Название: Introduction to Classifier Performance Analysis with R
Автор: Sutaip L.C. Saw
Издательство: CRC Press
Серия: Data Science Series
Год: 2025
Страниц: 222
Язык: английский
Формат: pdf (true), epub
Размер: 10.1 MB

Classification problems are common in business, medicine, science, engineering and other sectors of the economy. Data scientists and Machine Learning professionals solve these problems through the use of classifiers. Choosing one of these data driven classification algorithms for a given problem is a challenging task. An important aspect involved in this task is classifier performance analysis (CPA). Introduction to Classifier Performance Analysis with R provides an introductory account of commonly used CPA techniques for binary and multiclass problems, and use of the R software system to accomplish the analysis. Coverage draws on the extensive literature available on the subject, including descriptive and inferential approaches to CPA. Exercises are included at the end of each chapter to reinforce learning. This book is for those who want a reasonably complete (at least at an introductory level) and up-to-date coverage on the analysis of classification algorithms through the use of performance measures and curves. It attempts to synthesize useful material from the vast published literature on the subject. Another motivation for the book is to show how R can be used to perform the required analysis. As computational software, R has already demonstrated its excellence to a large international community of users. Its appeal is further enhanced by recently developed packages and meta-packages for Data Science, Machine Learning, and classification performance analysis in particular. This is a useful resource for upper level undergraduate and masters level students in Data Science, Machine Learning and related disciplines. Practitioners interested in learning how to use R to evaluate classifier performance can also potentially benefit from the book. The material and references in the book can also serve the needs of researchers in CPA.

Название: Solve Any Data Analysis Problem: Eight projects that show you how (MEAP v8)
Автор: David Asboth
Издательство: Manning Publications
Год: 2024
Страниц: 562
Язык: английский
Формат: pdf (true)
Размер: 91.8 MB

Complete eight Data Science projects that lock in important real world skills–along with a practical process you can use to learn any new technique quickly and efficiently. Solve Any Data Analysis Problem guides you through eight common scenarios you'll encounter as a data scientist or analyst. As you explore each project, you’ll also master a proven process for quickly learning new skills developed by author and Half Stack Data Science podcast host David Asboth. In Solve Any Data Analysis Problem you’ll learn how to shift the way you think about data from the structured clean problems you get in a classroom, book, or bootcamp to the messy open-ended challenges of the workplace. As you work through eight problems you’ll see over and over on the job, you’ll discover a solutions-driven methodology that’s focused on getting results. You’ll learn how to determine a minimum viable answer for your stakeholders, identify and obtain the data you need to deliver, and reliably present and iterate on your findings. Which tool you are comfortable doing the above in does not matter. I will provide example solutions to the projects in the book using Python, but the focus will be on problem-solving, not the specifics of the Python programming language. Appendix A gives you a quick overview of the skills and tools in the basic Data Science toolkit. If you need to brush up on anything, we’ve linked to some useful resources you can use to get up to speed. As for most solutions I provide, the code itself will be written in Python, primarily using the Pandas library. While code snippets will be used to explain the example solution, I will focus discussions on the conceptual solution and less on the specifics of the code. The solution will be in three parts: setting up the problem statement and the data, creating the first iteration of a solution, and a third part to review the work and decide on further steps.

Название: Starting Data Analytics with Generative AI and Python
Автор: Artur Guja, Marlena Siwiak, Marian Siwiak
Издательство: Manning Publications
Год: 2025
Страниц: 362
Язык: английский
Формат: pdf (true)
Размер: 15.5 MB

Accelerate your mastery of data analytics with the power of ChatGPT. Whether you’re brand new to data analysis or an experienced pro looking to do more work, faster, Starting Data Analytics with Generative AI and Python is here to help simplify and speed up your data analysis! Written by a pair of world-class data scientists and an experienced risk manager, the book concentrates on the practical analytics tasks you'll do every day. In Starting Data Analytics with Generative AI and Python you’ll learn how to improve your coding efficiency, generate new analytical approaches, and fine-tune data pipelines—all assisted by AI tools like ChatGPT. For each step in the data process, you’ll discover how ChatGPT can implement data techniques from simple plain-English prompts. Plus, you’ll develop a vital intuition about the risks and errors that still come with these tools. If you have basic knowledge of data analysis, this book will show you how to use ChatGPT to accelerate your essential data analytics work. This speed-up can be amazing: the authors report needing one third or even one quarter the time they needed before. You’ll find reliable and practical advice that works on the job. Improve problem exploration, generate new analytical approaches, and fine-tune your data pipelines—all while developing an intuition about the risks and errors that still come with AI tools. Assuming only that you know the foundations, this friendly book guides you through the entire analysis process—from gathering and preparing raw data, data cleaning, generating code-based solutions, selecting statistical tools, and finally creating effective data presentations. With clearly-explained prompts to extract, interpret, and present data, it will raise your skills to a whole different level.

