Applied Unsupervised Learning with R [eLearning]
Applied Unsupervised Learning with R [eLearning]
English | MP4 | AVC 1920×1080 | AAC 48KHz 2ch | 4h 21m | 6.22 GB
eLearning | Skill level: All Levels
Applied Unsupervised Learning with R [eLearning]: Design clever algorithms that discover hidden patterns and draw responses from unstructured, unlabeled data
Starting with the basics, Applied Unsupervised Learning with R explains clustering methods, distribution analysis, data encoders, and features of R that enable you to understand your data better and get answers to your most pressing 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.
- Implement clustering methods such as k-means, agglomerative, and divisive
- Write code in R to analyze market segmentation and consumer behavior
- 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
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.
