Machine Learning for the Web

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Author: Andrea Isoni

Publisher: Packt Publishing Ltd

ISBN: 1785888722

Category: Computers

Page: 298

View: 349

Explore the web and make smarter predictions using Python About This Book Targets two big and prominent markets where sophisticated web apps are of need and importance. Practical examples of building machine learning web application, which are easy to follow and replicate. A comprehensive tutorial on Python libraries and frameworks to get you up and started. Who This Book Is For The book is aimed at upcoming and new data scientists who have little experience with machine learning or users who are interested in and are working on developing smart (predictive) web applications. Knowledge of Django would be beneficial. The reader is expected to have a background in Python programming and good knowledge of statistics. What You Will Learn Get familiar with the fundamental concepts and some of the jargons used in the machine learning community Use tools and techniques to mine data from websites Grasp the core concepts of Django framework Get to know the most useful clustering and classification techniques and implement them in Python Acquire all the necessary knowledge to build a web application with Django Successfully build and deploy a movie recommendation system application using the Django framework in Python In Detail Python is a general purpose and also a comparatively easy to learn programming language. Hence it is the language of choice for data scientists to prototype, visualize, and run data analyses on small and medium-sized data sets. This is a unique book that helps bridge the gap between machine learning and web development. It focuses on the difficulties of implementing predictive analytics in web applications. We focus on the Python language, frameworks, tools, and libraries, showing you how to build a machine learning system. You will explore the core machine learning concepts and then develop and deploy the data into a web application using the Django framework. You will also learn to carry out web, document, and server mining tasks, and build recommendation engines. Later, you will explore Python's impressive Django framework and will find out how to build a modern simple web app with machine learning features. Style and approach Instead of being overwhelmed with multiple concepts at once, this book provides a step-by-step approach that will guide you through one topic at a time. An intuitive step-by step guide that will focus on one key topic at a time. Building upon the acquired knowledge in each chapter, we will connect the fundamental theory and practical tips by illustrative visualizations and hands-on code examples.

Ontology Learning for the Semantic Web

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Author: Alexander Maedche

Publisher: Springer Science & Business Media

ISBN: 1461509254

Category: Computers

Page: 244

View: 6234

Ontology Learning for the Semantic Web explores techniques for applying knowledge discovery techniques to different web data sources (such as HTML documents, dictionaries, etc.), in order to support the task of engineering and maintaining ontologies. The approach of ontology learning proposed in Ontology Learning for the Semantic Web includes a number of complementary disciplines that feed in different types of unstructured and semi-structured data. This data is necessary in order to support a semi-automatic ontology engineering process. Ontology Learning for the Semantic Web is designed for researchers and developers of semantic web applications. It also serves as an excellent supplemental reference to advanced level courses in ontologies and the semantic web.

Machine Learning for Multimodal Interaction

Third International Workshop, MLMI 2006, Bethesda, MD, USA, May 1-4, 2006, Revised Selected Papers

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Author: Steve Renals,Samy Bengio

Publisher: Springer Science & Business Media

ISBN: 3540692673

Category: Computers

Page: 470

View: 8067

This book constitutes the thoroughly refereed post-proceedings of the Third International Workshop on Machine Learning for Multimodal Interaction, MLMI 2006, held in Bethesda, MD, USA, in May 2006. The papers are organized in topical sections on multimodal processing, image and video processing, HCI and applications, discourse and dialogue, speech and audio processing, and NIST meeting recognition evaluation.

