Android Apps for Absolute Beginners

Android Apps for Absolute Beginners Author Wallace Jackson
ISBN-10 9781430247883
Year 2012-11-28
Pages 400
Language en
Publisher Apress
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Anybody can start building simple apps for the Android platform, and this book will show you how! Recently updated to include Android Jelly Bean, Android Apps for Absolute Beginners, Second Edition takes you through the process of getting your first Android apps up and running using plain English and practical examples. This book cuts through the fog of jargon and mystery that surrounds Android apps development, and gives you simple, step-by-step instructions to get you started. Teaches Android application development in language anyone can understand, giving you the best possible start in Android development Provides simple, step-by-step examples that make learning easy, allowing you to pick up the concepts without fuss Offers clear code descriptions and layout so that you can get your apps running as soon as possible This book is Android Jelly Bean compliant, but is backwards compatible to most of the previous Android releases. What you’ll learn Get yourself and your computer set up for Android apps development Use the Eclipse programming environment to make your Android development efficient and straightforward Follow steps in plain English to build simple apps and get them working immediately Style your application so that it appeals to potential users Make use of the Android’s touch screen Use shortcuts and cheat sheets to create apps the easy way Use the basics of Java and XML to move onto more advanced apps Who this book is for If you have a great idea for an Android app, but have never programmed before, then this book is for you. You don’t need to have any previous computer programming skills—as long as you have a desire to learn, and you know which end of the mouse is which, the world of Android apps development awaits!

Machine Learning For Dummies

Machine Learning For Dummies Author John Paul Mueller
ISBN-10 9781119245513
Year 2016-05-31
Pages 432
Language en
Publisher John Wiley & Sons
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Machine learning is an exciting new way to use computers to perform tasks that require the ability to learn from experience. In order to make machine learning a reality, programmers rely on special languages, such as Python and R, and new types of tools. Machine Learning For Dummies helps the reader understand what machine learning is, when it can help perform a new class of computer tasks, and how to implement machine learning using Python and R, along with the required tools. Unlike most machine learning books, Machine Learning For Dummies does not assume that the reader has years of experience using programming languages. This book provides the much-needed entry point for people who really could use machine learning to accomplish practical tasks, but dont necessarily have the skills required to use on more advanced books. This book will cover the entry level materials required to get readers up and running faster, how to perform practical tasks, how to perform useful work without getting overly involved in the underlying math principles, fun ways to play with new tools and learn as a result, and how to separate facts from myth to see how machine learning is useful in todays world. --

Android Apps for Absolute Beginners

Android Apps for Absolute Beginners Author Wallace Jackson
ISBN-10 9781484222683
Year 2017-03-14
Pages 484
Language en
Publisher Apress
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Get your first Android apps up and running with the help of plain English and practical examples. If you have a great idea for an Android app, but have never programmed before, then this book is for you. Android Apps for Absolute Beginners cuts through the fog of jargon and mystery that surrounds Android app development, and gives you simple, step-by-step instructions to get you started. This book teaches Android application development in language anyone can understand, giving you the best possible start in Android development. It provides clean, straightforward examples that make learning easy, allowing you to pick up the concepts without fuss. It offers clear code descriptions and layout so that you can get your apps running as soon as possible Although this book covers what's new in Android 7, it is also backwards compatible to cover some of the previous Android releases. What You'll Learn Download, install, and configure the latest software needed for Android app development Work efficiently using an integrated development environment (IDE) Build useful, attractive applications and get them working immediately Create apps with ease using XML markup and drag-and-drop graphical layout editors Use new media and graphics to skin your app so that it has maximum appeal Create advanced apps combining XML, Java and new media content Who This Book Is For If you have a great idea for an Android app, but have never programmed before, then this book is for you. You don’t need to have any previous computer programming skills — as long as you have a desire to learn and you know which end of the mouse is which, the world of Android apps development awaits.

Bayesian Reasoning and Machine Learning

Bayesian Reasoning and Machine Learning Author David Barber
ISBN-10 9780521518147
Year 2012-02-02
Pages 697
Language en
Publisher Cambridge University Press
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A practical introduction perfect for final-year undergraduate and graduate students without a solid background in linear algebra and calculus.

