Practical Machine Learning
By Sunila Gollapudi
Publisher: Packt Publishing
Final Release Date: January 2016
Pages: 468

Tackle the real-world complexities of modern machine learning with innovative, cutting-edge, techniques

About This Book

  • Fully-coded working examples using a wide range of machine learning libraries and tools, including Python, R, Julia, and Spark
  • Comprehensive practical solutions taking you into the future of machine learning
  • Go a step further and integrate your machine learning projects with Hadoop

Who 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 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 Learn

  • Implement a wide range of algorithms and techniques for tackling complex data
  • Get to grips with some of the most powerful languages in data science, including R, Python, and Julia
  • Harness the capabilities of Spark and Hadoop to manage and process data successfully
  • Apply the appropriate machine learning technique to address real-world problems
  • Get acquainted with Deep learning and find out how neural networks are being used at the cutting-edge of machine learning
  • Explore the future of machine learning and dive deeper into polyglot persistence, semantic data, and more

In Detail

Finding 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 including Spark, Hadoop, and Mahout. Practical Machine Learning is an essential resource for the modern data scientists who want to get to grips with its real-world application.

With this book, you will not only learn the fundamentals of machine learning but dive deep into the complexities of real world data before moving on to using Hadoop and its wider ecosystem of tools to process and manage your structured and unstructured data.

You will explore different machine learning techniques for both supervised and unsupervised learning; from decision trees to Naive Bayes classifiers and linear and clustering methods, you will learn strategies for a truly advanced approach to the statistical analysis of data. The book also explores the cutting-edge advancements 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.

Style and approach

A practical data science tutorial designed to give you an insight into the practical application of machine learning, this book takes you through complex concepts and tasks in an accessible way. Featuring information on a wide range of data science techniques, Practical Machine Learning is a comprehensive data science resource.

Product Details
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oreillyPractical Machine Learning
 
4.2

(based on 5 reviews)

Ratings Distribution

  • 5 Stars

     

    (3)

  • 4 Stars

     

    (1)

  • 3 Stars

     

    (0)

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    (1)

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80%

of respondents would recommend this to a friend.

Pros

  • Well-written (3)

Cons

No Cons

Best Uses

  • Intermediate (4)
    • Reviewer Profile:
    • Developer (3)

Reviewed by 5 customers

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5.0

Very crisp and concise

By Revanth R

from Bangalore

About Me Designer

Pros

  • Concise
  • Well-written

Cons

    Best Uses

    • Expert
    • Intermediate

    Comments about oreilly Practical Machine Learning:

    The only book that not only introduced Machine Learning in a down-to-earth language, but also explained the concept behind each algorithm. I am currently using the bundled code base and will post detailed feedback on the code as well

    (1 of 1 customers found this review helpful)

     
    5.0

    Good read and a lot of detail

    By Vincent Dennis

    from Spain

    About Me Developer

    Verified Reviewer

    Pros

    • Concise
    • Easy to understand

    Cons

      Best Uses

      • Intermediate
      • Student

      Comments about oreilly Practical Machine Learning:

      I have read a few books on this topic and really like the way the book is structured and the way the readers are transitioned from the concept to implementation. I think the technology stack covered is extensive and one of the few books that covered 5 different machine learning frameworks and tools. It would be good to see Python implementations directly as against the Scikit framework. The python focus seemed less overall. That said, I have tried Julia for the first time using this book and think it is definitely one of the best places to start.

      (1 of 1 customers found this review helpful)

       
      4.0

      Excellent coverage on the Machine learning algorithm scope

      By Randy Mayson

      from Korea

      About Me Designer, Developer

      Pros

      • Easy to understand
      • Well-written

      Cons

        Best Uses

        • Intermediate

        Comments about oreilly Practical Machine Learning:

        This book has covered the algorithm landscape very well with some simple mindmaps. There are extensive code samples provided as well. While I have tried R and Spark, I think it would be good to add a few sections in the chapters on how the libraries are used in the code samples. A little more information other than the comments in the code would be helpful. The overall structure of the code samples is simple and nice.

        (1 of 1 customers found this review helpful)

         
        5.0

        Very insightful

        By Racheal

        from New York, NY

        Verified Reviewer

        Pros

        • Helpful examples
        • Well-written

        Cons

          Best Uses

          • Intermediate

          Comments about oreilly Practical Machine Learning:

          This book is very ambitious and covers all the topics under machine learning. I was able to use the spark and R frameworks for my requirements and found it good enough for my work on hand

          (0 of 2 customers found this review helpful)

           
          2.0

          Not that practical

          By LittleDev

          from The Netherlands

          About Me Developer

          Pros

            Cons

            • Lacks Depth
            • Not comprehensive enough

            Best Uses

            • Novice

            Comments about oreilly Practical Machine Learning:

            Although the general structure of the book is fine, it lacks depth in many aspects:
            - It glances over most of the math and doesn't go out of it's way to give a more detailed description why the math is as it is.
            - The practical part about the book are the references to the code examples. It doesn't guide you through the examples in the book itself. Nor does it guide the reader though the algorithms in depth with examples.

            What the book does to good is give applications for the algorithms and describe multiple different algorithms and their uses. And it describes the different learning algorithms available.

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