Julia Programming Projects

Learn Julia 1.x by building apps for data analysis, visualization, machine learning, and the web

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Author: Adrian Salceanu

Publisher: Packt Publishing Ltd

ISBN: 1788297253

Category: Computers

Page: 500

View: 3818

A step-by-step guide that demonstrates how to build simple-to-advanced applications through examples in Julia Lang 1.x using modern tools Key Features Work with powerful open-source libraries for data wrangling, analysis, and visualization Develop full-featured, full-stack web applications Learn to perform supervised and unsupervised machine learning and time series analysis with Julia Book Description Julia is a new programming language that offers a unique combination of performance and productivity. Its powerful features, friendly syntax, and speed are attracting a growing number of adopters from Python, R, and Matlab, effectively raising the bar for modern general and scientific computing. After six years in the making, Julia has reached version 1.0. Now is the perfect time to learn it, due to its large-scale adoption across a wide range of domains, including fintech, biotech, education, and AI. Beginning with an introduction to the language, Julia Programming Projects goes on to illustrate how to analyze the Iris dataset using DataFrames. You will explore functions and the type system, methods, and multiple dispatch while building a web scraper and a web app. Next, you'll delve into machine learning, where you'll build a books recommender system. You will also see how to apply unsupervised machine learning to perform clustering on the San Francisco business database. After metaprogramming, the final chapters will discuss dates and time, time series analysis, visualization, and forecasting. We'll close with package development, documenting, testing and benchmarking. By the end of the book, you will have gained the practical knowledge to build real-world applications in Julia. What you will learn Leverage Julia's strengths, its top packages, and main IDE options Analyze and manipulate datasets using Julia and DataFrames Write complex code while building real-life Julia applications Develop and run a web app using Julia and the HTTP package Build a recommender system using supervised machine learning Perform exploratory data analysis Apply unsupervised machine learning algorithms Perform time series data analysis, visualization, and forecasting Who this book is for Data scientists, statisticians, business analysts, and developers who are interested in learning how to use Julia to crunch numbers, analyze data and build apps will find this book useful. A basic knowledge of programming is assumed.

Data Mining: Practical Machine Learning Tools and Techniques

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Author: Ian H. Witten,Eibe Frank,Mark A. Hall

Publisher: Elsevier

ISBN: 0080890369

Category: Computers

Page: 664

View: 671

Data Mining: Practical Machine Learning Tools and Techniques, Third Edition, offers a thorough grounding in machine learning concepts as well as practical advice on applying machine learning tools and techniques in real-world data mining situations. This highly anticipated third edition of the most acclaimed work on data mining and machine learning will teach you everything you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining. Thorough updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including new material on Data Transformations, Ensemble Learning, Massive Data Sets, Multi-instance Learning, plus a new version of the popular Weka machine learning software developed by the authors. Witten, Frank, and Hall include both tried-and-true techniques of today as well as methods at the leading edge of contemporary research. The book is targeted at information systems practitioners, programmers, consultants, developers, information technology managers, specification writers, data analysts, data modelers, database R&D professionals, data warehouse engineers, data mining professionals. The book will also be useful for professors and students of upper-level undergraduate and graduate-level data mining and machine learning courses who want to incorporate data mining as part of their data management knowledge base and expertise. Provides a thorough grounding in machine learning concepts as well as practical advice on applying the tools and techniques to your data mining projects Offers concrete tips and techniques for performance improvement that work by transforming the input or output in machine learning methods Includes downloadable Weka software toolkit, a collection of machine learning algorithms for data mining tasks—in an updated, interactive interface. Algorithms in toolkit cover: data pre-processing, classification, regression, clustering, association rules, visualization

Algorithmic Learning Theory

11th International Conference, ALT 2000 Sydney, Australia, December 11-13, 2000 Proceedings

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Author: Hiroki Arimura,Sanjay Jain,Arun Sharma

Publisher: Springer Science & Business Media

ISBN: 3540412379

Category: Computers

Page: 348

View: 2254

This book constitutes the refereed proceedings of the 11th International Conference on Algorithmic Learning Theory, ALT 2000, held in Sydney, Australia in December 2000. The 22 revised full papers presented together with three invited papers were carefully reviewed and selected from 39 submissions. The papers are organized in topical sections on statistical learning, inductive logic programming, inductive inference, complexity, neural networks and other paradigms, support vector machines.

