Unlocking the Secrets of Data: Unsupervised Learning with R from Udemy

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.

What's inside

Learning objectives

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

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

Syllabus

Recognize and load core R libraries needed to conduct various unsupervised learning techniques in R

Introduction to the Course and Welcome!

Instructor Welcome and Background

In this short video, I talk about where you can get various data sets for exploration.

In this lecture, we provide a brief background of clustering and typical real-world applications.

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.

In this lecture, I go over the need for scaling or normalizing the data as part of a clustering analysis.

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.

In this lecture, I talk about how easy it is to run a kMeans clustering analysis after you have prepped and cleansed your data.

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.

In this lecture, I discuss the ideas behind dimensionality reduction.

This quiz is about dimensionality reduction.

In this lecture (parts 1 and 2), I introduce you to the ideas around association rule mining and frequent itemset mining. This is a powerful unsupervised learning technique that, I believe, doesn't get enough attention. Make sure you have this one in your toolbelt!

This is Part 2 of the Introduction to Frequent Itemset Mining and Association Rule Mining

In this lecture, I discuss some of the metrics/measures you can use to interpret association rules.

In this lecture, I show you how to get and display the frequent itemsets.

In this lecture, I show you how to get the association rules, display them, and a little bit of interpretation.

So, you have your itemsets and rules results. Next, sort them by support or lift to examine some of the more "interesting" rules. You will have to experiment a bit.

This will test your knowledge of FIM and AR.

Traffic lights

Read about what's good

what should give you pause

and possible dealbreakers

Provides a comprehensive exploration of unsupervised learning with R, which is standard in industry

Builds a strong foundation in clustering and association rule mining, which are core skills for data scientists

Taught by Rich Huebner, who is recognized for their work in data science and R programming

Offers practical and theoretical balance, ensuring learners grasp the concepts and applications of unsupervised learning

Provides hands-on experience through real-world data projects, enhancing learners' skills

Requires learners to have prior knowledge of R programming, which may be a barrier for some

Reviews summary

Practical unsupervised learning with r

According to learners, "Unlocking the Secrets of Data: Unsupervised Learning with R" provides an incredibly clear and practical introduction to unsupervised machine learning techniques using R. Students particularly praise the hands-on projects that utilize real-world data applications, such as the College Scorecard example, finding them highly effective for solidifying understanding and building confidence. The instructor's engaging delivery and the beneficial live coding sessions are frequently highlighted. While many appreciate the balanced theoretical and practical approach, some feedback suggests the pacing can be inconsistent and that it might be too simplistic for advanced users seeking deep theoretical dives, yet not deep enough for absolute R beginners lacking foundational syntax knowledge.

Best for those with some R knowledge new to unsupervised learning.

"It's a decent start for absolute beginners [in unsupervised learning], but not challenging enough for intermediate learners."

"I felt some explanations were a bit too simplistic for someone with a basic understanding of R and data science."

"I struggled with the R programming deep dive as it wasn't as deep as expected for someone truly new to R. I had to look up a lot of basic R syntax externally."

Video lectures with live coding are highly beneficial.

"The video lectures with live coding were very helpful."

"The live coding sessions are invaluable. I learned so much about PCA and how to properly scale data for clustering."

"I found the R code provided clean and easy to follow, making the hands-on aspects very accessible and beneficial."

Instructor's ability to explain complex topics clearly.

"The instructor's explanations were incredibly clear, especially on PCA and hierarchical clustering."

"The instructor clearly knows their stuff and presents it in an engaging way. The concepts are broken down into digestible chunks."

"The video quality and instructor's clarity were consistently high."

Focuses on real-world data and hands-on projects.

"The hands-on projects using real-world data, like the College Scorecard example, really helped solidify my understanding. I appreciated the practical application focus."

"The most practical unsupervised learning course I've taken. The live coding sessions are invaluable. I learned so much about PCA and how to properly scale data for clustering."

"I've already started applying these skills at work after finishing this course, which clearly demonstrates its immediate practical value."

Some parts need more detailed coding and theoretical depth.

