Python

Learning Python for Forensics. Leverage the power of Python in forensic investigations - Second Edition

Preston Miller, Chapin Bryce

Digital forensics plays an integral role in solving complex cybercrimes and helping organizations make sense of cybersecurity incidents. This second edition of Learning Python for Forensics illustrates how Python can be used to support these digital investigations and permits the examiner to automate the parsing of forensic artifacts to spend more time examining actionable data.The second edition of Learning Python for Forensics will illustrate how to develop Python scripts using an iterative design. Further, it demonstrates how to leverage the various built-in and community-sourced forensics scripts and libraries available for Python today. This book will help strengthen your analysis skills and efficiency as you creatively solve real-world problems through instruction-based tutorials.By the end of this book, you will build a collection of Python scripts capable of investigating an array of forensic artifacts and master the skills of extracting metadata and parsing complex data structures into actionable reports. Most importantly, you will have developed a foundation upon which to build as you continue to learn Python and enhance your efficacy as an investigator.

Learning Python. Learn to code like a professional with Python - an open source, versatile, and powerful programming language

Fabrizio Romano

Learning Python has a dynamic and varied nature. It reads easily and lays a good foundation for those who are interested in digging deeper. It has a practical and example-oriented approach through which both the introductory and the advanced topics are explained. Starting with the fundamentals of programming and Python, it ends by exploring very different topics, like GUIs, web apps and data science. The book takes you all the way to creating a fully fledged application.The book begins by exploring the essentials of programming, data structures and teaches you how to manipulate them. It then moves on to controlling the flow of a program and writing reusable and error proof code. You will then explore different programming paradigms that will allow you to find the best approach to any situation, and also learn how to perform performance optimization as well as effective debugging. Throughout, the book steers you through the various types of applications, and it concludes with a complete mini website built upon all the concepts that you learned.

Learning Python Networking. A complete guide to build and deploy strong networking capabilities using Python 3.7 and Ansible - Second Edition

José Manuel Ortega, Dr. M. O. Faruque...

Network programming has always been a demanding task. With full-featured and well-documented libraries all the way up the stack, Python makes network programming the enjoyable experience it should be.Starting with a walk through of today's major networking protocols, through this book, you'll learn how to employ Python for network programming, how to request and retrieve web resources, and how to extract data in major formats over the web. You will utilize Python for emailing using different protocols, and you'll interact with remote systems and IP and DNS networking. You will cover the connection of networking devices and configuration using Python 3.7, along with cloud-based network management tasks using Python.As the book progresses, socket programming will be covered, followed by how to design servers, and the pros and cons of multithreaded and event-driven architectures. You'll develop practical clientside applications, including web API clients, email clients, SSH, and FTP. These applications will also be implemented through existing web application frameworks.

Learning Python Web Penetration Testing. Automate web penetration testing activities using Python

Christian Martorella

Web penetration testing is the use of tools and code to attack a website or web app in order to assess its vulnerability to external threats. While there are an increasing number of sophisticated, ready-made tools to scan systems for vulnerabilities, the use of Python allows you to write system-specific scripts, or alter and extend existing testing tools to find, exploit, and record as many security weaknesses as possible. Learning Python Web Penetration Testing will walk you through the web application penetration testing methodology, showing you how to write your own tools with Python for each activity throughout the process. The book begins by emphasizing the importance of knowing how to write your own tools with Python for web application penetration testing. You will then learn to interact with a web application using Python, understand the anatomy of an HTTP request, URL, headers and message body, and later create a script to perform a request, and interpret the response and its headers. As you make your way through the book, you will write a web crawler using Python and the Scrappy library. The book will also help you to develop a tool to perform brute force attacks in different parts of the web application. You will then discover more on detecting and exploiting SQL injection vulnerabilities. By the end of this book, you will have successfully created an HTTP proxy based on the mitmproxy tool.

Learning R for Geospatial Analysis. Leverage the power of R to elegantly manage crucial geospatial analysis tasks

Michael Dorman

This book is intended for anyone who wants to learn how to efficiently analyze geospatial data with R, including GIS analysts, researchers, educators, and students who work with spatial data and who are interested in expanding their capabilities through programming. The book assumes familiarity with the basic geographic information concepts (such as spatial coordinates), but no prior experience with R and/or programming is required. By focusing on R exclusively, you will not need to depend on any external software—a working installation of R is all that is necessary to begin.

