecture 4: Cleaning and Preparing Data for Frequent Itemset Mining and Association Rules

Lecture 5: Frequent Itemsets

Lecture 6: Association Rules

Lecture 7: Sorting Itemsets and Rules

Instructors

Rich Huebner
Passionate Educator and Data Science & Analytics Expert

Rating Distribution

1 stars: 1 votes

2 stars: 0 votes

3 stars: 0 votes

4 stars: 6 votes

5 stars: 16 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!