This book, originally published in 1992, encapsulates ten years of research at the Open University’s Human Cognition Research Laboratory. The research investigates the problems of novice programmers, and is strongly oriented toward the design and implementation of "programming environments" aimed at eliminating or easing novices’ problems. A range of languages is studied: Pascal, SOLO, Lisp, Prolog and "Knowledge Engineering Programming". The primary emphasis of the empirical studies is to...

Publisher's Note: This edition from 2017 is outdated and not compatible with TensorFlow 2.x or any of the most recent updates to Python libraries. A new edition completely updated and revised for 2020 with seven additional chapters that cover RNNs, AI and big data, fundamental use cases, chatbots, and more, is now available. Build real-world Artificial Intelligence applications with Python to intelligently interact with the world around you. Key FeaturesStep int...

From news and speeches to informal chatter on social media, natural language is one of the richest and most underutilized sources of data. Not only does it come in a constant stream, always changing and adapting in context; it also contains information that is not conveyed by traditional data sources. The key to unlocking natural language is through the creative application of text analytics. This practical book presents a data scientist’s approach to building language-aware products with ...

Everything we need to know about metadata, the usually invisible infrastructure for information with which we interact every day.When “metadata” became breaking news, appearing in stories about surveillance by the National Security Agency, many members of the public encountered this once-obscure term from information science for the first time. Should people be reassured that the NSA was “only” collecting metadata about phone calls—information about the caller, the...

Information Modeling and Relational Databases, Second Edition, provides an introduction to ORM (Object-Role Modeling)and much more. In fact, it is the only book to go beyond introductory coverage and provide all of the in-depth instruction you need to transform knowledge from domain experts into a sound database design. This book is intended for anyone with a stake in the accuracy and efficacy of databases: systems analysts, information modelers, database designers and administrators, and ...

Create your own natural language training corpus for machine learning. Whether you’re working with English, Chinese, or any other natural language, this hands-on book guides you through a proven annotation development cycle—the process of adding metadata to your training corpus to help ML algorithms work more efficiently. You don’t need any programming or linguistics experience to get started.Using detailed examples at every step, you’ll learn how the MATTER Annotation Developm...

The World Wide Web has enabled the creation of a global information space comprising linked documents. As the Web becomes ever more enmeshed with our daily lives, there is a growing desire for direct access to raw data not currently available on the Web or bound up in hypertext documents. Linked Data provides a publishing paradigm in which not only documents, but also data, can be a first class citizen of the Web, thereby enabling the extension of the Web with a global data space based on ...

Become an efficient data science practitioner by understanding Python\\'s key conceptsKey Features\[\*\] Quickly get familiar with data science using Python 3.5\[\*\] Save time (and effort) with all the essential tools explained\[\*\] Create effective data science projects and avoid common pitfalls with the help of examples and hints dictated by experienceBook DescriptionFully expanded and upgraded, the second edition of Pyth...

Learn how to apply test-driven development (TDD) to machine-learning algorithms—and catch mistakes that could sink your analysis. In this practical guide, author Matthew Kirk takes you through the principles of TDD and machine learning, and shows you how to apply TDD to several machine-learning algorithms, including Naive Bayesian classifiers and Neural Networks.Machine-learning algorithms often have tests baked in, but they can’t account for human errors in coding. Rather than bli...

Computational thinking is a timeless, transferable skill that enables you to think more clearly and logically, as well as a way to solve specific problems. Beginning with the core ideas of computational thinking, with this book you'll build up an understanding of the practical problem-solving approach and explore how computational thinking aids good practice in programming, complete with a full guided example.

Master text-taming techniques and build effective text-processing applications with RKey Features\[\*\] Develop all the relevant skills for building text-mining apps with R with this easy-to-follow guide\[\*\] Gain in-depth understanding of the text mining process with lucid implementation in the R language\[\*\] Example-rich guide that lets you gain high-quality information from text dataBook DescriptionText Mining (or text ...

Data collection, processing, analysis, and moreAbout This BookYour entry ticket to the world of data science with the stability and power of JavaExplore, analyse, and visualize your data effectively using easy-to-follow examplesA highly practical course covering a broad set of topics - from the basics of Machine Learning to Deep Learning and Big Data frameworks.Who This Book Is ForThis course is meant for...