Название: Python Essentials 1: The Official OpenEDG Python Institute beginners course with practical exercises – learn the basics of Python in seven days and pass the PCEP certification exam
Автор: Тhе ОреnЕDG Руthоn Institutе
Издательство: Тhе ОреnЕDG Руthоn Institutе
Серия: Python Essentials
Год: 2023
Страниц: 515
Язык: английский
Формат: pdf, epub
Размер: 10.1 MB

Unleash Your Potential and Transform Your Life with Python Essentials 1 — Your Gateway to Career Success! Are you stuck in a job that drains your energy? Do you yearn to achieve your full potential, all while earning a better income? Are you overwhelmed by repetitive, tedious tasks that eat away at your free time and joy? Step into a transformative learning experience with Python Essentials 1 — a meticulously crafted course that empowers you to claim the life you've always wanted. This book doesn't just teach you Python; it opens the door to a thrilling career as a professional programmer, and an opportunity to become part of a global community of people just like you. Every moment you invest in learning equips you to tackle real-world challenges with Python, setting you on a fast-track to becoming an in-demand professional. Python is one of the fastest growing programming languages in the world, and is used in almost every sector and industry, from gaming, to medicine, to nuclear physics. It is essential for any would-be programmer to have at least a foundational knowledge of Python. Luckily, Python is also one of the easiest programming languages to learn. With its focus on real-world words and syntax, a beginner learner of Python can start writing simple programs within minutes. All the code examples in the book have been tested on Python 3.4, 3.6, 3.7, 3.8, and 3.9, and should work with any subsequent versions of Python 3.x.

Название: Software Engineering for Data Scientists: From Notebooks to Scalable Systems (Final)
Автор: Саthеrinе Nеlsоn
Издательство: O’Reilly Media, Inc.
Год: 2024
Страниц: 400
Язык: английский
Формат: pdf, epub (true)
Размер: 11.2 MB

Data Science happens in code. The ability to write reproducible, robust, scaleable code is key to a data science project's success—and is absolutely essential for those working with production code. This practical book bridges the gap between data science and software engineering,and clearly explains how to apply the best practices from software engineering to Data Science. All the code examples in this book are written in Python, and many of the chapters describe Python-specific tools. In recent years, Python has become the most popular programming language for Data Science. Python has an extremely solid set of open source libraries for data science, with good backing and a healthy community of maintainers. Large trend-setting companies have chosen Python for their main ML frameworks, including TensorFlow (Google) and PyTorch (Meta). Because of this, Python appears to be especially popular among data scientists working on production Machine Learning code, where good coding skills are particularly important. In my experience, the Python community has been friendly and welcoming, with many excellent events that have helped me improve my skills. It’s my preferred programming language, so it was an easy choice for this book. This book is aimed at data scientists, but people working in closely related fields such as data analysts, Machine Learning (ML) engineers, and data engineers will also find it useful.

Название: Data Storytelling with Generative AI: using Python and Altair (MEAP v5)
Автор: Аngеliса Lо Duса
Издательство: Manning Publications
Год: 2024
Страниц: 425
Язык: английский
Формат: pdf, epub
Размер: 46.5 MB

Great data presentations tell a story. Learn how to organize, visualize, and present data using Python, generative AI, and the cutting-edge Altair data analysis toolkit. Data Storytelling with Python Altair and Generative AI teaches you how to turn raw data into effective, insightful data stories. You’ll learn exactly what goes into an effective data story, then combine your Python data skills with the Altair library and AI tools to rapidly create amazing visualizations. Your bosses and decision-makers will love your new presentations—and you’ll love how quick Generative AI makes the whole process! Python is the second ingredient of this book. As a data journalism professor, I have experimented with many Python libraries for data visualization, such as Matplotlib, Plotly, and Seaborn. However, at the end of my experiments, I realized that the simplest library for data visualization is Altair. Unlike other libraries, Altair is declarative, thus enabling you only to focus on the output of your data visualization. Thus, this book will focus on Altair to build data visualization. Although you can find many materials on the web about Altair, they focus only on how to build raw charts. In this book, you’ll learn how to build data stories with Altair and not simply raw charts. Last but not least, you’ll learn how to use Generative AI tools for data storytelling. This book won’t focus on Generative AI concepts and theory. Instead, you will learn how to apply Generative AI tools, including GitHub Copilot, ChatGPT, and DALL-E, to data storytelling.

Название: Math and Architectures of Deep Learning (Final Release)
Автор: Кrishnеndu Сhаudhurу
Издательство: Manning Publications
Год: 2024
Страниц: 553
Язык: английский
Формат: pdf (true)
Размер: 75.9 MB

Shine a spotlight into the Deep Learning "black box". This comprehensive and detailed guide reveals the mathematical and architectural concepts behind Deep Learning models, so you can customize, maintain, and explain them more effectively. Deep Learning can’t be learned by going through a small fragmented set of simplified recipes from which all math has been scrubbed out. This is a mathematical topic and mastery requires understanding the math along with the programming. What is needed is a resource which presents this topic with the requisite amount of math—no more and no less—with the connection between the Deep Learning and math explicitly spelled out. This is exactly what this book strives to provide with its dual presentation of the math and corresponding PyTorch code snippets. Readers need to know Python and the basics of algebra and calculus.

Название: Basic and Advance: Python Programming
Автор: Аgуа Rаm Vеrmа, Yаtеndrа Кumаr, Sаg Rаm Vеrmа
Издательство: NIPA GENX Electronic Resources & Solutions P. LTD.
Год: 2023
Страниц: 240
Язык: английский
Формат: pdf (true)
Размер: 15.9 MB

This book will introduce you to the Python programming language. It's aimed at beginning programmers, but even if you have written programs before and just want to add Python to your list of languages. Python is a high-level, interpreted, and general-purpose dynamic programming language that focuses on code readability. It has fewer steps when compared to Java and C. It was founded in 1991 by developer Guido Van Rossum. Python ranks among the most popular and fastest-growing languages in the world. Python is a powerful, flexible, and easy-to-use language. In addition, the community is very active there. It is used in many organizations as it supports multiple programming paradigms. It also performs automatic memory management. The first eight chapters explain Python's basic and you should read them in order. The later chapters show how Python is used in specific application areas such as the web, database, networks, and so on, read them in any order you like. Chapter 1 this chapter explain different basic topic related Python programming. Chapter 2 in this chapter define Python operator. Chapter 3 in this chapter define looping statement. Chapter 4 in this chapter define Python function.

Название: Learn Python the Hard Way, 5th Edition
Автор: Zеd А. Shаw
Издательство: Addison-Wesley Professional/Pearson Education
Год: 2024
Страниц: 348
Язык: английский
Формат: pdf (true), epub
Размер: 10.1 MB

You Will Learn Python! Zed Shaw has created the world's most reliable system for learning Python. Follow it and you will succeed–just like the millions of beginners Zed has taught to date! You bring the discipline, persistence, and attention; the author supplies the masterful knowledge you need to succeed. In Learn Python the Hard Way, Fifth Edition, you'll learn Python by working through 60 lovingly crafted exercises. Read them. Type in the code. Run it. Fix your mistakes. Repeat. As you do, you'll learn how a computer works, how to solve problems, and how to enjoy programming ... even when it's driving you crazy. This book instructs you in Python by slowly building and establishing skills through techniques such as practice and memorization, then applying them to increasingly difficult problems. By the end of the book you will have the tools needed to begin learning more complex programming topics. I like to tell people that my book gives you your “programming black belt.” What this means is that you know the basics well enough to now start learning programming. If you work hard, take your time, and build these skills, you will learn to code.

Название: PROC FCMP User-Defined Functions: An Introduction to the SAS Function Compiler
Автор: Тrоу Маrtin Нughеs
Издательство: SAS Institute Inc.
Год: 2024
Страниц: 425
Язык: английский
Формат: pdf, epub
Размер: 10.1 MB

In PROC FCMP User-Defined Functions, readers are introduced to the SAS Function Compiler, which enables users to create user-defined functions and subroutines. These modular, callable software components complement the diverse array of SAS built-in functions and extend the SAS programming language, creating more building blocks for constructing future software! The book opens by introducing the role of functions in software design and explaining how functions improve software quality characteristics. It then moves on to basic PROC FCMP syntax, including how to define and call user-defined functions. Next, readers learn about the SAS array and hash object, the primary data structures leveraged by PROC FCMP, and how PROC FCMP can manipulate them behind the scenes. Finally, the Python Component Object is introduced, which facilitates the interoperability of SAS and Python. PROC FCMP runs Python functions natively inside a SAS wrapper, which allows open-source functions to be incorporated without needing to be rewritten in SAS. The Python language has become one of the most versatile, widely distributed programming languages in the world. Can I say that in a SAS book? Yes! Because the SAS Institute is not only aware of this success—they are capitalizing on it, as they continue their commitment to platform and software interoperability. The FCMP procedure helps facilitate the integration of Python and the SAS application through the Python Component Object, which enables Python functions to be executed inside user-defined SAS functions and subroutines.

