PRACTICAL MALWARE ANALYSIS by Michael Sikorski
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Practical MLOps by Noah Gift
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Practical Natural Language Processing by Sowmya Vajjala
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Practical Python Data Wrangling and Data Quality by Susan E. McGregor
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Practical Statistics for Data Scientists 2nd by Peter Bruce
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Practical Time Series Analysis by Aileen Nielsen
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Practicing Trustworthy Machine Learning: Consistent, Transparent, and Fair AI Pipelines by Yada Pruksachatkun
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Pragmatic Flutter by Priyanka Tyagi
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Pragmatic Programmer, The: Your journey to mastery, 20th Anniversary Edition by David Thomas & Andrew Hunt
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Predictive Analytics for the Modern Enterprise by Nooruddin Abbas Ali
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Privacy and Security for Large Language Models by Baihan Lin
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Pro C# 10 with .NET 6 11th Edition by Andrew Troelsen
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Probabilistic Machine Learning by Kevin Patrick Murphy
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Production Kubernetes by Josh Rosso
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Programming Android with Kotlin by Pierre-Olivier Laurence
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Programming Bitcoin by Jimmy Song
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Programming C# 12 by Ian Griffiths
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Programming Interviews Exposed: Coding Your Way Through the Interview 4th by John Mongan
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Programming iOS 14, 11th by Matt Neuburg
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Programming Large Language Models With Azure Open Ai by Francesco Esposito
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Programming PHP, 4th by Peter MacIntyre
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Programming PyTorch for Deep Learning by Ian Pointer
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Programming Rust 2nd by Jim Blandy
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Programming the Internet of Things by Andy King
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Programming TypeScript: Making Your JavaScript Applications Scale by Boris Cherny
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Programming with Rust by Donis Marshall
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Programming: Principles and Practice Using C++ 3rd Edition by Bjarne Stroustrup
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Prometheus: Up & Running 2nd Edition by Julien Pivotto
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Prompt Engineering for Generative AI by James Phoenix
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Prompt Engineering for LLMs by John Berryman
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Python 3 Object-Oriented Programming 3rd by Dusty Phillips
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Python 3: 400 exercises and solutions for beginners by Assad Patel
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Python 3: The Comprehensive Guide by Johannes Ernesti
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Python and R for the Modern Data Scientist by Rick J. Scavetta
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Python Basics by David Amos
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Python Basics: A Self-Teaching Introduction by H. Bhasin
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This book is designed for use as aprimary introduction to Python and can be used as an introductory text or as aresource for professionals in industry. The book has been divided into foursections. The first section deals with the language fundamentals, primarily theprocedural part of the language, the second introduces the object-oriented paradigms,the third section deals with data structures, and the last is devoted toadvanced topics like handling multi-dimensional arrays using NumPy andvisualization using Matplotlib. Regular expressions and multi-threading havebeen introduced in the appendices.
FEATURES
•Includes sections dedicated to data structures
•Offers in-depth treatment of topics such as classes, inheritance, BST, andNumPy
•Introduces topics like Matplotlib and PIL
•Contains exercises for practice and a review of essential programming concepts
Python Cookbook 3rd by David Beazley
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Python Crash Course 3rd Edition by Eric Matthes
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Python Data Analysis 3rd by Avinash Navlani
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Python Data Analytics 3rd Edition by Fabio Nelli
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