Introduction to Machine Learning

Introduction to Machine Learning Author Ethem Alpaydin
ISBN-10 9780262028189
Year 2014-08-29
Pages 640
Language en
Publisher MIT Press
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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. 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. Subjects include supervised learning; Bayesian decision theory; parametric, semi-parametric, and nonparametric methods; multivariate analysis; hidden Markov models; reinforcement learning; kernel machines; graphical models; Bayesian estimation; and statistical testing.Machine learning is rapidly becoming a skill that computer science students must master before graduation. The third edition of Introduction to Machine Learning reflects this shift, with added support for beginners, including selected solutions for exercises and additional example data sets (with code available online). Other substantial changes include discussions of outlier detection; ranking algorithms for perceptrons and support vector machines; matrix decomposition and spectral methods; distance estimation; new kernel algorithms; deep learning in multilayered perceptrons; and the nonparametric approach to Bayesian methods. All learning algorithms are explained so that students can easily move from the equations in the book to a computer program. The book can be used by both advanced undergraduates and graduate students. It will also be of interest to professionals who are concerned with the application of machine learning methods.

Understanding Machine Learning

Understanding Machine Learning Author Shai Shalev-Shwartz
ISBN-10 9781107057135
Year 2014-05-19
Pages 409
Language en
Publisher Cambridge University Press
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Introduces machine learning and its algorithmic paradigms, explaining the principles behind automated learning approaches and the considerations underlying their usage.

Introduction to Machine Learning with Python

Introduction to Machine Learning with Python Author Andreas C. Müller
ISBN-10 9781449369903
Year 2016-09-26
Pages 394
Language en
Publisher "O'Reilly Media, Inc."
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Machine learning has become an integral part of many commercial applications and research projects, but this field is not exclusive to large companies with extensive research teams. If you use Python, even as a beginner, this book will teach you practical ways to build your own machine learning solutions. With all the data available today, machine learning applications are limited only by your imagination. You’ll learn the steps necessary to create a successful machine-learning application with Python and the scikit-learn library. Authors Andreas Müller and Sarah Guido focus on the practical aspects of using machine learning algorithms, rather than the math behind them. Familiarity with the NumPy and matplotlib libraries will help you get even more from this book. With this book, you’ll learn: Fundamental concepts and applications of machine learning Advantages and shortcomings of widely used machine learning algorithms How to represent data processed by machine learning, including which data aspects to focus on Advanced methods for model evaluation and parameter tuning The concept of pipelines for chaining models and encapsulating your workflow Methods for working with text data, including text-specific processing techniques Suggestions for improving your machine learning and data science skills

Machine Learning

Machine Learning Author Stephen Marsland
ISBN-10 1420067192
Year 2011-03-23
Pages 406
Language en
Publisher CRC Press
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Traditional books on machine learning can be divided into two groups — those aimed at advanced undergraduates or early postgraduates with reasonable mathematical knowledge and those that are primers on how to code algorithms. The field is ready for a text that not only demonstrates how to use the algorithms that make up machine learning methods, but also provides the background needed to understand how and why these algorithms work. Machine Learning: An Algorithmic Perspective is that text. Theory Backed up by Practical Examples The book covers neural networks, graphical models, reinforcement learning, evolutionary algorithms, dimensionality reduction methods, and the important area of optimization. It treads the fine line between adequate academic rigor and overwhelming students with equations and mathematical concepts. The author addresses the topics in a practical way while providing complete information and references where other expositions can be found. He includes examples based on widely available datasets and practical and theoretical problems to test understanding and application of the material. The book describes algorithms with code examples backed up by a website that provides working implementations in Python. The author uses data from a variety of applications to demonstrate the methods and includes practical problems for students to solve. Highlights a Range of Disciplines and Applications Drawing from computer science, statistics, mathematics, and engineering, the multidisciplinary nature of machine learning is underscored by its applicability to areas ranging from finance to biology and medicine to physics and chemistry. Written in an easily accessible style, this book bridges the gaps between disciplines, providing the ideal blend of theory and practical, applicable knowledge.

