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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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  • List Price: $39.99
  • Includes EPUB, MOBI, and PDF
  • About eBook Formats
  • This eBook includes the following formats, accessible from your Account page after purchase:

    ePub EPUB The open industry format known for its reflowable content and usability on supported mobile devices.

    MOBI MOBI The eBook format compatible with the Amazon Kindle and Amazon Kindle applications.

    Adobe Reader PDF The popular standard, used most often with the free Adobe® Reader® software.

    This eBook requires no passwords or activation to read. We customize your eBook by discreetly watermarking it with your name, making it uniquely yours.

About

Features

  • Practical principles and step-by-step techniques for transforming any business “ask” into actionable insight
  • Includes two complete end-to-end site optimization projects, with reusable code and open source tools recommendations readers can easily adapt for their own projects
  • Ideal for all junior and aspiring data scientists -- and for all software developers, engineers, and others with new responsibilities related to data science
  • By Andrew and Adam Kelleher, brothers playing pivotal roles in data science and engineering at BuzzFeed
  • No heavy math or statistics background required!

Description

  • Copyright 2019
  • Dimensions: 7" x 9-1/8"
  • Pages: 288
  • Edition: 1st
  • Book
  • ISBN-10: 0-13-411654-2
  • ISBN-13: 978-0-13-411654-9

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.

Extras

Author's Site

Please visit the author's site at adamkelleher.com/ml_book.

Sample Content

Online Sample Chapter

Tools for Causal Inference

Sample Pages

Download the sample pages (includes Chapter 13)

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

Updates

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