Machine learning methods are now an important tool for scientists, researchers, engineers and students in a wide range of areas. This book is written for people who want to adopt and use the main tools of machine learning, but aren´t necessarily going to want to be machine learning researchers. Intended for students in final year undergraduate or first year graduate computer science programs in machine learning, this textbook is a machine learning toolkit. Applied Machine Learning covers many topics for people who want to use machine learning processes to get things done, with a strong emphasis on using existing tools and packages, rather than writing one´s own code. A companion to the author´s Probability and Statistics for Computer Science , this book picks up where the earlier book left off (but also supplies a summary of probability that the reader can use). Emphasizing the usefulness of standard machinery from applied statistics, this textbook gives an overview of the major applied areas in learning, including coverage of: - classification using standard machinery (naive bayes; nearest neighbor; SVM) - clustering and vector quantization (largely as in PSCS) - PCA (largely as in PSCS) - variants of PCA (NIPALS; latent semantic analysis; canonical correlation analysis) - linear regression (largely as in PSCS) - generalized linear models including logistic regression - model selection with Lasso, elasticnet - robustness and m-estimators - Markov chains and HMM´s (largely as in PSCS) - EM in fairly gory detail; long experience teaching this suggests one detailed example is required, which students hate; but once they´ve been through that, the next one is easy - simple graphical models (in the variational inference section) - classification with neural networks, with a particular emphasis on image classification - autoencoding with neural networks - structure learning
Learn to build robust software that more closely meets the customer´s needs through applying the concept of user stories. A clear explanation of the most agile means of gathering software requirements Thoroughly reviewed and eagerly anticipated by the agile software development community Allows the reader to save time and resources by gathering the proper requirements BEFORE coding begins The concept of user stories has its roots as one of the main tenets of Extreme Programming. In simple terms, user stories represent an effective means of gathering requirements from the customer (roughly akin to use cases). This book describes user stories and demonstrates how they can be used to properly plan, manage, and test software development projects. The book highlights both successful and unsuccessful implementations of the concept, and provides sets of questions and exercises that drive home its main points. After absorbing the lessons in this book, readers will be able to introduce user stories in their organizations as an effective means of determining precisely what is required of a software application. Product Description Thoroughly reviewed and eagerly anticipated by the agile community, User Stories Applied offers a requirements process that saves time, eliminates rework, and leads directly to better software. The best way to build software that meets users´ needs is to begin with ´´user stories´´: simple, clear, brief descriptions of functionality that will be valuable to real users. In User Stories Applied , Mike Cohn provides you with a front-to-back blueprint for writing these user stories and weaving them into your development lifecycle. You´ll learn what makes a great user story, and what makes a bad one. You´ll discover practical ways to gather user stories, even when you can´t speak with your users. Then, once you´ve compiled your user stories, Cohn shows how to organize them, prioritize them, and use them for planning, management, and testing. User role modeling: understanding what users have in common, and where they differ Gathering stories: user interviewing, questionnaires, observation, and workshops Working with managers, trainers, salespeople and other ´´proxies´´ Writing user stories for acceptance testing Using stories to prioritize, set schedules, and estimate release costs Includes end-of-chapter practice questions and exercises User Stories Applied will be invaluable to every software developer, tester, analyst, and manager working with any agile method: XP, Scrum... or even your own home-grown approach. Features + Benefits Learn to build robust software that more closely meets the customer´s needs through applying the concept of user stories. ° A clear explanation of the most agile means of gathering software requirements ° Thoroughly reviewed and eagerly anticipated by the agile software development community ° Allows the reader to save time and resources by gathering the proper requirements BEFORE coding begins Backcover Agile requirements: discovering what your users really want. With this book, you will learn to: Flexible, quick and practical requirements that work Save time and develop better software that meets users´ needs Gathering user stories -- even when you can´t talk to users How user stories work, and how they differ from use cases, scenarios, and traditional requirements Leveraging user stories as part of planning, scheduling, estimating, and testing Ideal for Extreme Programming, Scrum, or any other agile methodology ---------------------------------------------------------------------------------------------------------- Thoroughly reviewed and eagerly anticipated by the agile community, User Stories Applied offers a requirements process that saves time, eliminates rework, and leads directly to better software. The best way to build software that meets users´ needs is to begin with user stories: simple, clear, brief descriptions of functionality that will be valuable to real users. In User Stories Applied , Mike Cohn provides you with a front-to-back blueprint for writing these user stories and weaving them into your development lifecycle. You´ll learn what makes a great user story, and what makes a bad one. You´ll discover practical ways to gather user stories, even when you can´t speak with your users. Then, once you´ve compiled your user stories, Cohn shows how to organize them, prioritize them, and use them for planning, management, and testing. User role modeling:
This book has two main goals: to define data science through the work of data scientists and their results, namely data products, while simultaneously providing the reader with relevant lessons learned from applied data science projects at the intersection of academia and industry. As such, it is not a replacement for a classical textbook (i.e., it does not elaborate on fundamentals of methods and principles described elsewhere), but systematically highlights the connection between theory, on the one hand, and its application in specific use cases, on the other. With these goals in mind, the book is divided into three parts: Part I pays tribute to the interdisciplinary nature of data science and provides a common understanding of data science terminology for readers with different backgrounds. These six chapters are geared towards drawing a consistent picture of data science and were predominantly written by the editors themselves. Part II then broadens the spectrum by presenting views and insights from diverse authors - some from academia and some from industry, ranging from financial to health and from manufacturing to e-commerce. Each of these chapters describes a fundamental principle, method or tool in data science by analyzing specific use cases and drawing concrete conclusions from them. The case studies presented, and the methods and tools applied, represent the nuts and bolts of data science. Finally, Part III was again written from the perspective of the editors and summarizes the lessons learned that have been distilled from the case studies in Part II. The section can be viewed as a meta-study on data science across a broad range of domains, viewpoints and fields. Moreover, it provides answers to the question of what the mission-critical factors for success in different data science undertakings are. The book targets professionals as well as students of data science: first, practicing data scientists in industry and academia who want to broaden their scope and expand their knowledge by drawing on the authors´ combined experience. Second, decision makers in businesses who face the challenge of creating or implementing a data-driven strategy and who want to learn from success stories spanning a range of industries. Third, students of data science who want to understand both the theoretical and practical aspects of data science, vetted by real-world case studies at the intersection of academia and industry.
A valuable reference for the novice as well as for the expert who needs a wider scope of coverage within the area of cryptography, this book provides easy and rapid access of information and includes more than 200 algorithms and protocols; more than 200 tables and figures; more than 1,000 numbered definitions, facts, examples, notes, and remarks; and over 1,250 significant references, including brief comments on each paper.
Learn to harness the power of AI for natural language processing, performing tasks such as spell check, text summarization, document classification, and natural language generation. Along the way, you will learn the skills to implement these methods in larger infrastructures to replace existing code or create new algorithms. Applied Natural Language Processing with Python starts with reviewing the necessary machine learning concepts before moving onto discussing various NLP problems. After reading this book, you will have the skills to apply these concepts in your own professional environment. What You Will Learn Utilize various machine learning and natural language processing libraries such as TensorFlow, Keras, NLTK, and Gensim Manipulate and preprocess raw text data in formats such as .txt and .pdf Strengthen your skills in data science by learning both the theory and the application of various algorithms Who This Book Is For You should be at least a beginner in ML to get the most out of this text, but you needn´t feel that you need be an expert to understand the content.
´´The book focuses on how machine learning and Internet of Things (IoT) has empowered the advancement of information driven arrangements including key concepts and advancements. Divided into sections such as machine learning, security, IoT and data mining, the concepts are explained with practical implementation including results´´--
Publisher´s Note: Products purchased from Third Party sellers are not guaranteed by the publisher for quality, authenticity, or access to any online entitlements included with the product. Cutting-edge techniques from leading Oracle security experts This Oracle Press guide demonstrates practical applications of the most compelling methods for developing secure Oracle database and middleware environments. You will find full coverage of the latest and most popular Oracle products, including Oracle Database and Audit Vaults, Oracle Application Express, and secure Business Intelligence applications. Applied Oracle Security demonstrates how to build and assemble the various Oracle technologies required to create the sophisticated applications demanded in today´s IT world. Most technical references only discuss a single product or product suite. As such, there is no roadmap to explain how to get one product, product-family, or suite to work with another. This book fills that void with respect to Oracle Middleware and Database products and the area of security.