Machine learning algorithms

Bridge the gap between a high-level understanding of how an algorithm works and knowing the nuts and bolts to tune your models better. This book will give you the confidence and skills when developing

Machine learning has become a hot topic in computer security in the past couple of years as a technique that can counter advances in attacker competency. This book will provide security and software p

Coefficient of Variation (CV) is a unit free index indicating the consistency of the data associated with a real-world process and is simple to mold into computational paradigms. This book provides ne

Building on Handbook of Machine Learning - Volume 1: Foundation of Artificial Intelligence, this volume on Optimization and Decision Making covers a range of algorithms and their applications. Like th

Put the power of AWS Cloud machine learning services to work in your business and commercial applications! Machine Learning in the AWS Cloud introduces readers to the machine learning (ML) capabiliti

The book focusses on how machine learning and Internet of Things (IoT) has empowered the advancement of information driven arrangements including key concepts and advancements. Ontologies that are use

This book covers machine learning, one of the hottest topics in more recent years. With computing power increasing exponentially and costs decreasing at the same time, there is no better time for mach

Self-driving cars, natural language recognition, and online recommendation engines are all possible thanks to Machine Learning. Now you can create your own genetic algorithms, nature-inspired swarms, Monte Carlo simulations, cellular automata, and clusters. Learn how to test your ML code and dive into even more advanced topics. If you are a beginner-to-intermediate programmer keen to understand machine learning, this book is for you.Discover machine learning algorithms using a handful of self-contained recipes. Build a repertoire of algorithms, discovering terms and approaches that apply generally. Bake intelligence into your algorithms, guiding them to discover good solutions to problems.In this book, you will:Use heuristics and design fitness functions.Build genetic algorithms.Make nature-inspired swarms with ants, bees and particles.Create Monte Carlo simulations.Investigate cellular automata.Find minima and maxima, using hill climbing and simulated annealing.Try selection methods,

Large-scale data analytics using machine learning (ML) underpins many modern data-driven applications. ML systems provide means of specifying and executing these ML workloads in an efficient and scala

This book constitutes the post-conference proceedings of the 4th International Conference on Machine Learning, Optimization, and Data Science, LOD 2018, held in Volterra, Italy, in September 2018.The

This book provides a unique, in-depth discussion of multiview learning, one of the fastest developing branches in machine learning. Multiview Learning has been proved to have good theoretical underpin

A new edition of a graduate-level machine learning textbook that focuses on the analysis and theory of algorithms.This book is a general introduction to machine learning that can serve as a textbook f

Create AI applications in Python and lay the foundations for your career in data scienceKey FeaturesPractical examples that explain key machine learning algorithmsExplore neural networks in detail wit

Industrial Applications of Machine Learning shows how machine learning can be applied to address real-world problems in the fourth industrial revolution, and provides the required knowledge and tools

Brain-machine interfacing or brain-computer interfacing (BMI/BCI) is an emerging and challenging technology used in engineering and neuroscience. The ultimate goal is to provide a pathway from the bra

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

A metaheuristic is a higher-level procedure designed to select a heuristic (partial search algorithm) that may lead to a sufficiently good solution to an optimization problem, especially with incomple

Source Separation and Machine Learning presents the fundamentals in adaptive learning algorithms for Blind Source Separation (BSS) and emphasizes the importance of machine learning perspectives. It il