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Machine Learning in Production, 1/e


Machine Learning in Production, 1/e
Author(s)  Simy Joy ,Payal Anand ,Priya Nair Rajeev
ISBN  9789389588507
Imprint  Pearson Education
Copyright  2020
Pages  256
Binding  Paperback
List Price  Rs. 670.00
  
 
 

Machine Learning in Production is a crash course in data science and machine learning for learners 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 signi¬cant 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 -nish.
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, classi¬cation, clustering, and
Bayesian inference, helping you choose the right algorithm for each production problem. Their
concluding section on hardware, infrastructure, and distributed systems o ers unique and invaluable
guidance on optimization in production environments.
They always focus on what matters in production: solving the problems that o er the highest return on
investment, using the simplest, lowest-risk approaches that work.
 

  • About the Authors
  • Contents
  • Features
  • Downloadable Resources

"Andrew Kelleher is a sta software engineer and distributed systems architect at Venmo. He was previously a sta software engineer at BuzzFeed and has worked on data pipelines and algorithm implementations for modern optimization. He graduated with a BS in physics from Clemson University. He runs a meetup in New York City that studies the fundamentals behind distributed systems in the context of production applications, and was ranked one of Fast Company's most creative people two years in a row.


Adam Kelleher wrote this book while working as principal data scientist at BuzzFeed and adjunct professor at Columbia University in the City of New York. As of May 2018, he is chief data scientist for research at Barclays and teaches causal inference and machine learning products at Columbia.


He graduated from Clemson University with a BS in physics, and has a PhD in cosmology from University of North Carolina at Chapel Hill.


 

 

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


 

 

1. ? Leverage agile principles to maximize development e_ciency in production projects


2. ? Learn from practical Python code examples and visualizations that bring essential algorithmic concepts to life


3. ? Start with simple heuristics and improve them as your data pipeline matures


4. ? Communicate your results with basic data visualization techniques


5. ? Master basic machine learning techniques, starting with linear regression and random forests


6. ? Perform classi_cation and clustering on both vector and graph data


7. ? Learn the basics of graphical models and Bayesian inference


8. ? Understand correlation and causation in machine learning models


9. ? Explore over_tting, model capacity, and other advanced machine learning techniques


10. ? Make informed architectural decisions about storage, data transfer, computation, and communication


 

 
 
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