"Some parts felt a bit rushed, especially when going through the code. I had to pause and re-watch some videos to fully grasp the steps."

"I wished for more depth; if you're looking for deep dives into the mathematical underpinnings or advanced tuning techniques, this might not be it."

"Sometimes the code demonstrations lack detailed commentary on *why* certain functions are chosen or parameters set, which made it harder to follow."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Unlocking the Secrets of Data: Unsupervised Learning with R with these activities:

Reading: Pattern Recognition and Machine Learning

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Comprehend theoretical concepts behind unsupervised learning by delving into a foundational text in the field.

View Pattern Recognition and Machine Learning... on Amazon

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Read relevant chapters on unsupervised learning and machine learning principles.
Summarize key ideas and concepts encountered in the book.

Review R Programming Basics

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Brush up on your understanding of R programming prior to taking this course to improve your confidence and fluency with the language.

Browse courses on R Programming

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Review the R syntax and data types.
Practice writing basic R functions.

Attend Data Science Meetup

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Expand your network and gain insights from like-minded individuals by attending a data science meetup focused on unsupervised learning.

Browse courses on Networking

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Search for data science meetups in your area that cover unsupervised learning topics.
Register for the meetup and attend.

Four other activities

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Show all seven activities

College Scorecard Data Exploration

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Test your skills in unsupervised learning by loading and working with college scorecard data to identify clusters.

Browse courses on Clustering Algorithms

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Load and clean the college scorecard data.
Perform a hierarchical clustering analysis on the data.
Visualize the results of your clustering analysis.

Association Rule Mining Tutorial

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Deepen your understanding of association rule mining by following a guided tutorial that demonstrates the techniques in action.

Browse courses on Association Rule Mining

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Find a tutorial on association rule mining using R.
Follow the tutorial and implement the techniques on a dataset.

Dimensionality Reduction Exercises

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Solidify your understanding of dimensionality reduction by practicing exercises that focus on these techniques.

Browse courses on Dimensionality Reduction

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Find online exercises or practice problems on dimensionality reduction.
Solve the exercises and check your answers against provided solutions.

Unsupervised Machine Learning Project

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Gain practical experience by applying unsupervised machine learning techniques to a dataset of your choice and presenting your findings.

Browse courses on Unsupervised Machine Learning

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Choose a dataset that aligns with your interests.
Develop a plan for applying unsupervised machine learning techniques to the dataset.
Implement your plan and document your findings.
Prepare a presentation to showcase your project.

Career center

Learners who complete Unlocking the Secrets of Data: Unsupervised Learning with R will develop knowledge and skills that may be useful to these careers:

Data Analyst

Data Analysts examine and interpret large volumes of data to help organizations understand their past performance and make informed decisions for the future. Unsupervised learning, as taught in this course, is a crucial skill for Data Analysts, as it allows them to identify patterns and relationships in data that may not be immediately apparent. By understanding the concepts and techniques of unsupervised learning, Data Analysts can extract valuable insights from data, helping organizations improve their decision-making processes.

See salaries and explore the career path for Data Analyst

Machine Learning Engineer

Machine Learning Engineers design, develop, and implement machine learning models to solve business problems. This course provides a strong foundation in unsupervised learning, which is a type of machine learning that can be used to discover patterns and relationships in data without the need for labeled training data. This knowledge is essential for Machine Learning Engineers, as it enables them to build more effective and accurate machine learning models.

See salaries and explore the career path for Machine Learning Engineer

Data Scientist

Data Scientists use their knowledge of statistics, mathematics, and computer science to extract insights from data. Unsupervised learning is a valuable tool for Data Scientists, as it allows them to identify patterns and relationships in data that may not be immediately apparent. This course provides a comprehensive overview of unsupervised learning techniques, giving Data Scientists the skills they need to succeed in their roles.

See salaries and explore the career path for Data Scientist

Business Analyst

Business Analysts use data to help organizations make better decisions. Unsupervised learning is a valuable tool for Business Analysts, as it allows them to identify patterns and trends in data that can be used to improve business processes and make more informed decisions. This course provides a practical introduction to unsupervised learning, giving Business Analysts the skills they need to succeed in their roles.

