Python Tools for Data Scientists Pocket Primer. A Quick Guide to Essential Python Libraries for Data Science

Mercury Learning and Information, Oswald Campesato

This book, part of the best-selling Pocket Primer series, offers a comprehensive introduction to essential Python tools for data scientists. It begins with an overview of Python basics, followed by in-depth coverage of NumPy and Pandas, focusing on their features and applications. The text also addresses the critical tasks of writing regular expressions and performing data cleaning.Further sections delve into data visualization techniques and the use of Sklearn and SciPy, providing practical knowledge and skills for handling complex data analysis tasks. This structured approach ensures that readers gain a complete understanding of the tools and techniques necessary for effective data science.Designed to be accessible yet thorough, this book includes numerous code samples to reinforce learning. Companion files with source code are available for download, making it an invaluable resource for anyone looking to master Python for data science and enhance their data analysis capabilities.

Artificial Intelligence and Expert Systems. Techniques and Applications for Problem Solving

Mercury Learning and Information, I. Gupta, G. Nagpal

This book covers current AI applications and techniques for solving problems and accomplishing tasks. It introduces branches of AI such as formal logic, reasoning, knowledge engineering, expert systems, neural networks, and fuzzy logic. It emphasizes expert systems, with sections on state space search, knowledge engineering, neural networks, fuzzy logic, and Prolog.It begins with an introduction to AI and its applications, setting the stage for foundational concepts. Readers are guided through state space search and heuristic search strategies, crucial for problem-solving in AI. The focus shifts to expert systems, covering their development life cycle, knowledge acquisition, and representation, providing a deep dive into emulating human decision-making.Later chapters cover neural networks and the learning process, essential for creating adaptive systems. Sections on fuzzy logic and fuzzy systems introduce methods for handling uncertainty in AI. Final chapters on programming in logic and advanced Prolog offer practical techniques for AI solutions. This approach equips readers with the skills to apply AI in various domains, enhancing their problem-solving abilities and understanding of intelligent systems.

Python. An Introduction to Python Programming

Mercury Learning and Information, James R. Parker

This book introduces programming concepts using Python 3, designed for beginners and hobbyists with no prior programming experience. It covers loops, strings, functions, files, graphics, multimedia, algorithms, classes, and more. Many examples are based on video game development, making learning engaging and practical. The book uses a just-in-time presentation style, providing material when it's needed, and includes companion files with source code, exercises, projects, and figures.The course starts with an overview of modern computers and basic programming concepts. It progresses through topics like repetition, sequences, functions, file input/output, classes, graphics, data manipulation, and multimedia. The latter chapters focus on basic algorithms, programming for the sciences, writing good programs, parsing, and communicating using graphics and Pygame.By the end of the course, students will have a solid understanding of programming fundamentals, enhanced by practical applications in video game development. This approach not only makes learning enjoyable but also equips students with the skills needed for further studies in computer science and programming.

Empirical Cloud Security. A Guide To Practical Intelligence to Evaluate Risks and Attacks

Mercury Learning and Information, Aditya K. Sood

This book, updated to include the latest research and developments in cloud security, is essential for security professionals, DevOps engineers, penetration testers, cloud security engineers, and cloud software developers. It offers practical approaches to securing cloud infrastructure and applications against threats, attacks, and data breaches. The content is refined for better accessibility and engagement, providing a comprehensive guide to cloud security.The course starts with fundamental cloud security concepts and progresses to hands-on assessment techniques based on real-world case studies. It covers cloud architecture, IAM for authentication and authorization, network security, database and storage security, cryptography controls, secure code review, and monitoring and logging. The practical strategies for assessing security and privacy are crucial for building a robust cloud infrastructure.The journey concludes with advanced topics such as privacy in the cloud, identifying security flaws and attacks, and understanding the impact of malicious code. New case studies reveal how threat actors exploit cloud environments, offering preventative measures to enhance cloud security. This structured approach ensures a thorough understanding and practical application of cloud security principles.

