Data Science Algorithms for Unsupervised Learning

This title is printed to order. This book may have been self-published. If so, we cannot guarantee the quality of the content. In the main most books will have gone through the editing process however some may not. We therefore suggest that you be aware of this before ordering this book. If in doubt check either the author or publisher’s details as we are unable to accept any returns unless they are faulty. Please contact us if you have any questions.

Artificial Intelligence and Data Science combines mathematical algorithms and techniques from Machine Learning, Deep Learning and Big Data to extract the knowledge contained in the data and present it in an understandable and automatic way. In the field of Artificial Intelligence and Data Science, we can highlight two types of learning that are widely used to train machines and devices to understand a set of data: supervised learning and unsupervised learning. supervised learnig techniques trains a model on known input and output data so that it can predict future outputs, and unsupervised learning techniques finds hidden patterns or intrinsic structures in input data. Unsupervised learning is more closely aligned with Artificial Intelligence as it gives the idea that a machine can learn to identify complex processes and patterns without the need for a human to provide guidance and supervision throughout the learning process. This book develops unsupervised learning techniques including cluster analysis, hierarchical cluster analysis, nonhierarchical cluster analysis, clustering with gaussian mixture models, clustering with hidden Markov models, Markov chaines, nearest neighbors classifiers, kNN classifiers, cluster visualization and cluster evaluation