Natural Language Annotation for Machine Learning

First booklet in the Language Olympic series. For English as Second Language (ESL) and English Language Learning (ELL) tutoring.

Key FeaturesSecond edition of the bestselling book on Machine LearningA practical approach to key frameworks in data science, machine learning, and deep learningUse the most powerful Python libraries

This book investigates language learning by young learners in instructed contexts and the potential for mixed methods to allow for a comprehensive understanding of early language learning. Chapters pr

This book investigates language learning by young learners in instructed contexts and the potential for mixed methods to allow for a comprehensive understanding of early language learning. Chapters pr

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

In Practical Machine Learning with H2O.ai, author Darren Cook introduces readers to H2O, an open-source machine learning package that is gaining popularity in the data science community. This concise

Specialists in foreign language learning and teaching explore the intersection of computer-aided language approaches with multicultural sensitivities, both for language teachers and for other educ

Data Mining: Practical Machine Learning Tools and Techniques offers a thorough grounding in machine learning concepts as well as practical advice on applying the tools and techniques in real-world dat

This is the first textbook on a generally applicable control strategy for turbulence and other complex nonlinear systems. The approach of the book employs powerful methods of machine learning for opti

Advances in Machine Learning and Data Mining for Astronomy documents numerous successful collaborations among computer scientists, statisticians, and astronomers who illustrate the application of stat

Today, machine learning underlies a range of applications we use every day, from product recommendations to voice recognition -- as well as some we don't yet use everyday

Machine learning, one of the top emerging sciences, has an extremely broad range of applications. However, many books on the subject provide only a theoretical approach, making it difficult for a newc

Quantum Machine Learning bridges the gap between abstract developments in quantum computing and the applied research on machine learning. Paring down the complexity of the disciplines involved, it foc

The work presents new approaches to Machine Learning for Cyber Physical Systems, experiences and visions. It contains some selected papersfrom the international Conference ML4CPS – Machine Learn

This book presents the features and advantages offered by complex networks in the machine learning domain. In the first part, an overview on complex networks and network-based machine learning is pres

Task Sequencing and Instructed Second Language Learning provides theoretical rationales for, and empirical studies of, the effects of sequencing language learning tasks to maximize second language lea

By teaching you how to code machine-learning algorithms using a test-driven approach, this practical book helps you gain the confidence you need to use machine learning effectively in a business envir

This book focuses on tackling challenging linguistic issues so that computers can better process natural language for machine translation and other related tasks. This is not a book on abstract semant

Predictive Analytics with Microsoft Azure Machine Learning, Second Edition is a practical tutorial introduction to the field of data science and machine learning, with a focus on building and deployin

Machine Learning Projects for .NET Developers shows you how to build smarter .NET applications that learn from data, using simple algorithms and techniques that can be applied to a wide range of real-

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

A practical, scenario-based tutorial, this book will help you get to grips with machine learning with Python and start building your own machine learning projects. By the end of the book you will have

Dig deep into the data with a hands-on guide to machine learningMachine Learning: Hands-On for Developers and Technical Professionals provides hands-on instruction and fully-coded working examples for

Building a simple but powerful recommendation system is much easier than you think. Approachable for all levels of expertise, this report explains innovations that make machine learning practical for

"Task Sequencing and Instructed Second Language Learning provides theoretical rationales for, and empirical studies of, the effects of sequencing language learning tasks to maximize second language le

An informative and comprehensive overview of the state-of-the-art in natural language generation (NLG) for interactive systems, this guide serves to introduce graduate students and new researchers to the field of natural language processing and artificial intelligence, while inspiring them with ideas for future research. Detailing the techniques and challenges of NLG for interactive applications, it focuses on the research into systems that model collaborativity and uncertainty, are capable of being scaled incrementally, and can engage with the user effectively. A range of real-world case studies is also included. The book and the accompanying website feature a comprehensive bibliography, and refer the reader to corpora, data, software and other resources for pursuing research on natural language generation and interactive systems, including dialog systems, multimodal interfaces and assistive technologies. It is an ideal resource for students and researchers in computational linguistic

