Machine learning Books

This book discusses various vision systems, their classifications and design strategies. It is essential reading for academic and industrial researchers to understand the computational intelligence research domain as related to vision systems and their...

This book provides a thorough introduction to both individual decision tree algorithms (Part I) and ensembles thereof (Part II). Part I of the book brings several different tree algorithms into focus, both conventional and contemporary. 

"Coding Creativity - How to Build a Chatbot or Art Generator from Scratch with Bonus: The AI Prompting Bible" is a unique and practical self-help book that provides readers with the tools and knowledge to tap into the power of artificial...

Master MATLAB tools for creating machine learning applications through effective code writing, guided by practical examples showcasing the versatility of machine learning in real-world applicationsKey FeaturesWork with the MATLAB Machine Learning Toolbox...

Take your machine learning models to the next level by learning how to leverage hyperparameter tuning, allowing you to control the model's finest detailsKey Features:Gain a deep understanding of how hyperparameter tuning worksExplore exhaustive search,...

Gain the key knowledge and skills required to manage data science projects using CometKey Features:Discover techniques to build, monitor, and optimize your data science projectsMove from prototyping to production using Comet and DevOps toolsGet to grips...

Presents an overview of state-of-the-art AI strategies for solving transportation challenges around the world, with a focus on traffic management, traffic safety, public transportation, urban mobility, and pollution mitigation. It examines modern AI...

The current availability of powerful computers and huge data sets is creating new opportunities in computational mathematics to bring together concepts and tools from graph theory, machine learning and signal processing, creating Data Analytics on Graphs...

Deep reinforcement learning is the combination of reinforcement learning (RL) and deep learning. This field of research has recently been able to solve a wide range of complex decision-making tasks that were previously out of reach for a machine.Deep RL...

The Handbook of Research on AI and ML for Intelligent Machines and Systems offers a comprehensive exploration of the pivotal role played by artificial intelligence (AI) and machine learning (ML) technologies in the development of intelligent machines. As...

Data stealing is a major concern on the internet as hackers and criminals have begun using simple tricks to hack social networks and violate privacy. Cyber-attack methods are progressively modern, and obstructing the attack is increasingly troublesome,...

Space and time are inextricably linked. Reasoning about space often involves reasoning about change in spatial configurations. Qualitative spatial information theory encompasses spatial as well as temporal representation and reasoning. Qualitative...

Transforming Animal Health with Cutting-Edge TechnologyStep into the future of veterinary medicine, where artificial intelligence meets animal care, redefining possibilities and enhancing well-being for our cherished animal companions. In "AI for...

Cognitive Convergence: The Intersection of Human and Artificial Intelligence" explores the fascinating realm where the capabilities of human intelligence merge with the vast potential of artificial intelligence. In this thought-provoking book, the...

In order to help organizations thrive in the modern digital age, "Decisions Based on Data: Analytics for Business Excellence" provides a comprehensive overview of data-driven decision-making and how to traverse its complex landscape. The book is a guide...

Turn raw data into meaningful solutionsDESCRIPTION Learn Data Science from Scratch equips you with the essential tools and techniques, from Python libraries to machine learning algorithms, to tackle real-world problems and make informed decisions...

Digital electronics is a very important subject of electronics and communication engineering. This book is actually meant for graduate engineering students of electronics and communication and electrical engineering; because in these two...

This volume takes the reader on a technological voyage of machine learning advancements, highlighting the systematic changes in algorithms, challenges, and constraints. The technological advancements in the ML arena have transformed and revolutionized...

Random matrix theory plays a central role in statistical physics, computational mathematics and engineering sciences, including data assimilation, signal processing, combinatorial optimization, compressed sensing, econometrics and mathematical finance,...

Technological tools and computational techniques have enhanced the healthcare industry. These advancements have led to significant progress in the diagnosis of heart disorders. Electrocardiogram Signal Classification and Machine Learning: Emerging...

Analytical tools and algorithms are essential in business data and information systems. Efficient economic and financial forecasting in machine learning techniques increases gains while reducing risks. Providing research on predictive models with high...

In the fast pace of the modern world it is important, more than ever, for factories to know how and why their machines are failing and what can be done to prevent it. As such, it is imperative that new research is conducted to make sure that factories...

