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Key FeaturesFully-coded working examples using a spread of machine learning libraries and tools, along with Python, R, Julia, and SparkComprehensive smart choices taking you into the future of machine learningGo a step further and mix your machine learning duties with Hadoop
Large data is true at the center of fashionable enterprise intelligence — the demand for finding actionable which suggests in increasingly more greater and additional difficult datasets is a reality of the fashionable world. Machine learning and predictive analytics have flip into the most crucial approaches to meeting this demand. By "educating" machines by method of algorithms to "interpret" data and predict outcomes, machine learning can ship dynamic insights into tendencies, patterns, and relationships inside data, which can be immensely priceless.
This book helps you uncover an in depth range of machine learning strategies for numerous varieties of data via the use of smart and precise-world examples. Whereas machine learning could possibly be densely theoretical, this data science book has been created to give you high-high high quality steering so you will give you the chance to begin to see merely how environment friendly machine learning is at tackling the trendy challenges of big data. Overlaying the two essential data science languages, R and Python, and the underrated nevertheless extremely efficient Julia, as well as to a spread of totally different large data tools along with Mahout and Spark, Practical Machine Learning is a vital helpful useful resource for data scientists who want to get to grips with its precise-world software.
With this book, you will research the fundamentals of machine learning sooner than shifting on to how to use Hadoop and its wider ecosystem of tools to course of and deal with your data. You will dive into completely totally different machine learning strategies for every supervised and unsupervised learning; from willpower timber to Naïve Bayes classifiers and linear and clustering methods, you will research strategies for a really superior technique to the statistical analysis of data. The book moreover explores the chopping-edge developments in machine learning, with information and steering on deep learning and reinforcement learning, providing you with precise-world demonstrations and samples that help take the idea–and thriller–out of even the most superior machine learning methodologies.
What you will LearnImplement a spread of algorithms and strategies for tackling difficult dataGet to grips with some of the strongest languages in data science, along with R, Python, and JuliaHarness the capabilities of Spark and Hadoop to deal with and course of data successfullyApply the relevant machine learning technique to address precise-world problemsGet acquainted with Deep learning and find out how neural networks are getting used at the chopping-edge of machine learningExplore the future of machine learning and dive deeper into polyglot persistence, semantic data, and additional
About the Author
Sunila Gollapudi works as the vice chairman of technology at Broadridge Financial Solutions (India) Pvt. Ltd., a subsidiary of the US-based Broadridge Financial Solutions Inc (BR). She has over a decade of experience in creating, designing, and architecting data-pushed choices collectively together with her longest and strongest stint being in the BFSI space. In her current place at Broadridge India, she’s chargeable for driving construction COE and performs a key place in big data and data science initiatives.
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