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    • Applied Unsupervised Learning with R

    Enroll Now
    Duration
    2 days
    Delivery
    (Online and onsite)
    Price
    Price Upon Request
    Starting with the basics, Applied Unsupervised Learning with R explains clustering methods, distribution analysis, data encoders, and all features of R that enable you to understand your data better and get answers to all your business questions. This course begins with the most important and commonly used method for unsupervised learning - clustering - and explains the three main clustering algorithms - k-means, divisive, and agglomerative. Following this, you'll study market basket analysis, kernel density estimation, principal component analysis, and anomaly detection. You'll be introduced to these methods using code written in R, with further instructions on how to work with, edit, and improve R code. To help you gain a practical understanding, the course also features useful tips on applying these methods to real business problems, including market segmentation and fraud detection. By working through interesting activities, you'll explore data encoders and latent variable models. By the end of this course, you will have a better understanding of different anomaly detection methods, such as outlier detection, Mahalanobis distances, and contextual and collective anomaly detection.  

    After completing this course, you will be able to:

    • Implement clustering methods such as agglomerative, and divisive
    • Write code in R to analyze market segmentation and consumer behaviour
    • Estimate distribution and probabilities of different outcomes
    • Implement dimension reduction using principal component analysis
    • Apply anomaly detection methods to identify fraud
    • Design algorithms with R and learn how to edit or improve code

    Applied Unsupervised Learning with R is designed for business professionals who want to learn about methods to understand their data better, and developers who have an interest in unsupervised learning.

    Although the course is for beginners, it will be beneficial to have some basic, beginner-level familiarity with R. This includes an understanding of how to open the R console, how to read data, and how to create a loop. To easily understand the concepts of this course, you should also know basic mathematical concepts, including exponents, square roots, means, and medians.

     

    Hardware:

    For the optimal student experience, we recommend the following hardware configuration:

    • Processor: Intel Core i5 or equivalent
    • Memory: 4 GB RAM
    • Storage: 5 GB available space
    • An internet connection

     

    Software:

    • OS: Windows 7 SP1 64-bit, Windows 8.1 64-bit or Windows 10 64-bit, Linux (Ubuntu, Debian, Red Hat, or Suse), or the latest version of OS X
    • R (3.0.0 or more recent, available for free at https://cran.r-project.org/)
    Overview
    Starting with the basics, Applied Unsupervised Learning with R explains clustering methods, distribution analysis, data encoders, and all features of R that enable you to understand your data better and get answers to all your business questions. This course begins with the most important and commonly used method for unsupervised learning - clustering - and explains the three main clustering algorithms - k-means, divisive, and agglomerative. Following this, you'll study market basket analysis, kernel density estimation, principal component analysis, and anomaly detection. You'll be introduced to these methods using code written in R, with further instructions on how to work with, edit, and improve R code. To help you gain a practical understanding, the course also features useful tips on applying these methods to real business problems, including market segmentation and fraud detection. By working through interesting activities, you'll explore data encoders and latent variable models. By the end of this course, you will have a better understanding of different anomaly detection methods, such as outlier detection, Mahalanobis distances, and contextual and collective anomaly detection.  

    After completing this course, you will be able to:

    • Implement clustering methods such as agglomerative, and divisive
    • Write code in R to analyze market segmentation and consumer behaviour
    • Estimate distribution and probabilities of different outcomes
    • Implement dimension reduction using principal component analysis
    • Apply anomaly detection methods to identify fraud
    • Design algorithms with R and learn how to edit or improve code

    Applied Unsupervised Learning with R is designed for business professionals who want to learn about methods to understand their data better, and developers who have an interest in unsupervised learning.

    Although the course is for beginners, it will be beneficial to have some basic, beginner-level familiarity with R. This includes an understanding of how to open the R console, how to read data, and how to create a loop. To easily understand the concepts of this course, you should also know basic mathematical concepts, including exponents, square roots, means, and medians.

     

    Hardware:

    For the optimal student experience, we recommend the following hardware configuration:

    • Processor: Intel Core i5 or equivalent
    • Memory: 4 GB RAM
    • Storage: 5 GB available space
    • An internet connection

     

    Software:

    • OS: Windows 7 SP1 64-bit, Windows 8.1 64-bit or Windows 10 64-bit, Linux (Ubuntu, Debian, Red Hat, or Suse), or the latest version of OS X
    • R (3.0.0 or more recent, available for free at https://cran.r-project.org/)
      Upcoming Dates
      Date on Request

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