The expansion of digital data has transformed various sectors of business such as healthcare, industrial manufacturing, and transportation. A new way of solving business problems has emerged through the use of machine learning techniques in conjunction...

Constant improvements in technological applications have allowed for more opportunities to develop automated systems. This not only leads to higher success in smart data analysis, but also ensures that technological progression will continue. Ubiquitous...

"Smart Business Problems and Analytical Hints" is a groundbreaking exploration into the practical applications of data science, artificial intelligence, and machine learning in the realm of business intelligence. This comprehensive guide presents 75...

A practical, step-by-step guide to using Microsoft's AutoML technology on the Azure Machine Learning service for developers and data scientists working with the Python programming languageKey Features:Create, deploy, productionalize, and scale automated...

The Book presents an overview of newly developed watermarking techniques in various independent and hybrid domains Covers the basics of digital watermarking, its types, domain in which it is implemented and the application of machine learning algorithms...

Welcome to "Mastering PCI DSS: Foundation to Mastery," the ultimate Book for anyone seeking to dive deep into the world of payment card industry security. This highly engaging Book is designed to provide you with a thorough understanding of the latest...

Discover how TPOT can be used to handle automation in machine learning and explore the different types of tasks that TPOT can automateKey Features:Understand parallelism and how to achieve it in Python.Learn how to use neurons, layers, and activation...

Comprehensive recipes to give you valuable insights on Transformers, Reinforcement Learning, and moreKey Features:Deep Learning solutions from Kaggle Masters and Google Developer ExpertsGet to grips with the fundamentals including variables, matrices,...

Thompson sampling is an algorithm for online decision problems where actions are taken sequentially in a manner that must balance between exploiting what is known to maximize immediate performance and investing to accumulate new information that may...

Unleash the hidden potential of Python to emerge as a change maker of contemporary industryDescription The current industry (also called Industry 4.0) has witnessed an unprecedented expansion of technology in a short span of time, owing to an exponential...

In contemporary science and engineering applications, the volume of available data is growing at an enormous rate. Spectral methods have emerged as a simple yet surprisingly effective approach for extracting information from massive, noisy and...

Among the data structures commonly used in machine learning, graphs are arguably one of the most general. Graphs allow the modelling of complex objects, each of which can be annotated by metadata. Nonetheless, seemingly simple questions, such as...

Kernel methods are among the most popular techniques in machine learning. From a regularization theory perspective, they provide a natural choice for the hypotheses space and the regularization functional through the notion of reproducing kernel Hilbert...

Evolutionary multi-objective optimization (EMO) has emerged as a sub-discipline of multi-objective optimization, combining the fields of evolutionary computation and classical multiple criteria decision making. This field has applications in artificial...

The beginning of the age of artificial intelligence and machine learning has created new challenges and opportunities for data analysts, statisticians, mathematicians, econometricians, computer scientists and many others. At the root of these techniques...

This book provides an introduction to the growing field of sparse modeling, including application examples, problem formulations that yield sparse solutions, algorithms for finding such solutions, and recent theoretical results on sparse recovery. It...

The Virtual Mind: Designing the Logic to Approximate Human Thinking, through an in-depth and multidisciplinary review, outlines and defines the underpinnings for modelling human thinking through approximating the mind. Whilst there are plenty of efforts...

In today's age of wireless and mobile computing, network and computer security is paramount. Case Studies in Secure Computing: Achievements and Trends gathers the latest research from researchers who share their insights and best practices through...

Have you always been drawn to Python, know the basics, but want to learn more? Do you want to learn all the secrets of the most ductile programming language and the most demanded by the business world?This is the book for you.Nowadays, there are many...

Get to grips with deep learning techniques for building image processing applications using PyTorch with the help of code notebooks and test questionsKey FeaturesImplement solutions to 50 real-world computer vision applications using PyTorchUnderstand...

Create scalable and reliable data pipelines easily with PachydermKey Features:Learn how to build an enterprise-level reproducible data science platform with PachydermDeploy Pachyderm on cloud platforms such as AWS EKS, Google Kubernetes Engine, and...

A step-by-step solution-based guide to preparing building, training, and deploying high-quality machine learning models with Amazon SageMakerKey Features:Perform ML experiments with built-in and custom algorithms in SageMakerExplore proven solutions when...