Machine Learning for Email

Machine Learning with Spark and Python Essential Techniques for Predictive Analytics Second Edition simplifies ML for practical uses by focusing on two key algorithms. This new second edition improves

This book develops a machine-learning framework for predicting economic growth. It can also be considered as a primer for using machine learning (also known as data mining or data analytics) to answer

This second edition focuses on audio, image and video data, the three main types of input that machines deal with when interacting with the real world. A set of appendices provides the reader with sel

Many Python developers are curious about what machine learning is and how it can be concretely applied to solve issues faced in businesses handling medium to large amount of data.Machine Learning with

The 21 papers in this collection develop new algorithms for helping machines learn from previous data and describe the application of machine learning algorithms to image retrieval, computer vision, h

Fifteen articles on the theory and application of adaptive database search and retrieval technologies showcase current research in the growing field of machine learning algorithms. Divided into sectio

Are you stuck in getting started with machine learning with python? A Step-By-Step Guide to Learn and Master Python Machine Learning walks you through steps for getting started with Machine Learning w

This book provides the first systematic study of facial kinship verification, a new research topic in biometrics. It presents three key aspects of facial kinship verification: 1) feature learning for

This authoritative, expanded and updated second edition of Encyclopedia of Machine Learning and Data Mining provides easy access to core information for those seeking entry into any aspect within the

Written by global leaders and pioneers in the field, this book is a must-have read for researchers, practicing engineers and university faculty working in SHM."Structural Health Monitoring: A Machine

The current book is the first publication of a complete overview of machine learning methodologies for the medical and health sector. It was written as a training companion and as a must-read, not onl

This book explores some of the emerging scientific and technological areas in which the need for data analytics arises and is likely to play a significant role in the years to come. At the dawn of the

With its intuitive yet rigorous approach to machine learning, this text provides students with the fundamental knowledge and practical tools needed to conduct research and build data-driven products. The authors prioritize geometric intuition and algorithmic thinking, and include detail on all the essential mathematical prerequisites, to offer a fresh and accessible way to learn. Practical applications are emphasized, with examples from disciplines including computer vision, natural language processing, economics, neuroscience, recommender systems, physics, and biology. Over 300 color illustrations are included and have been meticulously designed to enable an intuitive grasp of technical concepts, and over 100 in-depth coding exercises (in Python) provide a real understanding of crucial machine learning algorithms. A suite of online resources including sample code, data sets, interactive lecture slides, and a solutions manual are provided online, making this an ideal text both for grad

Machine learning is one of the fastest growing areas of computer science, with far-reaching applications. The aim of this textbook is to introduce machine learning, and the algorithmic paradigms it offers, in a principled way. The book provides a theoretical account of the fundamentals underlying machine learning and the mathematical derivations that transform these principles into practical algorithms. Following a presentation of the basics, the book covers a wide array of central topics unaddressed by previous textbooks. These include a discussion of the computational complexity of learning and the concepts of convexity and stability; important algorithmic paradigms including stochastic gradient descent, neural networks, and structured output learning; and emerging theoretical concepts such as the PAC-Bayes approach and compression-based bounds. Designed for advanced undergraduates or beginning graduates, the text makes the fundamentals and algorithms of machine learning accessible t