Learning Robotics using Python. Design, simulate, program, and prototype an autonomous mobile robot using ROS, OpenCV, PCL, and Python - Second Edition

Lentin Joseph

Robot Operating System (ROS) is one of the most popular robotics software frameworks in research and industry. It has various features for implementing different capabilities in a robot without implementing them from scratch.This book starts by showing you the fundamentals of ROS so you understand the basics of differential robots. Then, you'll learn about robot modeling and how to design and simulate it using ROS. Moving on, we'll design robot hardware and interfacing actuators. Then, you'll learn to configure and program depth sensors and LIDARs using ROS. Finally, you'll create a GUI for your robot using the Qt framework. By the end of this tutorial, you'll have a clear idea of how to integrate and assemble everything into a robot and how to bundle the software package.

Learning Tableau 2025. Leverage Tableau's newest features to revolutionize your data storytelling with AI-enhanced insights - Sixth Edition

Joshua N. Milligan

Tableau 2025 marks a new era in data visualization and analysis, bringing together advanced AI integrations and dynamic user experiences. This sixth edition, written by Tableau Visionary and Zen Master Joshua Miligan, is an end-to-end guide to mastering the latest innovations in Tableau that transform raw data into actionable insights.This edition introduces groundbreaking features like Tableau AI (including Tableau Pulse and Tableau Agent), enhancing your analytical capabilities with AI-driven data exploration and automated insights. With detailed walkthroughs, you’ll learn to build dynamic dashboards that respond to your data in real time and work with sophisticated AI functionalities that predict trends and model scenarios.Whether you're a seasoned data professional or new to Tableau, this book provides the tools you need to leverage Tableau’s full potential. From integrating diverse data sources using the enhanced data model to employing advanced geospatial functions for detailed mapping, every chapter is packed with expert knowledge and practical applications designed to put powerful analytics at your fingertips.*Email sign-up and proof of purchase required

Learning Website Development with Django

Ayman Hourieh

Being a beginner's guide this book has a very simple and clear approach. It is a practical guide that will help you learn the features of Django and help you build a dynamic website using those features. This book is for web developers who want to see how to build a complete site with Web 2.0 features, using the power of a proven and popular development system, but do not necessarily want to learn how a complete framework functions in order to do this. Basic knowledge of Python development is required for this book, but no knowledge of Django is expected.

Lepszy kod w Pythonie. Przewodnik dla aspirujących ekspertów

David Mertz

Wyjdź poza kod Pythona, który "w dużej mierze działa", do kodu, który jest ekspresyjny, solidny i wydajny Python jest zapewne najczęściej używanym językiem programowania na świecie, od nauczania w szkołach podstawowych, przez codzienne tworzenie stron internetowych, aż po najbardziej zaawansowane badania naukowe. Choć każde zadanie w Pythonie można wykonać na wiele różnych sposobów, niektóre z nich są błędne, nieeleganckie lub nieefektywne. Lepszy kod w Pythonie to przewodnik po programowaniu "pythonicznym", zbiór najlepszych praktyk, technik i niuansów, które łatwo przeoczyć, zwłaszcza gdy mamy nawyki zapożyczone z innych języków programowania. Autor David Mertz prezentuje konkretne i zwięzłe przykłady rozmaitych nieporozumień, pułapek i złych nawyków. Wyjaśnia, dlaczego niektóre praktyki są lepsze od innych, bazując na swoim ponad 25-letnim doświadczeniu jako uznany członek społeczności Pythona. Rozdziały ułożone są w kolejności rosnącej według stopnia zaawansowania, a każdy z nich szczegółowo omawia powiązane grupy pojęć. Nie ma znaczenia, czy dopiero zaczynasz pracę z Pythonem, czy też jesteś doświadczonym deweloperem przesuwającym granice swojego kodu w Pythonie. Ta książka jest dla każdego, kto chce być bardziej pythoniczny pisząc lepszy kod w języku Python. Dr David Mertz od ponad 25 lat jest członkiem społeczności Pythona i uczył Pythona naukowców, deweloperów z doświadczeniem w innych językach, a także początkujących programistów. David przez sześć lat był dyrektorem organizacji Python Software Foundation (PSF) i nadal przewodniczy lub należy do różnych grup roboczych PSF. Jest autorem kilku książek technicznych i wygłaszał wykłady na licznych międzynarodowych konferencjach programistycznych. "Moje wysokie oczekiwania wobec tej wciągającej książki o Pythonie zostały przekroczone: oferuje ona mnóstwo cennych informacji dla średnich i zaawansowanych programistów pozwalając im udoskonalić swoje umiejętności w Pythonie, obszernie dzieli się cennym doświadczeniem związanym z wykorzystywaniem i nauczaniem języka, a przy tym jest zwięzła, łatwa w czytaniu i pisana stylem konwersacyjnym. Alex Martelli Używaj właściwego rodzaju pętli w Pythonie Poznaj tajniki obiektów zmiennych i niezmiennych Uzyskaj porady od ekspertów w celu uniknięcia kłopotów w Pythonie Zbadaj zaawansowane tematy dotyczące Pythona Poruszaj się po "atrakcyjnych uciążliwościach", które istnieją w Pythonie Poznaj najbardziej przydatne struktury danych w Pythonie i dowiedz się, jak uniknąć ich niewłaściwego wykorzystywania Unikaj błędów związanych z bezpieczeństwem Poznaj podstawy obliczeń numerycznych, w tym liczby zmiennoprzecinkowe i numeryczne typy danych

Limitless Analytics with Azure Synapse. An end-to-end analytics service for data processing, management, and ingestion for BI and ML

Prashant Kumar Mishra, Mukesh Kumar

Azure Synapse Analytics, which Microsoft describes as the next evolution of Azure SQL Data Warehouse, is a limitless analytics service that brings enterprise data warehousing and big data analytics together. With this book, you'll learn how to discover insights from your data effectively using this platform.The book starts with an overview of Azure Synapse Analytics, its architecture, and how it can be used to improve business intelligence and machine learning capabilities. Next, you'll go on to choose and set up the correct environment for your business problem. You'll also learn a variety of ways to ingest data from various sources and orchestrate the data using transformation techniques offered by Azure Synapse. Later, you'll explore how to handle both relational and non-relational data using the SQL language. As you progress, you'll perform real-time streaming and execute data analysis operations on your data using various languages, before going on to apply ML techniques to derive accurate and granular insights from data. Finally, you'll discover how to protect sensitive data in real time by using security and privacy features.By the end of this Azure book, you'll be able to build end-to-end analytics solutions while focusing on data prep, data management, data warehousing, and AI tasks.

Linear Regression With Python. A Tutorial Introduction to the Mathematics of Regression Analysis

James V Stone

This book offers a detailed yet approachable introduction to linear regression, blending mathematical theory with Python-based practical applications. Beginning with fundamentals, it explains the best-fitting line, regression and causation, and statistical measures like variance, correlation, and the coefficient of determination. Clear examples and Python code ensure readers can connect theory to implementation.As the journey continues, readers explore statistical significance through concepts like t-tests, z-tests, and p-values, understanding how to assess slopes, intercepts, and overall model fit. Advanced chapters cover multivariate regression, introducing matrix formulations, the best-fitting plane, and methods to handle multiple variables. Topics such as Bayesian regression, nonlinear models, and weighted regression are explored in depth, with step-by-step coding guides for hands-on practice.The final sections tie together these techniques with maximum likelihood estimation and practical summaries. Appendices provide resources such as matrix tutorials, key equations, and mathematical symbols. Designed for both beginners and professionals, this book ensures a structured learning experience. Basic mathematical knowledge or foundation is recommended.

LLM Engineer's Handbook. Master the art of engineering large language models from concept to production

Paul Iusztin, Maxime Labonne, Julien Chaumond, Hamza...

Artificial intelligence has undergone rapid advancements, and Large Language Models (LLMs) are at the forefront of this revolution. This LLM book offers insights into designing, training, and deploying LLMs in real-world scenarios by leveraging MLOps best practices. The guide walks you through building an LLM-powered twin that’s cost-effective, scalable, and modular. It moves beyond isolated Jupyter notebooks, focusing on how to build production-grade end-to-end LLM systems.Throughout this book, you will learn data engineering, supervised fine-tuning, and deployment. The hands-on approach to building the LLM Twin use case will help you implement MLOps components in your own projects. You will also explore cutting-edge advancements in the field, including inference optimization, preference alignment, and real-time data processing, making this a vital resource for those looking to apply LLMs in their projects.By the end of this book, you will be proficient in deploying LLMs that solve practical problems while maintaining low-latency and high-availability inference capabilities. Whether you are new to artificial intelligence or an experienced practitioner, this book delivers guidance and practical techniques that will deepen your understanding of LLMs and sharpen your ability to implement them effectively.

