May 2019
Beginner to intermediate
288 pages
9h 1m
English
book
Machine Learning in Production: Developing and Optimizing Data Science Workflows and Applications
by Andrew Kelleher, Adam Kelleher
Content preview from Machine Learning in Production: Developing and Optimizing Data Science Workflows and Applications
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I. Principles of Framing
Chapter 1, “The Role of the Data Scientist,” provides background information about the field of data science. This should serve as a starting point to gain context for the role of data science in industry.
Chapter 2, “Project Workflow,” describes project workflow and how it relates to the principles of agile software development.
Chapter 3, “Quantifying Error,” introduces the concept of measurement error and describes how to quantify it. It then shows how to propagate error approximately through calculations.
Chapter 4, “Data Encoding and Preprocessing,” describes how to encode complex, real-world data into something a machine learning algorithm can understand. Using text processing as the example case, the chapter explores ...
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.