Real-World Machine Learning
暫譯: 實務機器學習

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

THE MACHINE-LEARNING WORKFLOW
What is machine learning?
Real-world data
Modeling and prediction
Model evaluation and optimization
Basic feature engineering
PRACTICAL APPLICATION
Example: NYC taxi data
Advanced feature engineering
Advanced NLP example: movie review sentiment
Scaling machine-learning workflows
Example: digital display advertising