Knowledge Engineering and Knowledge Management

19th International Conference, EKAW 2014, Linköping, Sweden, November 24-28, 2014, Proceedings

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Author: Krzysztof Janowicz,Stefan Schlobach,Patrick Lambrix,Eero Hyvönen

Publisher: Springer

ISBN: 3319137042

Category: Computers

Page: 620

View: 558

This book constitutes the refereed proceedings of the 19th International Conference on Knowledge Engineering and Knowledge Management, EKAW 2014, held in Linköping, Sweden, in November 2014. The 24 full papers and 21 short papers presented were carefully reviewed and selected from 138 submissions. The papers cover all aspects of eliciting, acquiring, modeling, and managing knowledge, the construction of knowledge-intensive systems and services for the Semantic Web, knowledge management, e-business, natural language processing, intelligent information integration, personal digital assistance systems, and a variety of other related topics.

Quantitative Semantics and Soft Computing Methods for the Web: Perspectives and Applications

Perspectives and Applications

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Author: Brena, Ramon F.

Publisher: IGI Global

ISBN: 1609608828

Category: Computers

Page: 304

View: 6607

The Internet has been acknowledged as a recent technological revolution, due to its significant impact on society as a whole. Nevertheless, precisely due to its impact, limitations of the current Internet are becoming apparent; in particular, its inability to automatically take into account the meaning of online documents. Some proposals for taking meaning into account began to appear, mainly the so-called Semantic Web, which includes a set of technologies like RDF that are based on new markup languages. Though these technologies could be technically sound, practical limitations, such as the high training level required to construct Semantic Web pages, and the small proportion of current Semantic Web pages make the Sematic Web marginal today and also in the near foreseeable future. Quantitative Semantics and Soft Computing Methods for the Web: Perspectives and Applications will provide relevant theoretical frameworks and the latest empirical research findings related to quantitative, soft-computing and approximate methods for dealing with Internet semantics. The target audience of this book is composed of professionals and researchers working in the fields of information and knowledge related technologies (e.g. Information sciences and technology, computer science, Web science, and artificial intelligence).

Machine Learning for Text

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Author: Charu C. Aggarwal

Publisher: Springer

ISBN: 3319735314

Category: Computers

Page: 493

View: 3628

Text analytics is a field that lies on the interface of information retrieval,machine learning, and natural language processing, and this textbook carefully covers a coherently organized framework drawn from these intersecting topics. The chapters of this textbook is organized into three categories: - Basic algorithms: Chapters 1 through 7 discuss the classical algorithms for machine learning from text such as preprocessing, similarity computation, topic modeling, matrix factorization, clustering, classification, regression, and ensemble analysis. - Domain-sensitive mining: Chapters 8 and 9 discuss the learning methods from text when combined with different domains such as multimedia and the Web. The problem of information retrieval and Web search is also discussed in the context of its relationship with ranking and machine learning methods. - Sequence-centric mining: Chapters 10 through 14 discuss various sequence-centric and natural language applications, such as feature engineering, neural language models, deep learning, text summarization, information extraction, opinion mining, text segmentation, and event detection. This textbook covers machine learning topics for text in detail. Since the coverage is extensive,multiple courses can be offered from the same book, depending on course level. Even though the presentation is text-centric, Chapters 3 to 7 cover machine learning algorithms that are often used indomains beyond text data. Therefore, the book can be used to offer courses not just in text analytics but also from the broader perspective of machine learning (with text as a backdrop). This textbook targets graduate students in computer science, as well as researchers, professors, and industrial practitioners working in these related fields. This textbook is accompanied with a solution manual for classroom teaching.

Hands-on Machine Learning with JavaScript

Solve complex computational web problems using machine learning

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Author: Burak Kanber

Publisher: Packt Publishing Ltd

ISBN: 1788990307

Category: Computers

Page: 356

View: 5400

A definitive guide to creating an intelligent web application with the best of machine learning and JavaScript Key Features Solve complex computational problems in browser with JavaScript Teach your browser how to learn from rules using the power of machine learning Understand discoveries on web interface and API in machine learning Book Description In over 20 years of existence, JavaScript has been pushing beyond the boundaries of web evolution with proven existence on servers, embedded devices, Smart TVs, IoT, Smart Cars, and more. Today, with the added advantage of machine learning research and support for JS libraries, JavaScript makes your browsers smarter than ever with the ability to learn patterns and reproduce them to become a part of innovative products and applications. Hands-on Machine Learning with JavaScript presents various avenues of machine learning in a practical and objective way, and helps implement them using the JavaScript language. Predicting behaviors, analyzing feelings, grouping data, and building neural models are some of the skills you will build from this book. You will learn how to train your machine learning models and work with different kinds of data. During this journey, you will come across use cases such as face detection, spam filtering, recommendation systems, character recognition, and more. Moreover, you will learn how to work with deep neural networks and guide your applications to gain insights from data. By the end of this book, you'll have gained hands-on knowledge on evaluating and implementing the right model, along with choosing from different JS libraries, such as NaturalNode, brain, harthur, classifier, and many more to design smarter applications. What you will learn Get an overview of state-of-the-art machine learning Understand the pre-processing of data handling, cleaning, and preparation Learn Mining and Pattern Extraction with JavaScript Build your own model for classification, clustering, and prediction Identify the most appropriate model for each type of problem Apply machine learning techniques to real-world applications Learn how JavaScript can be a powerful language for machine learning Who this book is for This book is for you if you are a JavaScript developer who wants to implement machine learning to make applications smarter, gain insightful information from the data, and enter the field of machine learning without switching to another language. Working knowledge of JavaScript language is expected to get the most out of the book.

Database Systems for Advanced Applications

15th International Conference, DASFAA 2010, Tsukuba, Japan, April 1-4, 2010, Proceedings

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Author: Hiroyuki Kitagawa,Yoshiharu Ishikawa,Qing Li,Chiemi Watanabe

Publisher: Springer Science & Business Media

ISBN: 3642120970

Category: Computers

Page: 485

View: 7148

This two volume set LNCS 5981 and LNCS 5982 constitutes the refereed proceedings of the 15th International Conference on Database Systems for Advanced Applications, DASFAA 2010, held in Tsukuba, Japan, in April 2010. The 39 revised full papers and 16 revised short papers presented together with 3 invited keynote papers, 22 demonstration papers, 6 industrial papers, and 2 keynote talks were carefully reviewed and selected from 285 submissions. The papers of the first volume are organized in topical sections on P2P-based technologies, data mining technologies, XML search and matching, graphs, spatial databases, XML technologies, time series and streams, advanced data mining, query processing, Web, sensor networks and communications, information management, as well as communities and Web graphs. The second volume contains contributions related to trajectories and moving objects, skyline queries, privacy and security, data streams, similarity search and event processing, storage and advanced topics, industrial, demo papers, and tutorials and panels.

The Semantic Web: Research and Applications

6th European Semantic Web Conference, ESWC 2009 Heraklion, Crete, Greece, May 31– June 4, 2009 Proceedings

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Author: Lora Aroyo,Paolo Traverso,Fabio Ciravegna,Philipp Cimiano,Tom Heath,Eero Hyvönen,Riichiro Mizoguchi,Eyal Oren,Marta Sabou,Elena Simperl

Publisher: Springer Science & Business Media

ISBN: 3642021204

Category: Computers

Page: 961

View: 2945

This book constitutes the refereed proceedings of the 6th European Semantic Web Conference, ESWC 2009, held in Heraklion, Crete, Greece, in May/June 2009. The 45 revised full papers of the research track presented together with the abstracts of 4 keynote lectures were carefully reviewed and selected from more than 250 submissions. The papers are organized in topical sections on applications, evaluation and benchmarking, ontologies and natural language, ontology alignment, ontology engineering, query processing, reasoning, search and identities, semantic Web architectures, semantic Web services, and tagging and annotation. In addition to the technical research track, this book presents 8 contributions to the ESWC 2009 PhD symposium, 24 system demo papers, as well as 8 contributions to the semantic Web in-use track.

Web Technologies: Concepts, Methodologies, Tools, and Applications

Concepts, Methodologies, Tools, and Applications

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Author: Tatnall, Arthur

Publisher: IGI Global

ISBN: 1605669830

Category: Computers

Page: 2824

View: 4998

With the technological advancement of mobile devices, social networking, and electronic services, Web technologies continues to play an ever-growing part of the global way of life, incorporated into cultural, economical, and organizational levels. Web Technologies: Concepts, Methodologies, Tools, and Applications (4 Volume) provides a comprehensive depiction of current and future trends in support of the evolution of Web information systems, Web applications, and the Internet. Through coverage of the latest models, concepts, and architectures, this multiple-volume reference supplies audiences with an authoritative source of information and direction for the further development of the Internet and Web-based phenomena.

Statistik-Workshop für Programmierer

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Author: Allen B. Downey

Publisher: O'Reilly Germany

ISBN: 3868993436

Category: Computers

Page: 160

View: 747

Wenn Sie programmieren können, beherrschen Sie bereits Techniken, um aus Daten Wissen zu extrahieren. Diese kompakte Einführung in die Statistik zeigt Ihnen, wie Sie rechnergestützt, anstatt auf mathematischem Weg Datenanalysen mit Python durchführen können. Praktischer Programmier-Workshop statt grauer Theorie: Das Buch führt Sie anhand eines durchgängigen Fallbeispiels durch eine vollständige Datenanalyse -- von der Datensammlung über die Berechnung statistischer Kennwerte und Identifikation von Mustern bis hin zum Testen statistischer Hypothesen. Gleichzeitig werden Sie mit statistischen Verteilungen, den Regeln der Wahrscheinlichkeitsrechnung, Visualisierungsmöglichkeiten und vielen anderen Arbeitstechniken und Konzepten vertraut gemacht. Statistik-Konzepte zum Ausprobieren: Entwickeln Sie über das Schreiben und Testen von Code ein Verständnis für die Grundlagen von Wahrscheinlichkeitsrechnung und Statistik: Überprüfen Sie das Verhalten statistischer Merkmale durch Zufallsexperimente, zum Beispiel indem Sie Stichproben aus unterschiedlichen Verteilungen ziehen. Nutzen Sie Simulationen, um Konzepte zu verstehen, die auf mathematischem Weg nur schwer zugänglich sind. Lernen Sie etwas über Themen, die in Einführungen üblicherweise nicht vermittelt werden, beispielsweise über die Bayessche Schätzung. Nutzen Sie Python zur Bereinigung und Aufbereitung von Rohdaten aus nahezu beliebigen Quellen. Beantworten Sie mit den Mitteln der Inferenzstatistik Fragestellungen zu realen Daten.

Foundations for the Web of Information and Services

A Review of 20 Years of Semantic Web Research

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Author: Dieter Fensel

Publisher: Springer Science & Business Media

ISBN: 9783642197970

Category: Computers

Page: 341

View: 4134

In the mid 1990s, Tim Berners-Lee had the idea of developing the World Wide Web into a „Semantic Web“, a web of information that could be interpreted by machines in order to allow the automatic exploitation of data, which until then had to be done by humans manually. One of the first people to research topics related to the Semantic Web was Professor Rudi Studer. From the beginning, Rudi drove projects like ONTOBROKER and On-to-Knowledge, which later resulted in W3C standards such as RDF and OWL. By the late 1990s, Rudi had established a research group at the University of Karlsruhe, which later became the nucleus and breeding ground for Semantic Web research, and many of today’s well-known research groups were either founded by his disciples or benefited from close cooperation with this think tank. In this book, published in celebration of Rudi’s 60th birthday, many of his colleagues look back on the main research results achieved during the last 20 years. Under the editorship of Dieter Fensel, once one of Rudi’s early PhD students, an impressive list of contributors and contributions has been collected, covering areas like Knowledge Management, Ontology Engineering, Service Management, and Semantic Search. Overall, this book provides an excellent overview of the state of the art in Semantic Web research, by combining historical roots with the latest results, which may finally make the dream of a “Web of knowledge, software and services” come true.

Semantic Web Services and Web Process Composition

First International Workshop, SWSWPC 2004, San Diego, CA, USA, July 6, 2004, Revised Selected Papers

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Author: Jorge Cardoso

Publisher: Springer Science & Business Media

ISBN: 9783540243281

Category: Computers

Page: 146

View: 3078

This book constitutes the thoroughly refereed postproceedings of the First International Workshop on Semantic Web Services and Web Process Composition, SWSWPC 2004, held in San Diego, CA, USA in July 2004. The 9 revised full papers presented together with an introduction by the volume editors, a panel summary and the extended abstract of an invited talk were carefully selected during two rounds of reviewing and improvement. Web services and Web processes promise to ease several current Web infrastructure challenges, such as the integration of data, applications and processes. Web services are truly platform independent and allow the development of distributed loosely coupled applications, a key characteristic for the success of dynamic processes.

Machine Learning For Dummies

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Author: John Paul Mueller,Luca Massaron

Publisher: John Wiley & Sons

ISBN: 111924577X

Category: Computers

Page: 432

View: 2805

Your no-nonsense guide to making sense of machine learning Machine learning can be a mind-boggling concept for the masses, but those who are in the trenches of computer programming know just how invaluable it is. Without machine learning, fraud detection, web search results, real-time ads on web pages, credit scoring, automation, and email spam filtering wouldn't be possible, and this is only showcasing just a few of its capabilities. Written by two data science experts, Machine Learning For Dummies offers a much-needed entry point for anyone looking to use machine learning to accomplish practical tasks. Covering the entry-level topics needed to get you familiar with the basic concepts of machine learning, this guide quickly helps you make sense of the programming languages and tools you need to turn machine learning-based tasks into a reality. Whether you're maddened by the math behind machine learning, apprehensive about AI, perplexed by preprocessing data—or anything in between—this guide makes it easier to understand and implement machine learning seamlessly. Grasp how day-to-day activities are powered by machine learning Learn to 'speak' certain languages, such as Python and R, to teach machines to perform pattern-oriented tasks and data analysis Learn to code in R using R Studio Find out how to code in Python using Anaconda Dive into this complete beginner's guide so you are armed with all you need to know about machine learning!

Machine Learning. Eine Analyse des State of the Art

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Author: Kevin Donath

Publisher: GRIN Verlag

ISBN: 3668614466

Category: Computers

Page: 51

View: 8262

Machine Learning ist eine mögliche Umsetzung von künstlicher Intelligenz (kurz KI), die in Software für Dinge wie Computer Vision, Spracherkennung, Sprachverarbeitung und Steuerung von Robotern eingesetzt wird. KI ist ein Zweig der Informatik, der sich damit beschäftigt intelligentes Verhalten in Computern zu simulieren. Dieses Konzept wird für Firmen aus allen Wirtschaftszweigen sowohl in internen Prozessen als auch in Produkten immer bedeutender. In dieser Publikation gibt der Autor einen Überblick über den aktuellen Stand des Machine Learning. Sein Fokus liegt dabei auf der Darstellung des aktuellen Standes der Technologien, den Aktivitäten der Key Player und den Anwendungsgebieten.

Graph-theoretic Techniques for Web Content Mining

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Author: Adam Schenker

Publisher: World Scientific

ISBN: 9789812569455

Category: Computers

Page: 249

View: 4462

This book describes exciting new opportunities for utilizing robust graph representations of data with common machine learning algorithms. Graphs can model additional information which is often not present in commonly used data representations, such as vectors.