Data-Driven Science and Engineering : Machine Learning, Dynamical Systems, and Control
Steven L. (University of Washington) Brunton
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Mathematics for Machine Learning
Marc Peter (University College London) Deisenroth
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Designing Machine Learning Systems : An Iterative Process for Production-Ready Applications
Chip Huyen
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Learning Spark
Jules Damji
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Essential Math for Data Science : Take Control of Your Data with Fundamental Linear Algebra, Probability, and Statistics
Thomas Nield
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The Theory and Practice of Enterprise AI : Recipes and Reference Implementations for Marketing, Supply Chain, and Production Operations
Ilya Katsov
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Python Data Science Handbook : Essential Tools for Working with Data
Jake Vanderplas
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Observability Engineering : Achieving Production Excellence
Charity Majors
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Essential Math for AI : Next-Level Mathematics for Efficient and Successful AI Systems
Hala Nelson
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Building Machine Learning Powered Applications : Going from Idea to Product
Emmanuel Ameisen
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Building Machine Learning Pipelines
Hannes Hapke
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Data Science on AWS : Implementing End-to-End, Continuous AI and Machine Learning Pipelines
Chris Fregly
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Machine Learning and Data Science Blueprints for Finance : From Building Trading Strategies to Robo-Advisors Using Python
Hariom Tatsat
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Spark in Action, Second Edition
Jean-Georges Perrin
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AI at the Edge : Solving Real-World Problems with Embedded Machine Learning
Daniel Situnayake
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Practical Linear Algebra for Data Science : From Core Concepts to Applications Using Python
Mike X Cohen
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Machine Learning for High-Risk Applications : Approaches to Responsible AI
Patrick Hall
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Multiagent Systems
Gerhard (Professor, Chair of Department of Knowledge Engineerin Weiss
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Blueprints for Text Analytics using Python : Machine Learning Based Solutions for Common Real World (NLP) Applications
Jens Albrecht
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Introducing MLOps : How to Scale Machine Learning in the Enterprise
Mark Treveil
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Applied Machine Learning and AI for Engineers : Solve Business Problems That Can't Be Solved Algorithmically
Jeff Prosise
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Scaling Machine Learning with Spark : Distributed ML with MLlib, TensorFlow, and PyTorch
Adi Polak
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Practicing Trustworthy Machine Learning : Consistent, Transparent, and Fair AI Pipelines
Yada Pruksachatkun
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Bandit Algorithms
Tor (University of Alberta) Lattimore
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