Natural Language Processing Fundamentals for Developers. A Practical Guide to Building NLP Applications

Mercury Learning and Information, Oswald Campesato

This book is for developers seeking an overview of basic concepts in Natural Language Processing (NLP). It caters to those with varied technical backgrounds, offering numerous code samples and listings to illustrate the wide range of topics covered. The journey begins with managing data relevant to NLP, followed by two chapters on fundamental NLP concepts. This foundation is reinforced with Python code samples that bring these concepts to life.The book then delves into practical NLP applications, such as sentiment analysis, recommender systems, COVID-19 analysis, spam detection, and chatbots. These examples provide real-world context and demonstrate how NLP techniques can be applied to solve common problems. The final chapter introduces advanced topics, including the Transformer architecture, BERT-based models, and the GPT family, highlighting the latest state-of-the-art developments in the field.Appendices offer additional resources, including Python code samples on regular expressions and probability/statistical concepts, ensuring a well-rounded understanding. Companion files with source code and figures enhance the learning experience, making this book a comprehensive guide for mastering NLP techniques and applications.

Natural Language Processing using R Pocket Primer. Learn Essential NLP Techniques and Tools for Developers

Mercury Learning and Information, Oswald Campesato

This book is for developers seeking an overview of basic concepts in Natural Language Processing (NLP). It caters to a technical audience, offering numerous code samples and listings to illustrate the wide range of topics covered. The journey begins with managing data relevant to NLP, followed by two chapters on fundamental NLP concepts. This foundation is reinforced with Python code samples that bring these concepts to life.The book then delves into practical NLP applications, such as sentiment analysis, recommender systems, COVID-19 analysis, spam detection, and chatbots. These examples provide real-world context and demonstrate how NLP techniques can be applied to solve common problems. The final chapter introduces advanced topics, including the Transformer architecture, BERT-based models, and the GPT family, highlighting the latest state-of-the-art developments in the field.Appendices offer additional resources, including Python code samples on regular expressions and probability/statistical concepts, ensuring a well-rounded understanding. Companion files with source code and figures enhance the learning experience, making this book a comprehensive guide for mastering NLP techniques and applications.

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.

Python 3 and Data Analytics Pocket Primer. A Quick Guide to NumPy, Pandas, and Data Visualization

Mercury Learning and Information, Oswald Campesato

This book, part of the best-selling Pocket Primer series, introduces readers to the fundamental concepts of data analytics using Python 3. The course begins with a concise introduction to Python, covering essential programming constructs and data manipulation techniques. This foundation sets the stage for deeper dives into data analytics, emphasizing the importance of data cleaning, a critical step in any data analysis process.Following the Python basics, the course explores powerful libraries such as NumPy and Pandas for efficient data handling and manipulation. It then delves into statistical concepts, providing the necessary background for understanding data distributions and analytical methods. The course culminates in data visualization techniques using Matplotlib and Seaborn, demonstrating how to effectively communicate insights through graphical representations.Throughout the course, numerous code samples and practical examples are provided, reinforcing learning and offering hands-on experience. Companion files with source code and figures are available online, supporting the learning journey. This comprehensive guide equips both beginners and seasoned professionals with the skills needed to excel in data analytics.

Python 3 and Data Visualization. Mastering Graphics and Data Manipulation with Python

Mercury Learning and Information, Oswald Campesato

Python 3 and Data Visualization provides an in-depth exploration of Python 3 programming and data visualization techniques. The course begins with an introduction to Python, covering essential topics from basic data types and loops to advanced constructs such as dictionaries and matrices. This foundation prepares readers for the next section, which focuses on NumPy and its powerful array operations, seamlessly leading into data visualization using prominent libraries like Matplotlib.Chapter 6 delves into Seaborn's rich visualization tools, providing insights into datasets like Iris and Titanic. The appendix covers additional visualization tools and techniques, including SVG graphics and D3 for dynamic visualizations. The companion files include numerous Python code samples and figures, enhancing the learning experience.From foundational Python concepts to advanced data visualization techniques, this course serves as a comprehensive resource for both beginners and seasoned professionals, equipping them with the necessary skills to effectively visualize data.

