Practical machine learning : tackle the real-world complexities of modern machine learning with innovative and cutting-edge techniques

Description

About 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 For This book has been created for data scientists who want to see Machine learning in action and explore its real-world applications. 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 Mahout used in conjunction with Hadoop to manage and process data successfullyApply the appropriate Machine learning technique to address a real-world problemGet 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 Detail This book explores an extensive range of Machine learning techniques, uncovering hidden tips and tricks for several types of data using practical 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. We will cover the leading data science languages, Python and R, and the underrated but powerful Julia, as well as a range of big data platforms including Spark, Hadoop, and Mahout. Practical Machine Learning is an essential resource for modern data scientists who want to get to grips with Machine learning's real-world application. The book also explores cutting-edge advances in Machine learning, with worked examples and guidance on Deep learning and Reinforcement learning, providing you with practical demonstrations and samples that help take the theory–and mystery–out of even the most advanced Machine learning methodologies.