The last two decades have seen Machine Learning emerge as a critical component of information technology. As more and more information becomes accessible, it stands to reason that sophisticated data analysis will grow increasingly vital to the advancement of technology. From a high level of generalization, we may say that machine learns anytime it makes adjustments to its internal structure, software, or data (in reaction to or as a result of external information) that result in an improvement in performance. Some of these alterations, such adding a new record to a database, belong more appropriately in other fields and aren’t always better understood when labeled learning. Yet when, for instance, a voice-recognition system becomes better at its job after hearing several examples of a given person’s speech. This book aims to provide reader a bird’s-eye perspective of the wide variety of applications that boil down to a machine learning issue and to offer some semblance of organization to the otherwise chaotic landscape. Book will provide a collection of simple algorithms for addressing a significant challenge, namely, categorization. Later sections of the book will include more complex methods, elaborate analyses, and discussions of more broad topics.