See salaries and explore the career path for Business Analyst

Statistician

Statisticians collect, analyze, interpret, and present data. Unsupervised learning is a valuable tool for Statisticians, as it allows them to identify patterns and relationships in data that may not be immediately apparent. This course provides a comprehensive overview of unsupervised learning techniques, giving Statisticians the skills they need to succeed in their roles.

See salaries and explore the career path for Statistician

Market Researcher

Market Researchers use data to understand consumer behavior and market trends. Unsupervised learning is a valuable tool for Market Researchers, as it allows them to identify patterns and relationships in data that may not be immediately apparent. This course provides a practical introduction to unsupervised learning, giving Market Researchers the skills they need to succeed in their roles.

See salaries and explore the career path for Market Researcher

Data Engineer

Data Engineers design, build, and maintain data infrastructure. Unsupervised learning is a valuable tool for Data Engineers, as it allows them to identify patterns and relationships in data that can be used to improve data quality and performance. This course provides a comprehensive overview of unsupervised learning techniques, giving Data Engineers the skills they need to succeed in their roles.

See salaries and explore the career path for Data Engineer

Quantitative Analyst

Quantitative Analysts use mathematical and statistical models to analyze financial data. Unsupervised learning is a valuable tool for Quantitative Analysts, as it allows them to identify patterns and relationships in data that may not be immediately apparent. This course provides a practical introduction to unsupervised learning, giving Quantitative Analysts the skills they need to succeed in their roles.

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Software Engineer

Software Engineers design, develop, and maintain software applications. Unsupervised learning is a valuable tool for Software Engineers, as it allows them to identify patterns and relationships in data that can be used to improve software quality and performance. This course provides a comprehensive overview of unsupervised learning techniques, giving Software Engineers the skills they need to succeed in their roles.

See salaries and explore the career path for Software Engineer

Product Manager

Product Managers are responsible for the development and launch of new products. Unsupervised learning is a valuable tool for Product Managers, as it allows them to identify patterns and trends in data that can be used to improve product design and marketing. This course provides a practical introduction to unsupervised learning, giving Product Managers the skills they need to succeed in their roles.

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Consultant

Consultants provide advice and guidance to organizations on a variety of business issues. Unsupervised learning is a valuable tool for Consultants, as it allows them to identify patterns and trends in data that can be used to improve business processes and make more informed decisions. This course provides a practical introduction to unsupervised learning, giving Consultants the skills they need to succeed in their roles.

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Financial Analyst

Financial Analysts use data to analyze financial performance and make investment recommendations. Unsupervised learning is a valuable tool for Financial Analysts, as it allows them to identify patterns and relationships in data that may not be immediately apparent. This course provides a practical introduction to unsupervised learning, giving Financial Analysts the skills they need to succeed in their roles.

See salaries and explore the career path for Financial Analyst

Marketing Manager

Marketing Managers develop and execute marketing campaigns to promote products and services. Unsupervised learning is a valuable tool for Marketing Managers, as it allows them to identify patterns and trends in data that can be used to improve marketing campaigns and reach target audiences more effectively. This course provides a practical introduction to unsupervised learning, giving Marketing Managers the skills they need to succeed in their roles.

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Operations Manager

Operations Managers are responsible for the day-to-day operations of an organization. Unsupervised learning is a valuable tool for Operations Managers, as it allows them to identify patterns and trends in data that can be used to improve operational efficiency and productivity. This course provides a practical introduction to unsupervised learning, giving Operations Managers the skills they need to succeed in their roles.

See salaries and explore the career path for Operations Manager

Project Manager

Project Managers plan and execute projects to achieve specific goals. Unsupervised learning is a valuable tool for Project Managers, as it allows them to identify patterns and trends in data that can be used to improve project planning and execution. This course provides a practical introduction to unsupervised learning, giving Project Managers the skills they need to succeed in their roles.

See salaries and explore the career path for Project Manager