Python 3 and Machine Learning Using ChatGPT / GPT-4. Harness the Power of Python, Machine Learning, and Generative AI

Mercury Learning and Information, Oswald Campesato

This book bridges the gap between theoretical knowledge and practical application in Python programming, machine learning, and using ChatGPT-4 in data science. It starts with an introduction to Pandas for data manipulation and analysis. The book then explores various machine learning classifiers, from kNN to SVMs. Later chapters cover GPT-4's capabilities, enhancing linear regression analysis, and using ChatGPT in data visualization, including AI apps, GANs, and DALL-E.The journey begins with mastering Pandas and machine learning fundamentals. It progresses to applying GPT-4 in linear regression and machine learning classifiers. The final chapters focus on using ChatGPT for data visualization, making complex results accessible and understandable.Understanding these concepts is crucial for modern data scientists. This book transitions readers from basic Python programming to advanced applications of ChatGPT-4 in data science. Companion files with source code, datasets, and figures enhance learning, making this an essential resource for mastering Python, machine learning, and AI-driven data visualization.

Python 3 Data Visualization Using ChatGPT / GPT-4. Master Python Visualization Techniques with AI Integration

Mercury Learning and Information, Oswald Campesato

This book teaches Python 3 programming and data visualization, exploring cutting-edge techniques with ChatGPT/GPT-4 for generating compelling visuals. It starts with Python essentials, covering basic data types, loops, functions, and advanced constructs like dictionaries and matrices. The journey progresses to NumPy's array operations and data visualization using libraries such as Matplotlib and Seaborn. The book also covers tools like SVG graphics and D3 for dynamic visualizations.The course begins with foundational Python concepts, moves into NumPy and data visualization with Pandas, Matplotlib, and Seaborn. Advanced chapters explore ChatGPT and GPT-4, demonstrating their use in creating data visualizations from datasets like the Titanic. Each chapter builds on the previous one, ensuring a comprehensive understanding of Python and visualization techniques.These concepts are crucial for Python practitioners, data scientists, and anyone in data analytics. This book transitions readers from basic Python programming to advanced data visualization, blending theoretical knowledge with practical skills. Companion files with code, datasets, and figures enhance learning, making this an essential resource for mastering Python and data visualization.

Python 3 Data Visualization Using Google Gemini. Unlock the Power of Python and Google Gemini for Stunning Data Visualizations

Mercury Learning and Information, Oswald Campesato

This book teaches Python 3 programming and data visualization, exploring cutting-edge techniques with ChatGPT/GPT-4 for generating compelling visuals. It starts with Python essentials, covering basic data types, loops, functions, and advanced constructs like dictionaries and matrices. The journey progresses to NumPy's array operations and data visualization using libraries such as Matplotlib and Seaborn. The book also covers tools like SVG graphics and D3 for dynamic visualizations.The course begins with foundational Python concepts, moves into NumPy and data visualization with Pandas, Matplotlib, and Seaborn. Advanced chapters explore ChatGPT and GPT-4, demonstrating their use in creating data visualizations from datasets like the Titanic. Each chapter builds on the previous one, ensuring a comprehensive understanding of Python and visualization techniques.These concepts are crucial for Python practitioners, data scientists, and anyone in data analytics. This book transitions readers from basic Python programming to advanced data visualization, blending theoretical knowledge with practical skills. Companion files with code, datasets, and figures enhance learning, making this an essential resource for mastering Python and data visualization.

