September 2016
Beginner to intermediate
264 pages
9h 26m
English
Overview
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.
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.
Quotes
This is that crucial other book that many old hands wish they had back in the day.
- From the Foreword by Beau Cronin, 21 Inc.
A comprehensive guide on how to prepare data for ML and how to choose the appropriate algorithms.
- Michael Lund, iCodeIT
Very approachable. Great information on data preparation and feature engineering, which are typically ignored.
- Robert Diana, RSI Content Solutions
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