Real-World Machine Learning

ISBN: 1617291927

Author: Brink, Henrik

Condition: New

SummaryReal-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 TechnologyMachine 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 BookReal-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 behavior Performance evaluation and optimization Analyzing sentiment and making recommendationsAbout the ReaderNo prior machine learning experience assumed. Readers should know Python.About the AuthorsHenrik Brink, Joseph Richards and Mark Fetherolf are experienced data scientists engaged in the daily practice of machine learning.Table of ContentsPART 1: THE MACHINE-LEARNING WORKFLOWWhat is machine learning? Real-world data Modeling and prediction Model evaluation and optimization Basic feature engineeringPART 2: PRACTICAL APPLICATIONExample: NYC taxi data Advanced feature engineering Advanced NLP example: movie review sentiment Scaling machine-learning workflows Example: digital display advertising