Unlocking The Secrets Of Data: Unsupervised Learning With R

Clustering, Association Rule Mining, and Dimensionality Reduction Techniques

What you'll 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

Requirements

Some familiarity with R is needed. Learners should have R and R Studio installed already. I will make sure there is at least one lecture describing which packages you will need for this course.

Description

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!

Overview

Section 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

Section 2: Unsupervised Learning: Clustering

Lecture 6 Introduction to Clustering

Lecture 7 Example of using College Scorecard Data - from Industry

Lecture 8 Getting and Loading the College Scorecard Data

Lecture 9 Scaling The Data - Required for Clustering Analyses

Lecture 10 Using Hierarchical Clustering in R

Lecture 11 Running a kMeans Clustering Analysis in R

Lecture 12 Cluster Validity

Section 3: Unsupervised Learning: Dimensionality Reduction

Lecture 13 Introduction to Dimensionality Reduction

Lecture 14 Feature Removal of Highly Correlated Features

Lecture 15 PCA in R - Part 1

Lecture 16 PCA in R - Part 2

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

Lecture 17 Introduction to Frequent Itemset Mining and Association Rule Mining - Part 1

Lecture 18 Introduction to Frequent Itemset Mining and Association Rule Mining - Part 2

Lecture 19 Measuring Results of Association Rules

Lecture 20 Cleaning and Preparing Data for Frequent Itemset Mining and Association Rules

Lecture 21 Frequent Itemsets

Lecture 22 Association Rules

Lecture 23 Sorting Itemsets and Rules

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