Python 3 for Machine Learning. Harness the Power of Python for Advanced Machine Learning Projects

Mercury Learning and Information, Oswald Campesato

This book introduces basic Python 3 programming concepts related to machine learning. The first four chapters provide a fast-paced introduction to Python 3, NumPy, and Pandas. The fifth chapter covers fundamental machine learning concepts. The sixth chapter dives into machine learning classifiers, such as logistic regression, k-NN, decision trees, random forests, and SVMs. The final chapter includes material on natural language processing (NLP) and reinforcement learning (RL). Keras-based code samples supplement the theoretical discussion.The course begins with Python basics, including conditional logic, loops, functions, and collections. It then explores data manipulation with NumPy and Pandas. The journey continues with an introduction to machine learning, focusing on essential concepts and classifiers. Advanced topics like NLP and RL are covered, ensuring a comprehensive understanding of machine learning.These concepts are crucial for developing machine learning applications. This book transitions readers from basic Python programming to advanced machine learning techniques, blending theory with practical skills. Appendices for regular expressions, Keras, and TensorFlow 2, along with companion files, enhance learning, making this an essential resource for mastering Python and machine learning.

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.

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.

Python for Programmers. A Comprehensive Guide for Intermediate to Advanced Python Programmers and Developers

Mercury Learning and Information, Oswald Campesato

This book is designed for developers with little to no experience in Python or Pandas, providing a fast-paced introduction to Python programming and practical solutions to various tasks. The journey begins with a quick tour of basic Python 3, followed by a deep dive into loops and conditional logic. The text covers data structures extensively, and includes tasks involving strings and arrays.As the reader progresses, object-oriented programming concepts are introduced with illustrative code samples, along with an exploration of recursion and fundamental topics in combinatorics. An appendix provides an introduction to Pandas, equipping readers with essential tools for data manipulation and analysis.This book offers a comprehensive yet concise learning path, reinforced by numerous code samples and companion files available for download. It is an invaluable resource for beginners seeking to master Python and Pandas, providing a solid foundation for further exploration in programming and data science.

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 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.

SQL Pocket Primer. A Comprehensive Guide to SQL and MySQL for Data Professionals

Mercury Learning and Information, Oswald Campesato

As part of the best-selling Pocket Primer series, this book is designed for data scientists and machine learning engineers seeking to deepen their SQL knowledge using MySQL as the primary RDBMS. It features Python-based code samples for accessing data from MySQL tables in Pandas data frames and Java-based samples for data access in MySQL, along with handling XML and JSON documents.The book also introduces NoSQL, with an overview of MongoDB, and SQLite, an open-source RDBMS for mobile devices. The final section covers diverse topics like normalization, schemas, database optimization, and performance. This comprehensive approach ensures a well-rounded understanding of SQL and related technologies, enhancing data manipulation and database management skills.Numerous code samples and listings throughout the book support the various topics covered. Companion files with source code and figures are available from the publisher, making this an essential resource for advancing your SQL proficiency and database management expertise.

TensorFlow 2 Pocket Primer. A Quick Reference Guide for TensorFlow 2 Developers

Mercury Learning and Information, Oswald Campesato

As part of the best-selling *Pocket Primer* series, this book introduces beginners to basic machine learning algorithms using TensorFlow 2. It provides a fast-paced introduction to TensorFlow, covering core features and machine learning basics with Python code samples. An appendix includes Keras-based code samples and explores MLPs, CNNs, RNNs, and LSTMs. The chapters illustrate how to solve various tasks, encouraging further reading to deepen your knowledge.The journey begins with an introduction to TensorFlow 2, followed by essential APIs and datasets. You'll explore linear regression and classifiers, learning to apply TensorFlow to practical problems. The comprehensive appendix covers advanced topics like NLPs and deep learning architectures, enhancing your understanding of machine learning.Understanding these concepts is crucial for modern AI applications. This book transitions readers from basic TensorFlow use to advanced machine learning techniques, blending theory with practical examples. Companion files with source code and figures enhance learning, making this an essential resource for mastering TensorFlow and machine learning.