Tackle the real-world complexities of modern machine learning with innovative, cutting-edge, techniquesAbout This BookFully-coded working examples using a wide range of machine learning libraries and tools, including Python, R, Julia, and SparkComprehensive practical solutions taking you into the future of machine learningGo a step further and integrate your machine learning projects with HadoopWho This Book Is ForThis book has been created for data scientists who want to see machine learning in action and explore its real-world application. With guidance on everything from the fundamentals of machine learning and predictive analytics to the latest innovations set to lead the big data revolution into the future, this is an unmissable resource for anyone dedicated to tackling current big data challenges. Knowledge of programming (Python and R) and mathematics is advisable if you want to get started immediately.What You Will LearnImplement a wide range of algorithms and techniques for tackling complex dataGet to grips with some of the most powerful languages in data science, including R, Python, and JuliaHarness the capabilities of Spark and Hadoop to manage and process data successfullyApply the appropriate machine learning technique to address real-world problemsGet acquainted with Deep learning and find out how neural networks are being used at the cutting-edge of machine learningExplore the future of machine learning and dive deeper into polyglot persistence, semantic data, and moreIn DetailFinding meaning in increasingly larger and more complex datasets is a growing demand of the modern world. Machine learning and predictive analytics have become the most important approaches to uncover data gold mines. Machine learning uses complex algorithms to make improved predictions of outcomes based on historical patterns and the behaviour of data sets. Machine learning can deliver dynamic insights into trends, patterns, and relationships within data, immensely valuable to business growth and development.This book explores an extensive range of machine learning techniques uncovering hidden tricks and tips for several types of data using practical and real-world examples. While machine learning can be highly theoretical, this book offers a refreshing hands-on approach without losing sight of the underlying principles. Inside, a full exploration of the various algorithms gives you high-quality guidance so you can begin to see just how effective machine learning is at tackling contemporary challenges of big data.This is the only book you need to implement a whole suite of open source tools, frameworks, and languages in machine learning. We will cover the leading data science languages, Python and R, and the underrated but powerful Julia, as well as a range of other big data platforms in