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Foundational Hands-On Skills for Succeeding with Real Data Science Projects
This pragmatic book introduces both machine learning and data science, bridging gaps between data scientist and engineer, and helping you bring these techniques into production. It helps ensure that your efforts actually solve your problem, and offers unique coverage of real-world optimization in production settings.
–From the Foreword by Paul Dix, series editor
Machine Learning in Production is a crash course in data science and machine learning for people who need to solve real-world problems in production environments. Written for technically competent “accidental data scientists” with more curiosity and ambition than formal training, this complete and rigorous introduction stresses practice, not theory.
Building on agile principles, Andrew and Adam Kelleher show how to quickly deliver significant value in production, resisting overhyped tools and unnecessary complexity. Drawing on their extensive experience, they help you ask useful questions and then execute production projects from start to finish.
The authors show just how much information you can glean with straightforward queries, aggregations, and visualizations, and they teach indispensable error analysis methods to avoid costly mistakes. They turn to workhorse machine learning techniques such as linear regression, classification, clustering, and Bayesian inference, helping you choose the right algorithm for each production problem. Their concluding section on hardware, infrastructure, and distributed systems offers unique and invaluable guidance on optimization in production environments.
Andrew and Adam always focus on what matters in production: solving the problems that offer the highest return on investment, using the simplest, lowest-risk approaches that work.
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About the Authors
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 Pre-Processing
Chapter 5: Hypothesis Testing
Chapter 6: Data Visualization
Part II: Algorithms and Architectures
Chapter 7: 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