Practical Machine Learning With Spark
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Practical Machine Learning With Spark
$14.72
Sale price
$14.72
Regular price
$14.72
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Description
Explore The Cosmic Secrets Of Distributed Processing For Deep Learning Applications Description
This Book Provides The Reader With An Up-To-Date Explanation Of Machine Learning And An In-Depth, Comprehensive, And Straightforward Understanding Of The Architectural Techniques Used To Evaluate And Anticipate The Futuristic Insights Of Data Using Apache Spark.
The Book Walks Readers By Setting Up Hadoop And Spark Installations On-Premises, Docker, And Aws. Readers Will Learn About Spark Mlib And How To Utilize It In Supervised And Unsupervised Machine Learning Scenarios. With The Help Of Spark, Some Of The Most Prominent Technologies, Such As Natural Language Processing And Computer Vision, Are Evaluated And Demonstrated In A Realistic Setting. Using The Capabilities Of Apache Spark, This Book Discusses The Fundamental Components That Underlie Each Of These Natural Language Processing, Computer Vision, And Machine Learning Technologies.
What You Will Learn
Learn How To Get Started With Machine Learning Projects Using Spark.
Witness How To Use Spark MlibS Design For Machine Learning And Deep Learning Operations.
Use Spark In Tasks Involving Nlp, Unsupervised Learning, And Computer Vision.Who This Book Is For
This Book Is Valuable For Data Engineers, Machine Learning Engineers, Data Scientists, Data Architects, Business Analysts, And Technical Consultants Worldwide. It Would Be Beneficial To Have Some Familiarity With The Fundamentals Of Hadoop And Python.Table Of Contents
1. Introduction To Machine Learning
2. Apache Spark Environment Setup And Configuration
3. Apache Spark
4. Apache Spark Mllib
5. Supervised Learning With Spark
6. Un-Supervised Learning With Apache Spark
7. Natural Language Processing With Apache Spark
8. Recommendation Engine With Spark
9. Deep Learning With Spark
10. Computer Vision With Apache Spark
This Book Provides The Reader With An Up-To-Date Explanation Of Machine Learning And An In-Depth, Comprehensive, And Straightforward Understanding Of The Architectural Techniques Used To Evaluate And Anticipate The Futuristic Insights Of Data Using Apache Spark.
The Book Walks Readers By Setting Up Hadoop And Spark Installations On-Premises, Docker, And Aws. Readers Will Learn About Spark Mlib And How To Utilize It In Supervised And Unsupervised Machine Learning Scenarios. With The Help Of Spark, Some Of The Most Prominent Technologies, Such As Natural Language Processing And Computer Vision, Are Evaluated And Demonstrated In A Realistic Setting. Using The Capabilities Of Apache Spark, This Book Discusses The Fundamental Components That Underlie Each Of These Natural Language Processing, Computer Vision, And Machine Learning Technologies.
What You Will Learn
Learn How To Get Started With Machine Learning Projects Using Spark.
Witness How To Use Spark MlibS Design For Machine Learning And Deep Learning Operations.
Use Spark In Tasks Involving Nlp, Unsupervised Learning, And Computer Vision.Who This Book Is For
This Book Is Valuable For Data Engineers, Machine Learning Engineers, Data Scientists, Data Architects, Business Analysts, And Technical Consultants Worldwide. It Would Be Beneficial To Have Some Familiarity With The Fundamentals Of Hadoop And Python.Table Of Contents
1. Introduction To Machine Learning
2. Apache Spark Environment Setup And Configuration
3. Apache Spark
4. Apache Spark Mllib
5. Supervised Learning With Spark
6. Un-Supervised Learning With Apache Spark
7. Natural Language Processing With Apache Spark
8. Recommendation Engine With Spark
9. Deep Learning With Spark
10. Computer Vision With Apache Spark
Product Notice
This book is sold in used condition unless explicitly stated as new.
Condition is graded and described accurately. Some books may contain
previous owner's markings, highlights, or inscriptions. This product
may contain chemicals known to the State of California to cause cancer
or reproductive harm. For more information visit
www.P65Warnings.ca.gov
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