Without overdosing you on academic theory and complex mathematics, it introduces the day-to-day practice of machine learning, preparing you to successfully build and deploy powerful ML systems.Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.About the TechnologyMachine learning systems help you find valuable insights and patterns in data, which you'd never recognize with traditional methods. In the real world, ML techniques give you a way to identify trends, forecast behavior, and make fact-based recommendations. It's a hot and growing field, and up-to-speed ML developers are in demand.About the BookReal-World Machine Learning will teach you the concepts and techniques you need to be a successful machine learning practitioner without overdosing you on abstract theory and complex mathematics. By working through immediately relevant examples in Python, you'll build skills in data acquisition and modeling, classification, and regression. You'll also explore the most important tasks like model validation, optimization, scalability, and real-time streaming. When you're done, you'll be ready to successfully build, deploy, and maintain your own powerful ML systems.What's InsidePredicting future behaviorPerformance evaluation and optimizationAnalyzing sentiment and making recommendationsAbout the ReaderNo prior machine learning experience assumed. Readers should know Python.About the AuthorsHenrik Brink, Joseph Richards and Mark Fetherolf are experienced data scientists engaged in the daily practice of machine learning.Table of ContentsTHE MACHINE-LEARNING WORKFLOWWhat is machine learning?Real-world dataModeling and predictionModel evaluation and optimizationBasic feature engineeringPRACTICAL APPLICATIONExample: NYC taxi dataAdvanced feature engineeringAdvanced NLP example: movie review sentimentScaling machine-learning workflowsExample: digital display advertising

    Practical exercises show how to critically appraise sampling and measurement techniques, evaluate results, and conduct a power analysis for a study. Written by nursing statistics experts Susan Grove and Daisha Cipher, this is the only statistics workbook for nursing to include research examples from both nursing and medical literature for a complete perspective on health sciences research. Comprehensive coverage includes exercises that address all common techniques of sampling, measurement, and statistical analysis that you are likely to see in nursing and medical literature. A literature-based approach incorporates a relevant research article into each exercise/chapter, with key excerpts. 45 sampling, measurement, and statistical analysis exercises provide a practical review of both basic and advanced techniques, and prepare you to apply statistics to nursing practice. Consistent format for all chapters facilitates quick review and easier learning, covering the statistical technique in review, results from a research article, and study questions.  Study questions in each chapter help you apply concepts to clinical practice. Questions to Be Graded in each chapter may be completed and submitted online, to assess your mastery of key statistical techniques. A concise index makes it easy to locate information quickly. NEW examples show the latest, high-quality research studies.  NEW! Expanded coverage helps undergraduate students apply the information learned in statistics and research courses, serves as a refresher/review for graduate students, and also helps in critically appraising studies to determine whether their findings may be used in evidence-based practice. NEW! Understanding Statistical Methods section includes exercises to help in understanding the levels of measurement (nominal, ordinal, interval, and ratio) and in appraising the samples and measurement methods in studies. NEW! Conducting and Interpreting Statistical Analyses section includes exercises to help in understanding the power analysis and how to conduct a power analysis for a study, showing how to determine the most appropriate statistical method(s) for analyzing data for a class project, for a clinical agency project, or for an actual research study. NEW! Answers to study questions are located in the back of the book.

    It covers the theory of Social Architecture, and the tools you need to build a community. It explains the ZeroMQ community in detail, including its collaboration process (C4). This is a powerful book for anyone building an Open Source community, or an on-line community in other areas.

    This portable learning tool has been designed as a one-stop reference for students to understand and master the subjects by themselves.