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:
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Fundamentals of Unsupervised Learning: Grasp the core concepts and different approaches of unsupervised learning in data science.
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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.
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Key Algorithms and Techniques: Explore essential algorithms like hierarchical clustering, association rules, and Principal Component Analysis (PCA).
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Real-world Data Projects: Apply your knowledge to real-world datasets, uncovering hidden patterns and gaining practical, hands-on experience.
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Interactive Learning Experience: Engage with coding challenges, enhancing your learning experience.
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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:
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Tailored Content: Content designed to cater to both beginners and those with prior knowledge, ensuring a comprehensive learning curve.
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Practical and Theoretical Balance: A well-balanced blend of theoretical knowledge and practical application.
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Video Lectures: Unique video based learning that demonstrates live coding sessions.
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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!
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6
Introduction to ClusteringIn this lecture, we provide a brief background of clustering and typical real-world applications.
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7
Example of using College Scorecard Data - from Industry
This video lecture discusses a real-world, specific example of how I used the College Scorecard data to create clusters. The customer success team absolutely loves the visualization and uses it weekly to develop success strategies for the customers the serve.
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8
Getting and Loading the College Scorecard Data
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9
Scaling The Data - Required for Clustering Analyses
In this lecture, I go over the need for scaling or normalizing the data as part of a clustering analysis.
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10
Using Hierarchical Clustering in R
This lecture discusses how hierarchical clustering (also called agglomerative hierarchical clustering) can be used to place institutions into clusters. This is a great analysis to understand how institutions differ or are similar, especially for students examining which college or university to attend.
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11
Running a kMeans Clustering Analysis in R
In this lecture, I talk about how easy it is to run a kMeans clustering analysis after you have prepped and cleansed your data.
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12
Cluster Validity
In this lecture, I review one method for determining the optimal number of clusters you can use for your clustering analysis. YMMV and keep in mind results are suggestions, not hard-and-fast rules.
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13
Creating Your Own Clustering Analysis
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14
Introduction to Dimensionality Reduction
In this lecture, I discuss the ideas behind dimensionality reduction.
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15
Feature Removal of Highly Correlated Features
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16
PCA in R - Part 1
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17
PCA in R - Part 2
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18
Dimensionality Quiz
This quiz is about dimensionality reduction.
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19
Apply PCA to a New Dataset
Lessons:
