Machine Learning for the Web

DOWNLOAD NOW »

Author: Andrea Isoni

Publisher: Packt Publishing Ltd

ISBN: 1785888722

Category: Computers

Page: 298

View: 6182

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

DOWNLOAD NOW »

Author: Alexander Maedche

Publisher: Springer Science & Business Media

ISBN: 9780792376569

Category: Computers

Page: 244

View: 5257

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 with AWS

Explore the power of cloud services for your machine learning and artificial intelligence projects

DOWNLOAD NOW »

Author: Jeffrey Jackovich,Ruze Richards

Publisher: Packt Publishing Ltd

ISBN: 1789809576

Category: Computers

Page: 254

View: 4904

Use artificial intelligence and machine learning on AWS to create engaging applications Key Features Explore popular AI and ML services with their underlying algorithms Use the AWS environment to manage your AI workflow Reinforce key concepts with hands-on exercises using real-world datasets Book Description Machine Learning with AWS is the right place to start if you are a beginner interested in learning useful artificial intelligence (AI) and machine learning skills using Amazon Web Services (AWS), the most popular and powerful cloud platform. You will learn how to use AWS to transform your projects into apps that work at high speed and are highly scalable. From natural language processing (NLP) applications, such as language translation and understanding news articles and other text sources, to creating chatbots with both voice and text interfaces, you will learn all that there is to know about using AWS to your advantage. You will also understand how to process huge numbers of images fast and create machine learning models. By the end of this book, you will have developed the skills you need to efficiently use AWS in your machine learning and artificial intelligence projects. What you will learn Get up and running with machine learning on the AWS platform Analyze unstructured text using AI and Amazon Comprehend Create a chatbot and interact with it using speech and text input Retrieve external data via your chatbot Develop a natural language interface Apply AI to images and videos with Amazon Rekognition Who this book is for Machine Learning with AWS is ideal for data scientists, programmers, and machine learning enthusiasts who want to learn about the artificial intelligence and machine learning capabilities of Amazon Web Services.

Machine Learning for Text

DOWNLOAD NOW »

Author: Charu C. Aggarwal

Publisher: Springer

ISBN: 3319735314

Category: Computers

Page: 493

View: 1227

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.

Machine Learning for Multimodal Interaction

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

DOWNLOAD NOW »

Author: Steve Renals,Samy Bengio

Publisher: Springer Science & Business Media

ISBN: 3540692673

Category: Computers

Page: 470

View: 4575

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.

Hands-on Machine Learning with JavaScript

Solve complex computational web problems using machine learning

DOWNLOAD NOW »

Author: Burak Kanber

Publisher: Packt Publishing Ltd

ISBN: 1788990307

Category: Computers

Page: 356

View: 6734

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.

Data Mining: Practical Machine Learning Tools and Techniques

DOWNLOAD NOW »

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

Publisher: Elsevier

ISBN: 0080890369

Category: Computers

Page: 664

View: 5426

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

DOWNLOAD NOW »

Author: Hiroki Arimura,Sanjay Jain,Arun Sharma

Publisher: Springer Science & Business Media

ISBN: 3540412379

Category: Computers

Page: 348

View: 8147

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

DOWNLOAD NOW »

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

Publisher: Springer

ISBN: 3319137042

Category: Computers

Page: 620

View: 1355

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.

Machine Learning For Dummies

DOWNLOAD NOW »

Author: John Paul Mueller,Luca Massaron

Publisher: John Wiley & Sons

ISBN: 111924577X

Category: Computers

Page: 432

View: 3285

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!

Python Machine Learning

DOWNLOAD NOW »

Author: Sebastian Raschka

Publisher: Packt Publishing Ltd

ISBN: 1783555149

Category: Computers

Page: 454

View: 3209

Unlock deeper insights into Machine Leaning with this vital guide to cutting-edge predictive analytics About This Book Leverage Python's most powerful open-source libraries for deep learning, data wrangling, and data visualization Learn effective strategies and best practices to improve and optimize machine learning systems and algorithms Ask – and answer – tough questions of your data with robust statistical models, built for a range of datasets Who This Book Is For If you want to find out how to use Python to start answering critical questions of your data, pick up Python Machine Learning – whether you want to get started from scratch or want to extend your data science knowledge, this is an essential and unmissable resource. What You Will Learn Explore how to use different machine learning models to ask different questions of your data Learn how to build neural networks using Keras and Theano Find out how to write clean and elegant Python code that will optimize the strength of your algorithms Discover how to embed your machine learning model in a web application for increased accessibility Predict continuous target outcomes using regression analysis Uncover hidden patterns and structures in data with clustering Organize data using effective pre-processing techniques Get to grips with sentiment analysis to delve deeper into textual and social media data In Detail Machine learning and predictive analytics are transforming the way businesses and other organizations operate. Being able to understand trends and patterns in complex data is critical to success, becoming one of the key strategies for unlocking growth in a challenging contemporary marketplace. Python can help you deliver key insights into your data – its unique capabilities as a language let you build sophisticated algorithms and statistical models that can reveal new perspectives and answer key questions that are vital for success. Python Machine Learning gives you access to the world of predictive analytics and demonstrates why Python is one of the world's leading data science languages. If you want to ask better questions of data, or need to improve and extend the capabilities of your machine learning systems, this practical data science book is invaluable. Covering a wide range of powerful Python libraries, including scikit-learn, Theano, and Keras, and featuring guidance and tips on everything from sentiment analysis to neural networks, you'll soon be able to answer some of the most important questions facing you and your organization. Style and approach Python Machine Learning connects the fundamental theoretical principles behind machine learning to their practical application in a way that focuses you on asking and answering the right questions. It walks you through the key elements of Python and its powerful machine learning libraries, while demonstrating how to get to grips with a range of statistical models.

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

Perspectives and Applications

DOWNLOAD NOW »

Author: Brena, Ramon F.

Publisher: IGI Global

ISBN: 1609608828

Category: Computers

Page: 304

View: 9590

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).

Introduction to Machine Learning

DOWNLOAD NOW »

Author: Ethem Alpaydin

Publisher: MIT Press

ISBN: 0262303264

Category: Computers

Page: 584

View: 5967

The goal of machine learning is to program computers to use example data or past experience to solve a given problem. Many successful applications of machine learning exist already, including systems that analyze past sales data to predict customer behavior, optimize robot behavior so that a task can be completed using minimum resources, and extract knowledge from bioinformatics data. The second edition of Introduction to Machine Learning is a comprehensive textbook on the subject, covering a broad array of topics not usually included in introductory machine learning texts. In order to present a unified treatment of machine learning problems and solutions, it discusses many methods from different fields, including statistics, pattern recognition, neural networks, artificial intelligence, signal processing, control, and data mining. All learning algorithms are explained so that the student can easily move from the equations in the book to a computer program. The text covers such topics as supervised learning, Bayesian decision theory, parametric methods, multivariate methods, multilayer perceptrons, local models, hidden Markov models, assessing and comparing classification algorithms, and reinforcement learning. New to the second edition are chapters on kernel machines, graphical models, and Bayesian estimation; expanded coverage of statistical tests in a chapter on design and analysis of machine learning experiments; case studies available on the Web (with downloadable results for instructors); and many additional exercises. All chapters have been revised and updated. Introduction to Machine Learning can be used by advanced undergraduates and graduate students who have completed courses in computer programming, probability, calculus, and linear algebra. It will also be of interest to engineers in the field who are concerned with the application of machine learning methods.

The Semantic Web

6th International Semantic Web Conference, 2nd Asian Semantic Web Conference, ISWC 2007 + ASWC 2007, Busan, Korea, November 11-15, 2007, Proceedings

DOWNLOAD NOW »

Author: Karl Aberer,Key-Sun Choi,Dean Allemang,Natasha Noy,Kyung-Il Lee,Jennifer Golbeck,Lyndon Nixon,Peter Mika,Diana Maynard,Riichiro Mizoguchi,Philippe Cudré-Mauroux,Guus Schreiber

Publisher: Springer Science & Business Media

ISBN: 3540762973

Category: Computers

Page: 973

View: 6813

This book constitutes the refereed proceedings of the joint 6th International Semantic Web Conference, ISWC 2007, and the 2nd Asian Semantic Web Conference, ASWC 2007, held in Busan, Korea, in November 2007. The 50 revised full academic papers and 12 revised application papers presented together with 5 Semantic Web Challenge papers and 12 selected doctoral consortium articles were carefully reviewed and selected from a total of 257 submitted papers to the academic track and 29 to the applications track. The papers address all current issues in the field of the semantic Web, ranging from theoretical and foundational aspects to various applied topics such as management of semantic Web data, ontologies, semantic Web architecture, social semantic Web, as well as applications of the semantic Web. Short descriptions of the top five winning applications submitted to the Semantic Web Challenge competition conclude the volume.

Database Systems for Advanced Applications

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

DOWNLOAD NOW »

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

Publisher: Springer Science & Business Media

ISBN: 3642120970

Category: Computers

Page: 485

View: 1155

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.

Machine Learning and Its Applications

Advanced Lectures

DOWNLOAD NOW »

Author: Georgios Paliouras,Vangelis Karkaletsis,Constantine D. Spyropoulos

Publisher: Springer Science & Business Media

ISBN: 3540424903

Category: Computers

Page: 324

View: 4383

In recent years machine learning has made its way from artificial intelligence into areas of administration, commerce, and industry. Data mining is perhaps the most widely known demonstration of this migration, complemented by less publicized applications of machine learning like adaptive systems in industry, financial prediction, medical diagnosis and the construction of user profiles for Web browsers. This book presents the capabilities of machine learning methods and ideas on how these methods could be used to solve real-world problems. The first ten chapters assess the current state of the art of machine learning, from symbolic concept learning and conceptual clustering to case-based reasoning, neural networks, and genetic algorithms. The second part introduces the reader to innovative applications of ML techniques in fields such as data mining, knowledge discovery, human language technology, user modeling, data analysis, discovery science, agent technology, finance, etc.

The Semantic Web: Research and Applications

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

DOWNLOAD NOW »

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: 6151

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

DOWNLOAD NOW »

Author: Tatnall, Arthur

Publisher: IGI Global

ISBN: 1605669830

Category: Computers

Page: 2824

View: 5970

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.

Machine Learning

The New AI

DOWNLOAD NOW »

Author: Ethem Alpaydin

Publisher: MIT Press

ISBN: 0262529513

Category: Computers

Page: 224

View: 9326

A concise overview of machine learning -- computer programs that learn from data -- which underlies applications that include recommendation systems, face recognition, and driverless cars.