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PYTHON MACHINE LEARNING BLUEPRINTS: INTUITIVE

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    Sinopse

    An approachable guide to applying advanced machine learning methods to everyday problems About This Book Put machine learning principles into practice to solve real-world problems Get to grips with Python's impressive range of Machine Learning libraries and frameworks From retrieving data from APIs to cleaning and visualization, become more confident at tackling every stage of the data pipeline Who This Book Is For Python programmers and data scientists - put your skills to the test with this practical guide dedicated to real-world machine learning that makes a real impact. What You Will Learn Explore and use Python's impressive machine learning ecosystem Successfully evaluate and apply the most effective models to problems Learn the fundamentals of NLP - and put them into practice Visualize data for maximum impact and clarity Deploy machine learning models using third party APIs Get to grips with feature engineering In Detail Machine Learning is transforming the way we understand and interact with the world around us. But how much do you really understand it? How confident are you interacting with the tools and models that drive it? Python Machine Learning Blueprints puts your skills and knowledge to the test, guiding you through the development of some awesome machine learning applications and algorithms with real-world examples that demonstrate how to put concepts into practice. You'll learn how to use cluster techniques to discover bargain air fares, and apply linear regression to find yourself a cheap apartment – and much more. Everything you learn is backed by a real-world example, whether its data manipulation or statistical modelling. That way you're never left floundering in theory – you'll be simply collecting and analyzing data in a way that makes a real impact. Style and approach Packed with real-world projects, this book takes you beyond the theory to demonstrate how to apply machine learning techniques to real problems.

    Detalhes do Produto

      • Edição:  1
      • Ano de Edição: 2016
      • Ano:  2016
      • País de Produção: United Kingdom
      • Código de Barras:  2001009874423
      • ISBN:  9781784392239

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