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.
- Title
- Real-World Machine Learning
- Publisher
- Manning
- Author(s)
- Henrick Brink, Joesph Richards, Mark Fetherolf
- Published
- 2016-09-29
- Edition
- 1
- Format
- eBook (pdf, epub, mobi)
- Pages
- 264
- Language
- English
- ISBN-10
- 1617291927
- ISBN-13
- 9781617291920
- License
- Read online for free
- Book Homepage
- Free eBook, Errata, Code, Solutions, etc.
Copyright Brief Table of Contents Table of Contents Foreword Preface Acknowledgments About this Book About the Authors About the Cover Illustration Part 1. The machine-learning workflow Chapter 1. What is machine learning? Chapter 2. Real-world data Chapter 3. Modeling and prediction Chapter 4. Model evaluation and optimization Chapter 5. Basic feature engineering Part 2. Practical application Chapter 6. Example: NYC taxi data Chapter 7. Advanced feature engineering Chapter 8. Advanced NLP example: movie review sentiment Chapter 9. Scaling machine-learning workflows Chapter 10. Example: digital display advertising Appendix. Popular machine-learning algorithms Index List of Figures List of Tables List of Listings