Название: Python Recipes for Earth Sciences, Second Edition
Автор: Martin H. Trauth
Издательство: Springer
Год: 2024
Страниц: 500
Язык: английский
Формат: pdf (true)
Размер: 20.1 MB

Python is used in a wide range of geoscientific applications, such as in processing images for remote sensing, in generating and processing digital elevation models, and in analyzing time series. This book introduces methods of data analysis in the geosciences using Python that include basic statistics for univariate, bivariate, and multivariate data sets, time series analysis, and signal processing; the analysis of spatial and directional data; and image analysis. The text includes numerous examples that demonstrate how Python can be used on data sets from the earth sciences. Codes are available online through GitHub. The book Python Recipes for Earth Sciences is designed to help undergraduate and postgraduate students, doctoral students, post-doctoral researchers, and professionals alike in finding quick solutions to common data analysis problems in the earth sciences. It provides a minimal amount of theoretical background and demonstrates the application of all described methods via examples. The present book contains Python scripts that can be used to solve typical problems in the earth sciences via simple statistics, time series analysis, geostatistics, and image processing. It also demonstrates the application of selected advanced techniques of data analysis, such as nonlinear time series analysis, adaptive filtering, bootstrapping, and terrain analysis. In order to derive the maximum benefit from this book, the reader will need to have access to the Python software and be able to execute the recipes while reading the book. The Python recipes yield various graphs on the screen that are not shown in the printed book. The tutorial-style book does, however, contain numerous figures, thereby making it possible to go through the text without actually running Python on a computer. I developed the recipes using Python 3.8.8, though most recipes will also work with earlier software releases, but not with Python 2.

Название: Lead Developer Career Guide (Final Release)
Автор: Shelley Benhoff
Издательство: Manning Publications
Год: 2024
Страниц: 320
Язык: английский
Формат: pdf (true)
Размер: 29.1 MB

Learn the essential skills needed to become a successful lead developer, with expert advice on mentoring teams, handling clients and project managers, and keeping your cool in emergencies. The Lead Developer Career Guide teaches you how to transition from an individual contributor to a thriving lead developer. It's packed with insider tips, tricks, and strategies drawn from author Shelley Benhoff's 25-year career in technology, providing vital insights for navigating the unique challenges and expectations of the lead developer role. This one-of-a-kind book demonstrates how critical thinking and communication skills can elevate your career. To be a successful Lead Developer you’ll need more than just technical expertise. You’ll be responsible for everything from facilitating architectural decisions that satisfy all stakeholders to mentoring your fellow developers. And you’ll be on the hook for delivering great software on time and under budget. Are you ready for the challenge? This book will help get you there! The Lead Developer Career Guide provides the techniques and wisdom you need to transition from individual contributor to lead developer. You’ll learn how to collaborate effectively with executive leadership and project managers, present elegant solutions to clients, and think quickly in those inevitable emergencies. When all eyes are on you, this book will ensure you know exactly what to do. For aspiring lead developers.

Название: Python Logging: Auditing and Debugging Through
Автор: Michael Driscoll
Издательство: Teach Me Python LLC/Leanpub
Год: 2024-10-03
Страниц: 154
Язык: английский
Формат: pdf (true), epub
Размер: 10.1 MB

What does every new developer do when they are first learning to program? They print out strings to their terminal. It's how we learn! But printing out to the terminal isn't what you do with most professional applications. In those cases, you log in. Sometimes, you log into multiple locations at once. These logs may serve as an audit trail for compliance purposes or help the engineers debug what went wrong. Python Logging teaches you how to log in the Python programming language. Python is one of the most popular programming languages in the world. Python comes with a logging module that makes logging easy. You don’t need to be a programmer or engineer to use this book, but it helps. The primary target is people who want to learn about what logging in Python is and how to use it effectively. If you understand the basics of Python, then you’ll be even better off!

Название: Surviving Other People's APIs
Автор: Phil Sturgeon, Mike Bifulco
Издательство: Leanpub
Год: 2023-04-17
Страниц: 173
Язык: английский
Формат: pdf (true), epub
Размер: 15.9 MB

"Just pull that from the API" they said. "It'll be easy!" they said. How can you build the best web/mobile/client-side application, when the APIs you need to work with are poorly documented, struggle with sketchy performance, or generally make you feel like you're on a dangerous expidition into an ancient tomb, with hidden booby traps, doors operated by archaic runes, where one wrong move triggers swinging battering rambs to wreck your day into a wall of spikes. This whole premise of an API client-server relationship is inherently fraught with danger, because going over the network for anything can lead to unexpected errors, unexpected change, connection problems... hell a rat could have chewed through a cable. This book aims to help you not just react to these problems, but ideally prempt them and build an application that laughs in the face of danger. Just like with Build APIs You Won’t Hate, this book will take a non-academic, easy-to-read approach to some pretty complex topics around HTTP interactions, versioning, client-caching, state management, differences between how you interact with RPC, REST and GraphQL, using JSON Schema for local validation, and all sorts of other awesome stuff that nobody ever bothered to mention to you.

