
The Pragmatic Programmer for Machine Learning
Engineering Analytics and Data Science Solutions
- 340 pages
- English
- ePUB (mobile friendly)
- Available on iOS & Android
The Pragmatic Programmer for Machine Learning
Engineering Analytics and Data Science Solutions
About this book
Machine learning has redefined the way we work with data and is increasingly becoming an indispensable part of everyday life. The Pragmatic Programmer for Machine Learning: Engineering Analytics and Data Science Solutions discusses how modern software engineering practices are part of this revolution both conceptually and in practical applictions.
Comprising a broad overview of how to design machine learning pipelines as well as the state-of-the-art tools we use to make them, this book provides a multi-disciplinary view of how traditional software engineering can be adapted to and integrated with the workflows of domain experts and probabilistic models.
From choosing the right hardware to designing effective pipelines architectures and adopting software development best practices, this guide will appeal to machine learning and data science specialists, whilst also laying out key high-level principlesin a way that is approachable for students of computer science and aspiring programmers.
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Information
Table of contents
- Cover Page
- Half-Title Page
- Series Page
- Title Page
- Copyright Page
- Dedication Page
- Contents
- Preface
- 1 What Is This Book About?
- I Foundations of Scientific Computing
- II Best Practices for Machine Learning Pipelines
- III Tools and Technologies
- IV A Case Study
- Bibliography
- Index