Human-Machine Interfaces for Industrial Robotic Cells, discusses how to make human-machine interfaces more natural and accessible to humans. Programming robots is still too complex and too slow. Cons

Offering a fundamental basis in kernel-based learning theory, this book covers both statistical and algebraic principles. It provides over 30 major theorems for kernel-based supervised and unsupervised learning models. The first of the theorems establishes a condition, arguably necessary and sufficient, for the kernelization of learning models. In addition, several other theorems are devoted to proving mathematical equivalence between seemingly unrelated models. With over 25 closed-form and iterative algorithms, the book provides a step-by-step guide to algorithmic procedures and analysing which factors to consider in tackling a given problem, enabling readers to improve specifically designed learning algorithms, build models for new applications and develop efficient techniques suitable for green machine learning technologies. Numerous real-world examples and over 200 problems, several of which are Matlab-based simulation exercises, make this an essential resource for graduate student

Offering sequenced guidance for non-specialists on how to reap the benefits of machine learning in medicine and healthcare, this text harnesses the power of cutting-edge computing to maximize the acce

A practical, scenario-based tutorial, this book will help you get to grips with machine learning with Python and start building your own machine learning projects. By the end of the book you will have

The integration of machine learning techniques and cartoon animation research is fast becoming a hot topic. This book helps readers learn the latest machine learning techniques, including patch alignm

Sewing Machine Secrets is an indispensable resource for sewers at all levels, showing the reader how to choose, use and maintain their sewing machine, matering sewing techniques and learning handy tip

"The NCL Natural Constraint Language" presents the NCL language which is a description language in conventional mathematical logic for modeling and solving constraint satisfaction problems. NCL differ

Today's Web-enabled deluge of electronic data calls for automated methods of dataanalysis. Machine learning provides these, developing methods that can automatically detect patternsin data and then us

Reinforcement and Systemic Machine Learning for Decision MakingThere are always difficulties in making machines that learn from experience. Complete information is not always available—or it beco

Advances in Machine Learning and Data Mining for Astronomy documents numerous successful collaborations among computer scientists, statisticians, and astronomers who illustrate the application of stat

Machine Learning and Knowledge Discovery for Engineering Systems Health Management presents state-of-the-art tools and techniques for automatically detecting, diagnosing, and predicting the effects of

Drama is increasingly being recognised as a valuable pedagogy for language learning as it can harness children‘s imaginations and stimulate their desire to communicate. Second Learning Language throug

This book is for anyone who has biomedical data and needs to identify variables that predict an outcome, for two-group outcomes such as tumor/not-tumor, survival/death, or response from treatment. Statistical learning machines are ideally suited to these types of prediction problems, especially if the variables being studied may not meet the assumptions of traditional techniques. Learning machines come from the world of probability and computer science but are not yet widely used in biomedical research. This introduction brings learning machine techniques to the biomedical world in an accessible way, explaining the underlying principles in nontechnical language and using extensive examples and figures. The authors connect these new methods to familiar techniques by showing how to use the learning machine models to generate smaller, more easily interpretable traditional models. Coverage includes single decision trees, multiple-tree techniques such as Random Forests™, neural nets, suppor

This book is for anyone who has biomedical data and needs to identify variables that predict an outcome, for two-group outcomes such as tumor/not-tumor, survival/death, or response from treatment. Statistical learning machines are ideally suited to these types of prediction problems, especially if the variables being studied may not meet the assumptions of traditional techniques. Learning machines come from the world of probability and computer science but are not yet widely used in biomedical research. This introduction brings learning machine techniques to the biomedical world in an accessible way, explaining the underlying principles in nontechnical language and using extensive examples and figures. The authors connect these new methods to familiar techniques by showing how to use the learning machine models to generate smaller, more easily interpretable traditional models. Coverage includes single decision trees, multiple-tree techniques such as Random Forests™, neural nets, suppor