Machine Learning in Production
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Machine Learning in Production: Developing and Optimizing Data Science Workflows and Applications

Machine Learning in Production: Developing and Optimizing Data Science Workflows and Applications

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About the Book

The typical data science task in industry starts with an “ask” from the business. But few data scientists have been taught what to do with that ask. This book shows them how to assess it in the context of the business’s goals, reframe it to work optimally for both the data scientist and the employer, and then execute on it. Written by two of the experts who’ve achieved breakthrough optimizations at BuzzFeed, it’s packed with real-world examples that take you from start to finish: from ask to actionable insight.   Andrew Kelleher and Adam Kelleher walk you through well-formed, concrete principles for approaching common data science problems, giving you an easy-to-use checklist for effective execution. Using their principles and techniques, you’ll gain deeper understanding of your data, learn how to analyze noise and confounding variables so they don’t compromise your analysis, and save weeks of iterative improvement by planning your projects more effectively upfront.   Once you’ve mastered their principles, you’ll put them to work in two realistic, beginning-to-end site optimization tasks. These extended examples come complete with reusable code examples and recommended open-source solutions designed for easy adaptation to your everyday challenges. They will be especially valuable for anyone seeking their first data science job -- and everyone who’s found that job and wants to succeed in it.

Table of Contents:
Part I: Principles of Framing Chapter 1: The Role of the Data Scientist Chapter 2: Project Workflow Chapter 3: Quantifying Error Chapter 4: Data Encoding and Preprocessing Chapter 5: Hypothesis Testing Chapter 6: Data Visualization Part II: Algorithms and Architectures Chapter 7: Introduction to Algorithms and Architectures Chapter 8: Comparison Chapter 9: Regression Chapter 10: Classification and Clustering Chapter 11: Bayesian Networks Chapter 12: Dimensional Reduction and Latent Variable Models Chapter 13: Causal Inference Chapter 14: Advanced Machine Learning Part III: Bottlenecks and Optimizations Chapter 15: Hardware Fundamentals Chapter 16: Software Fundamentals Chapter 17: Software Architecture Chapter 18: The CAP Theorem Chapter 19: Logical Network Topological Nodes Bibliography


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Product Details
  • ISBN-13: 9780134116549
  • Publisher: Pearson Education (US)
  • Publisher Imprint: Addison Wesley
  • Height: 230 mm
  • No of Pages: 288
  • Spine Width: 20 mm
  • Weight: 561 gr
  • ISBN-10: 0134116542
  • Publisher Date: 15 May 2019
  • Binding: Paperback
  • Language: English
  • Returnable: N
  • Sub Title: Developing and Optimizing Data Science Workflows and Applications
  • Width: 180 mm


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Machine Learning in Production: Developing and Optimizing Data Science Workflows and Applications
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Machine Learning in Production: Developing and Optimizing Data Science Workflows and Applications
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