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{ "item_title" : "Real-World Machine Learning", "item_author" : [" Henrik Brink", "Joseph Richards", "Mark Fetherolf "], "item_description" : "Summary Real-World Machine Learning is a practical guide designed to teach working developers the art of ML project execution. Without overdosing you on academic theory and complex mathematics, it introduces the day-to-day practice of machine learning, preparing you to successfully build and deploy powerful ML systems. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology Machine learning systems help you find valuable insights and patterns in data, which you'd never recognize with traditional methods. In the real world, ML techniques give you a way to identify trends, forecast behavior, and make fact-based recommendations. It's a hot and growing field, and up-to-speed ML developers are in demand. About the Book Real-World Machine Learning will teach you the concepts and techniques you need to be a successful machine learning practitioner without overdosing you on abstract theory and complex mathematics. By working through immediately relevant examples in Python, you'll build skills in data acquisition and modeling, classification, and regression. You'll also explore the most important tasks like model validation, optimization, scalability, and real-time streaming. When you're done, you'll be ready to successfully build, deploy, and maintain your own powerful ML systems. What's InsidePredicting future behaviorPerformance evaluation and optimizationAnalyzing sentiment and making recommendationsAbout the Reader No prior machine learning experience assumed. Readers should know Python. About the Authors Henrik Brink, Joseph Richards and Mark Fetherolf are experienced data scientists engaged in the daily practice of machine learning. Table of Contents PART 1: THE MACHINE-LEARNING WORKFLOWWhat is machine learning?Real-world dataModeling and predictionModel evaluation and optimizationBasic feature engineeringPART 2: PRACTICAL APPLICATIONExample: NYC taxi dataAdvanced feature engineeringAdvanced NLP example: movie review sentimentScaling machine-learning workflowsExample: digital display advertising", "item_img_path" : "https://covers1.booksamillion.com/covers/bam/1/61/729/192/1617291927_b.jpg", "price_data" : { "retail_price" : "49.99", "online_price" : "49.99", "our_price" : "49.99", "club_price" : "49.99", "savings_pct" : "0", "savings_amt" : "0.00", "club_savings_pct" : "0", "club_savings_amt" : "0.00", "discount_pct" : "10", "store_price" : "" } }
Real-World Machine Learning|Henrik Brink

Real-World Machine Learning

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Overview

Summary Real-World Machine Learning is a practical guide designed to teach working developers the art of ML project execution. Without overdosing you on academic theory and complex mathematics, it introduces the day-to-day practice of machine learning, preparing you to successfully build and deploy powerful ML systems. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology Machine learning systems help you find valuable insights and patterns in data, which you'd never recognize with traditional methods. In the real world, ML techniques give you a way to identify trends, forecast behavior, and make fact-based recommendations. It's a hot and growing field, and up-to-speed ML developers are in demand. About the Book Real-World Machine Learning will teach you the concepts and techniques you need to be a successful machine learning practitioner without overdosing you on abstract theory and complex mathematics. By working through immediately relevant examples in Python, you'll build skills in data acquisition and modeling, classification, and regression. You'll also explore the most important tasks like model validation, optimization, scalability, and real-time streaming. When you're done, you'll be ready to successfully build, deploy, and maintain your own powerful ML systems. What's Inside

  • Predicting future behavior
  • Performance evaluation and optimization
  • Analyzing sentiment and making recommendations
About the Reader No prior machine learning experience assumed. Readers should know Python. About the Authors Henrik Brink, Joseph Richards and Mark Fetherolf are experienced data scientists engaged in the daily practice of machine learning. Table of Contents PART 1: THE MACHINE-LEARNING WORKFLOW
  1. What is machine learning?
  2. Real-world data
  3. Modeling and prediction
  4. Model evaluation and optimization
  5. Basic feature engineering

PART 2: PRACTICAL APPLICATION
  1. Example: NYC taxi data
  2. Advanced feature engineering
  3. Advanced NLP example: movie review sentiment
  4. Scaling machine-learning workflows
  5. Example: digital display advertising

This item is Non-Returnable

Details

  • ISBN-13: 9781617291920
  • ISBN-10: 1617291927
  • Publisher: Manning Publications
  • Publish Date: September 2016
  • Dimensions: 9.1 x 7.3 x 0.7 inches
  • Shipping Weight: 1 pounds
  • Page Count: 264

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