Real-World Machine Learning

Real-World Machine Learning
PDF
  • eBook:
    Real-World Machine Learning
  • Author:
    Henrik Brink, Joseph Richards, Mark Fetherolf
  • Edition:
    1 edition
  • Categories:
  • Data:
    September 30, 2016
  • ISBN:
    1617291927
  • ISBN-13:
    9781617291920
  • Language:
    English
  • Pages:
    264 pages
  • Format:
    PDF

Book Description
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

Content

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
6. Example: NYC taxi data
7. Advanced feature engineering
8. Advanced NLP example: movie review sentiment
9. Scaling machine-learning workflows
10. Example: digital display advertising

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