Название: Introduction to Data Science: A Python Approach to Concepts, Techniques and Applications 2nd Edition
Автор: Lаurа Iguаl, Sаnti Sеguі
Издательство: Springer
Серия: Undergraduate Topics in Computer Science
Год: 2024
Страниц: 255
Язык: английский
Формат: pdf
Размер: 10.1 MB

This accessible and classroom-tested textbook/reference presents an introduction to the fundamentals of the interdisciplinary field of Data Science. The coverage spans key concepts from statistics, Machine Learning/Deep Learning and responsible Data Science, useful techniques for network analysis and natural language processing (NLP), and practical applications of Data Science such as recommender systems or sentiment analysis. This book includes three different kinds of chapters. The first kind is about Python extensions. Python was originally designed to have a minimum number of data objects (int, float, string, etc.); but when dealing with data, it is necessary to extend the native set to more complex objects such as (NumPy) numerical arrays or (Pandas) data frames. The second kind of chapter includes techniques and modules to perform statistical analysis and Machine Learning. Finally, there are some chapters that describe several applications of Data Science, such as building recommenders or sentiment analysis. The composition of these chapters was chosen to offer a panoramic view of the Data Science field, but we encourage the reader to delve deeper into these topics and to explore those topics that have not been covered: big data analytics and more advanced mathematical and statistical methods (e.g., Bayesian statistics). This book is addressed to upper-tier undergraduate and beginning graduate students from technical disciplines. Moreover, this book is also addressed to professional audiences following continuous education short courses and to researchers from diverse areas following self-study courses. Basic skills in computer science, mathematics, and statistics are required. Code programming in Python is of benefit.

Название: Causal AI (MEAP v9)
Автор: Rоbеrt Оsаzuwа Nеss
Издательство: Manning Publications
Год: 2024
Страниц: 576
Язык: английский
Формат: epub
Размер: 39.0 MB

How do you know what might have happened, had you done things differently? Causal Machine Learning gives you the insight you need to make predictions and control outcomes based on causal relationships instead of pure correlation, so you can make precise and timely interventions. Causal Machine Learning is a major milestone in Machine Learning, allowing AI models to make accurate predictions based on causes rather than just correlations. Causal techniques help you make models that are more robust, explainable, and fair, and have a wide range of applications, from improving recommendation engines to perfecting self-driving cars. Causal AI teaches you how to build Machine Learning and Deep Learning models that implement causal reasoning. Discover why leading AI engineers are so excited by causal reasoning, and develop a high-level understanding of this next major trend in AI. New techniques are demonstrated with example models for solving industry-relevant problems. You’ll learn about causality for recommendations; causal modeling of online conversions; and uplift, attribution, and churn modeling. Each technique is tested against a common set of problems, data, and Python libraries, so you can compare and contrast which will work best for you. For data scientists and Machine Learning engineers. A familiarity with probability and statistics will be helpful, but not essential, to begin this guide. Examples in Python.

Название: Deep Learning Models: A Practical Approach for Hands-On Professionals
Автор: Jоnаh Gаmbа
Издательство: Springer
Год: 2024
Страниц: 211
Язык: английский
Формат: pdf (true), epub
Размер: 65.3 MB

This book focuses on and prioritizes a practical approach, minimizing theoretical concepts to deliver algorithms effectively. With Deep Learning emerging as a vibrant field of research and development in numerous industrial applications, there is a pressing need for accessible resources that provide comprehensive examples and quick guidance. Unfortunately, many existing books on the market tend to emphasize theoretical aspects, leaving newcomers scrambling for practical guidance. This book takes a different approach by focusing on practicality while keeping theoretical concepts to a necessary minimum. The book begins by laying a foundation of basic information on Deep Learning, gradually delving into the subject matter to explain and illustrate the limitations of existing algorithms. A dedicated chapter is allocated to evaluating the performance of multiple algorithms on specific datasets, highlighting techniques and strategies that can address real-world challenges when Deep Learning is employed. The Chapter 2 introduce some of the concepts needed to start building Deep Learning models in Python. The chapter starts with basic principles related to data manipulation and ends with explanation on how to set up the modelling environment. It is expected that the reader is familiar with some high-level programming concepts which are very easy to acquire within a short space of time. Deep Learning models mostly deal with vectors and matrices as we know them from linear algebra. This book is designed to equip professionals with the necessary skills to thrive in the active field of Deep Learning, where it has the potential to revolutionize traditional problem-solving approaches.