Combating Cyberattacks Targeting the AI Ecosystem. Strategies to secure AI systems from emerging cyber threats, risks, and vulnerabilities

Mercury Learning and Information, Aditya K. Sood

Artificial intelligence is transforming industries, but it also exposes organizations to new cyber threats. This course begins by introducing the foundational concepts of securing large language models (LLMs), generative AI applications, and the broader AI infrastructure. Participants will explore the evolving threat landscape, gaining insights into how attackers exploit vulnerabilities in AI systems and the risks posed by trust and compliance failures.The course provides real-world case studies to highlight attack vectors like adversarial inputs, data poisoning, and model theft. Participants will learn practical methods for identifying and mitigating vulnerabilities in AI systems. These insights prepare learners to proactively safeguard their AI infrastructures using advanced security assessment techniques.Finally, the course equips participants with actionable strategies to defend AI systems. You’ll learn to protect sensitive data, implement robust security measures, and address ethical challenges in AI. By the end, you’ll be ready to secure AI ecosystems and adapt to the fast-evolving AI security landscape.

WORKING WITH grep, sed, AND awk Pocket Primer. A Quick Guide to Mastering Powerful Command Line Tools

Mercury Learning and Information, Oswald Campesato

This book introduces readers to three powerful command-line utilities—grep, sed, and awk—that can create simple yet powerful shell scripts. Using the bash shell, it focuses on small text files to help readers understand these tools. Grep searches for patterns in data, sed modifies data, and awk performs tasks on pattern matches. Aimed at those new to the bash environment, the book is also valuable for those with some experience.The journey starts with grep, teaching how to search for specific words or patterns in data. It then moves to sed, showing how to change or modify data efficiently. Finally, it delves into awk, a versatile programming language for searching and processing data files. The book also includes a chapter on using regular expressions with these tools, enhancing your scripting capabilities.Mastering these utilities is crucial for efficient data handling and automation in a bash environment. This book transitions readers from basic to advanced command-line skills, blending theory with practical examples. It is an essential resource for anyone looking to harness the full power of bash scripting.

Python for TensorFlow Pocket Primer. A Quick Guide to Python Libraries for TensorFlow Developers

Mercury Learning and Information, Oswald Campesato

As part of the best-selling *Pocket Primer* series, this book prepares programmers for machine learning and deep learning with TensorFlow. It begins with a quick introduction to Python, followed by chapters on NumPy, Pandas, Matplotlib, and scikit-learn. The final chapters provide TensorFlow 1.x code samples, including detailed examples for TensorFlow Dataset, crucial for TensorFlow 2.The journey starts with Python basics and progresses through essential data manipulation and visualization libraries. You'll explore machine learning fundamentals with scikit-learn before diving into TensorFlow, learning to construct data pipelines with TensorFlow Dataset APIs like map(), filter(), and batch().Understanding these concepts is vital for modern AI applications. This book transitions readers from basic programming to advanced machine learning and deep learning techniques, blending theory with practical skills. Companion files with source code enhance learning, making this an essential resource for mastering Python, machine learning, and TensorFlow.

Python 3 Using ChatGPT / GPT-4. Harnessing AI for Efficient Python Programming

Mercury Learning and Information, Oswald Campesato

This book is for people who want to learn Python 3 and how to use ChatGPT with Python. It starts with an introduction to Python programming, covering data types, number formatting, Unicode handling, and text manipulation. The book then covers loops, conditional logic, reserved words, user input, exception management, and command-line arguments.The journey continues into Generative AI, discussing its distinction from Conversational AI. Popular platforms like ChatGPT and GPT-4 are explored, along with their strengths, weaknesses, and potential applications. The book shows how to generate Python 3 code samples via ChatGPT using the “Code Interpreter” plugin.Understanding these concepts is crucial for navigating Python and AI. This book transitions readers from basic Python programming to advanced AI applications, blending theory with practical skills. Companion files with code samples and figures enhance learning, making this an essential resource for mastering Python and ChatGPT.