Clojure for Machine Learning

In DetailMachine learning is all about understanding and predicting patterns in data. Handling massive amounts of data is not uncommon in this day and age, and machine learning helps us model large amounts of data, thus allowing us to predict future values in the given data. Machine learning is still an area of active research in the field of computer science.Clojure for Machine Learning is an introduction to machine learning problems, techniques and algorithms. This book demonstrates how you can apply these techniques to real-world problems using the Clojure programming language.This book explores several machine learning techniques and also describes how you can use Clojure to build machine learning systems. You will explore several popular Clojure libraries which can be used to leverage machine learning techniques.This book starts off by introducing simple machine learning problems of regression and classification. In the later chapters, more advanced models of machine learning such as clustering, artificial neural networks and support vector machines will be explored in detail. This book also describes how you can implement these machine learning techniques in Clojure. Also, several Clojure libraries which can be useful in solving machine learning problems are also demonstrated.&quote;Clojure for Machine Learning&quote; familiarizes you with several pragmatic machine learning techniques. By the end of this book, you will be fully aware of the Clojure libraries that could be used to solve a given machine learning problem.ApproachA book that brings out the strengths of Clojure programming that have to facilitate machine learning. Each topic is described in substantial detail, and examples and libraries in Clojure are also demonstrated.Who this book is forThis book is intended for Clojure developers who want to explore the area of machine learning. Basic understanding of the Clojure programming language is required, but thorough acquaintance with the standard Clojure library or any libraries are not required. Familiarity with theoretical concepts and notation of mathematics and statistics would be an added advantage.