Название: Hands-On Prescriptive Analytics: Optimizing Your Decisions with Python (Early Release)
Автор: Walter R. Paczkowski
Издательство: O’Reilly Media, Inc.
Год: 2024-10-04
Страниц: 300
Язык: английский
Формат: epub
Размер: 16.2 MB

Business decisions in any context—operational, tactical, or strategic—can have considerable consequences. Whether the outcome is positive and rewarding or negative and damaging to the business, its employees, and stakeholders is unknown when action is approved. These decisions are usually made under the proverbial cloud of uncertainty. With this practical guide, data analysts, data scientists, and business analysts will learn why and how maximizing positive consequences and minimizing negative ones requires three forms of rich information: Descriptive analytics explores the results from an action—what has already happened. Predictive analytics focuses on what could happen. The third, prescriptive analytics, informs us what should happen in the future. While all three are important for decision-makers, the primary focus of this book is on the third: prescriptive analytics. This book is intended for people involved in demand measurement and forecasting; predictive modeling; pricing analytics including elasticity estimation; customer satisfaction assessment; market and advertisement research; new product development and research; capital investment decisions; and any place where these analyses are input into major decisions at the operational, tactical, and strategic levels. This book will provide background for Prescriptive Analytics by explaining the intuition underlying analytic concepts; developing the necessary mathematical and statistical analytic principles; demonstrating concepts using Python in JupyterLab notebooks; and illustrating analytical concepts with use-cases.

Название: Build APIs You Won't Hate : Everyone and their dog wants an API, so you should probably learn how to build them
Автор: Phil Sturgeon
Издательство: Leanpub
Год: 2016-03-30
Страниц: 194
Язык: английский
Формат: pdf (true), epub + Extras
Размер: 10.1 MB

Tasked with building an API for your company but don't have a clue where to start? Taken over an existing API and hate it? Built your own API and still hate it? This book is for you. API development is becoming increasingly common for server-side developers thanks to the rise of front-end jаvascript frameworks, iPhone applications, and API-centric architectures. It might seem like grabbing stuff from a data source and shoving it out as JSON would be easy, but surviving changes in business logic, database schema updates, new features, or deprecated endpoints can be a nightmare. By passing on some best practices and general good advice you can hit the ground running with API development, combined with some horror stories and how they were overcome/avoided/averted. This book will discuss the theory of designing and building APIs in any language or framework, with this theory applied in PHP-based examples. This book will discuss the theory of designing and building APIs in any language or framework. This theory will be applied in examples built mostly in PHP, with some Ruby and Python too. The book will not be too code-heavy regardless, since reading code is no fun. By the end of this book, you will have built an API that can create, read, update, delete things, handle searching, and do everything else a good Hypermedia API needs to do.

Название: System Innovation for an Artificial Intelligence Era: Applied System Innovation X
Автор: Artde D.K.T. Lam, Stephen D. Prior, Siu-Tsen Shen, Sheng-Joue Young
Издательство: CRC Press
Год: 2025
Страниц: 427
Язык: английский
Формат: pdf (true)
Размер: 16.2 MB

The book aims to provide an integrated communication platform for researchers from a wide range of topics including information technology, communication science, applied mathematics, Computer Science, advanced material science, and engineering. The maturing Artificial Intelligence (AI) technology can produce diverse images through text prompts, useful for scene creation, conceptual modeling, and virtual illustrations. As AI art gains traction, artists increasingly employ AI image generation tools to spark or augment their creative process. This study aims to compare various AI image generation tools’ applications across fields, exploring factors like image composition and user evaluations to aid tool selection. Five representative AI image generation tools were chosen: Midjourney, Leonardo.AI, Deep AI, Stable Diffusion, and Playground AI. Through literature review, factors such as clarity, color, contrast, lighting, and composition were identified as criteria for judging image quality. Differences among these factors across tools influence generated image style, quality, and diversity.A questionnaire survey was conducted to understand image characteristics generated by different AI tools, allowing respondents to evaluate them. Subjects’ preferences and evaluations of AI-generated images may vary, influenced by their educational background and personal inclinations. A comprehensive literature review highlights differences and similarities in AI image generation tool applications across fields, delineating their respective advantages and limitations. This study’s findings offer insights into AI tool applicability across domains and inform users’ tool selection.