RapidMiner

RapidMiner Author Markus Hofmann
ISBN-10 9781482205503
Year 2016-04-19
Pages 525
Language en
Publisher CRC Press
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Powerful, Flexible Tools for a Data-Driven World As the data deluge continues in today’s world, the need to master data mining, predictive analytics, and business analytics has never been greater. These techniques and tools provide unprecedented insights into data, enabling better decision making and forecasting, and ultimately the solution of increasingly complex problems. Learn from the Creators of the RapidMiner Software Written by leaders in the data mining community, including the developers of the RapidMiner software, RapidMiner: Data Mining Use Cases and Business Analytics Applications provides an in-depth introduction to the application of data mining and business analytics techniques and tools in scientific research, medicine, industry, commerce, and diverse other sectors. It presents the most powerful and flexible open source software solutions: RapidMiner and RapidAnalytics. The software and their extensions can be freely downloaded at www.RapidMiner.com. Understand Each Stage of the Data Mining Process The book and software tools cover all relevant steps of the data mining process, from data loading, transformation, integration, aggregation, and visualization to automated feature selection, automated parameter and process optimization, and integration with other tools, such as R packages or your IT infrastructure via web services. The book and software also extensively discuss the analysis of unstructured data, including text and image mining. Easily Implement Analytics Approaches Using RapidMiner and RapidAnalytics Each chapter describes an application, how to approach it with data mining methods, and how to implement it with RapidMiner and RapidAnalytics. These application-oriented chapters give you not only the necessary analytics to solve problems and tasks, but also reproducible, step-by-step descriptions of using RapidMiner and RapidAnalytics. The case studies serve as blueprints for your own data mining applications, enabling you to effectively solve similar problems.

Artificial Intelligence Simplified

Artificial Intelligence Simplified Author Binto George
ISBN-10 9781944708023
Year 2016-01-08
Pages 138
Language en
Publisher CSTrends LLP
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The book introduces key Artificial Intelligence (AI) concepts in an easy-to-read format with examples and illustrations. A complex, long, overly mathematical textbook does not always serve the purpose of conveying the basic AI concepts to most people. Someone with basic knowledge in Computer Science can have a quick overview of AI (heuristic searches, genetic algorithms, expert systems, game trees, fuzzy expert systems, natural language processing, super intelligence, etc.) with everyday examples. If you are taking a basic AI course and find the traditional AI textbooks intimidating, you may choose this as a “bridge” book, or as an introductory textbook. For students, there is a lower priced edition (ISBN 978-1944708016) of the same book. Published by CSTrends LLP.

Machine Learning

Machine Learning Author Kevin P. Murphy
ISBN-10 9780262018029
Year 2012-08-24
Pages 1067
Language en
Publisher MIT Press
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A comprehensive introduction to machine learning that uses probabilistic models and inference as a unifying approach.

XML

XML Author Kevin Howard Goldberg
ISBN-10 0132104318
Year 2010-04-26
Pages 240
Language en
Publisher Peachpit Press
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What is XML? XML, or eXtensible Markup Language, is a specification for storing information. It is also a specification for describing the structure of that information. And while XML is a markup language (just like HTML), XML has no tags of its own. It allows the person writing the XML to create whatever tags they need. The only condition is that these newly created tags adhere to the rules of the XML specification. In the seven years since the first edition of “XML: Visual QuickStart Guide” was published, XML has taken its place next to HTML as a foundational language on the Internet. XML has become a very popular method for storing data and the most popular method for transmitting data between all sorts of systems and applications. The reason being, where HTML was designed to display information, XML was designed to manage it. “XML: Visual QuickStart Guide, 2nd Edition” is divided into seven parts. Each part contains one or more chapters with step-by-step instructions that explain how to perform XML-related tasks. Wherever possible, examples of the concepts being discussed are displayed, and the parts of the examples on which to focus are highlighted. The order of the book is intentionally designed to be an introduction to the fundamentals of XML, followed by discussions of related XML technologies.

Machine Learning

Machine Learning Author Ethem Alpaydin
ISBN-10 9780262529518
Year 2016-10-07
Pages 224
Language en
Publisher MIT Press
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A concise overview of machine learning -- computer programs that learn from data -- which underlies applications that include recommendation systems, face recognition, and driverless cars.

An Introduction to Parallel Programming

An Introduction to Parallel Programming Author Peter Pacheco
ISBN-10 0080921442
Year 2011-02-17
Pages 392
Language en
Publisher Elsevier
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An Introduction to Parallel Programming is the first undergraduate text to directly address compiling and running parallel programs on the new multi-core and cluster architecture. It explains how to design, debug, and evaluate the performance of distributed and shared-memory programs. The author Peter Pacheco uses a tutorial approach to show students how to develop effective parallel programs with MPI, Pthreads, and OpenMP, starting with small programming examples and building progressively to more challenging ones. The text is written for students in undergraduate parallel programming or parallel computing courses designed for the computer science major or as a service course to other departments; professionals with no background in parallel computing. Takes a tutorial approach, starting with small programming examples and building progressively to more challenging examples Focuses on designing, debugging and evaluating the performance of distributed and shared-memory programs Explains how to develop parallel programs using MPI, Pthreads, and OpenMP programming models