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.

Pandas Basics. Mastering Data Analysis with Pandas

Mercury Learning and Information, Oswald Campesato

This book is essential for aspiring data scientists and anyone needing to perform data cleaning using Pandas and NumPy. It offers numerous code samples and comprehensive coverage of NumPy and Pandas features, including writing regular expressions. Chapter 3 introduces fundamental statistical concepts, while Chapter 7 delves into data visualization using Matplotlib and Seaborn. Companion files with code are available for download from the publisher.Starting with an introduction to Python, the course progresses through working with data, and then moves into Pandas, covering its functionalities in three detailed chapters. The statistical concepts provided are crucial for analyzing data effectively, while the visualization techniques enhance the ability to present data insights clearly.By the end of this course, users will have a solid foundation in data manipulation and cleaning, statistical analysis, and data visualization, enabling them to tackle real-world data science tasks confidently and efficiently.

Microsoft Access 2021 Programming Pocket Primer. A Comprehensive Guide to Mastering Access VBA

Mercury Learning and Information, Julitta Korol

This book, part of the successful Pocket Primer series, is designed for those who need to master Access programming fundamentals quickly. You only need this concise guide to get started and feel comfortable with VBA. The concepts learned here also apply to Excel programming. The book is divided into nine chapters, each progressively introducing you to programming in Microsoft Access 2021.Beginning with an introduction to Access VBA, the book covers the Visual Basic Editor, fundamental VBA programming concepts, and how to create both built-in and custom functions. You will learn to add decision-making capabilities and repeating actions to your programs, manage multiple values using arrays and collections, and utilize built-in tools for testing and debugging your code.This structured approach ensures that by the end of the course, you will have a solid foundation in Access programming. The companion files provide all the hands-on resources needed to complete chapter projects, making this an essential resource for mastering Access VBA efficiently and effectively.

Python Data Structures Pocket Primer. A concise guide to Python data structures to enhance your skills

Mercury Learning and Information, Oswald Campesato

This Pocket Primer book introduces the fundamentals of data structures using Python. It provides a comprehensive yet fast-paced introduction to core Python concepts and data structures, emphasizing their importance in managing large datasets and implementing search and sort algorithms effectively. The course starts with a basic introduction to Python, setting a solid foundation for more complex topics.The journey continues with an exploration of recursion and combinatorics, followed by detailed discussions on strings, arrays, and various search and sort algorithms. Further, the book delves into linked lists, queues, and stacks, illustrating their practical applications with numerous code samples. This structured approach ensures that learners can progressively build their knowledge and skills in data structures, reinforced by hands-on coding examples.With companion files available for download, the book provides additional resources for practice and deeper understanding. This comprehensive guide is ideal for both beginners and those looking to strengthen their grasp of data structures in Python, equipping them with essential tools for managing and manipulating large datasets.

Autodesk(R) Revit(R) 2024 Architecture. Mastering Building Design with BIM

Mercury Learning and Information, Munir Hamad

This book covers all major techniques in Revit 2024 Architecture using both metric and imperial units. It details the myriad drawing and editing tools and includes over 50 mini-workshops that guide users from concept to plotting. By solving these workshops, readers will master Revit Architecture, covering all basic commands and functions. Companion files with templates and models are available for practical application.The course begins with an introduction to Revit 2024, followed by detailed chapters on drawing, modifying, project preparation, and wall construction. Subsequent chapters cover doors, windows, curtain walls, floors, roofs, components, and ceilings. Users will also learn about creating stairs, ramps, railings, views, annotations, legends, and visualization.Further, the book delves into schedules, project phasing, design options, topography, rooms, tagging, detailing, groups, file import/export, mass creation, and customizing elements. It concludes with chapters on creating and customizing families, and managing worksets and shared views, ensuring comprehensive knowledge and skills in Revit 2024 Architecture.

Large Language Models An Introduction. Understanding the Fundamentals and Applications of Generative AI

Mercury Learning and Information, Oswald Campesato

This book provides a comprehensive overview of large language models (LLMs) and generative AI, covering foundational principles, advanced concepts, and practical applications. It begins with an introduction to the generative AI landscape, examining key features, popular techniques, and its influence on industries like art, business, and technology. The book also explores prominent models like ChatGPT and GPT-4, detailing their architectures, capabilities, and applications in text generation, code assistance, and conversational AI.Additionally, the book traces the evolution of LLMs, particularly focusing on the BERT family and its wide-ranging applications in natural language processing, translation, and multilingual tasks. Readers will also gain expertise in prompt engineering, learning practical tips and examples to interact more effectively with LLMs.In later chapters, the book tackles advanced topics such as fine-tuning LLMs, working with SVG, and concepts like few-shot learning and reinforcement learning. It concludes with discussions on ethical issues, biases in generative AI, and emerging trends like AI’s integration with IoT, quantum computing, and neuromorphic computing. This guide equips professionals and enthusiasts with a thorough understanding of generative AI's present and future.

Data Science for IoT Engineers. Master Data Science Techniques and Machine Learning Applications for Innovative IoT Solutions

Mercury Learning and Information, P. G. Madhavan

This book introduces data science to professionals in engineering, physics, mathematics, and related fields. It serves as a workbook with MATLAB code, linking subject knowledge to data science, machine learning, and analytics, with applications in IoT. Part One integrates machine learning, systems theory, linear algebra, digital signal processing, and probability theory. Part Two develops a nonlinear, time-varying machine learning solution for modeling real-life business problems.Understanding data science is crucial for modern applications, particularly in IoT. This book presents a dynamic machine learning solution to handle these complexities. Topics include machine learning, systems theory, linear algebra, digital signal processing, probability theory, state-space formulation, Bayesian estimation, Kalman filter, causality, and digital twins.The journey begins with data science and machine learning, covering systems theory and linear algebra. Advanced concepts like the Kalman filter and Bayesian estimation lead to developing a dynamic machine learning model. The book ends with practical applications using digital twins.

The Challenge to AI. The Future of AGI: Bridging Human Cognition and Artificial Intelligence

Mercury Learning and Information, Stephen Robbins

This book invites readers to explore where AI, consciousness, and human cognition intersect. It challenges existing notions in AI and cognitive science by arguing that true intelligence lies not in software but in the brain's complex biochemical system, far removed from current AI architectures. Through examining time, perception, language, and thought, the book presents a compelling case for the indispensability of biology and consciousness in cognition, highlighting the profound differences between man and machine.The journey begins with an introduction to AGI and the brain's resonance, moving through philosophical perspectives on perception and the external world. Readers will delve into the biochemical basis of cognition, exploring implicit and explicit memory, conscious cognition, voluntary actions, and the unique aspects of human speech and generative AI. The book culminates with discussions on affect, space, time, and the requirements for creating a conscious device.Understanding these concepts is crucial for advancing AI and cognitive science. This book transitions readers from conventional views to a new framework that integrates biology and consciousness, blending theory with profound insights. It is an essential resource for those seeking to understand the true nature of intelligence and the future of AI.