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. 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.
- 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