LLM Prompt Engineering for Developers. The Art and Science of Unlocking LLMs' True Potential

Aymen El Amri

LLM Prompt Engineering For Developers begins by laying the groundwork with essential principles of natural language processing (NLP), setting the stage for more complex topics. It methodically guides readers through the initial steps of understanding how large language models work, providing a solid foundation that prepares them for the more intricate aspects of prompt engineering.As you proceed, the book transitions into advanced strategies and techniques that reveal how to effectively interact with and utilize these powerful models. From crafting precise prompts that enhance model responses to exploring innovative methods like few-shot and zero-shot learning, this resource is designed to unlock the full potential of language model technology.This book not only teaches the technical skills needed to excel in the field but also addresses the broader implications of AI technology. It encourages thoughtful consideration of ethical issues and the impact of AI on society. By the end of this book, readers will master the technical aspects of prompt engineering & appreciate the importance of responsible AI development, making them well-rounded professionals ready to focus on the advancement of this cutting-edge technology.

LLM w projektowaniu oprogramowania. Tworzenie inteligentnych aplikacji i agentów z wykorzystaniem dużych modeli językowych

Valentina Alto

Duże modele językowe (LLM) stały się technologicznym przełomem. Ich wszechstronność i funkcjonalność sprawiły, że coraz częściej mówi się o nowej erze inteligentnie działających urządzeń i aplikacji. Umiejętność zastosowania LLM we własnych projektach już dziś jest koniecznością dla wielu projektantów i programistów. Dzięki tej książce opanujesz podstawowe koncepcje związane z użyciem LLM. Poznasz unikatowe cechy i mocne strony kilku najważniejszych modeli (w tym GPT, Gemini, Falcon). Następnie dowiesz się, w jaki sposób LangChain, lekki framework Pythona, pozwala na projektowanie inteligentnych agentów do przetwarzania danych o nieuporządkowanej strukturze. Znajdziesz tu również informacje dotyczące dużych modeli podstawowych, które wykraczają poza obsługę języka i potrafią wykonywać różne zadania związane na przykład z grafiką i dźwiękiem. Na koniec zgłębisz zagadnienia dotyczące ryzyka związanego z LLM, a także poznasz techniki uniemożliwiania tym modelom potencjalnie szkodliwych działań w aplikacji. Najciekawsze zagadnienia: architektura dużych modeli językowych unikatowe funkcje LLM komponenty służące do koordynacji sztucznej inteligencji, w tym tworzenia frontendu użycie wiedzy nieparametrycznej i wektorowych baz danych dostrajanie dużych modeli językowych do własnych potrzeb odpowiedzialność i etyka w systemach korzystających z LLM Odkryj, jak łatwo model generatywnej AI zintegruje się z Twoją aplikacją!   O książce w mediach: Eksperyment Myślowy - recenzja książki

LLMs in Enterprise. Design strategies, patterns, and best practices for large language model development

Ahmed Menshawy, Mahmoud Fahmy

The integration of large language models (LLMs) into enterprise applications is transforming how businesses use AI to drive smarter decisions and efficient operations. LLMs in Enterprise is your practical guide to bringing these capabilities into real-world business contexts. It demystifies the complexities of LLM deployment and provides a structured approach for enhancing decision-making and operational efficiency with AI.Starting with an introduction to the foundational concepts, the book swiftly moves on to hands-on applications focusing on real-world challenges and solutions. You’ll master data strategies and explore design patterns that streamline the optimization and deployment of LLMs in enterprise environments. From fine-tuning techniques to advanced inferencing patterns, the book equips you with a toolkit for solving complex challenges and driving AI-led innovation in business processes.By the end of this book, you’ll have a solid grasp of key LLM design patterns and how to apply them to enhance the performance and scalability of your generative AI solutions.

Machine Learning Algorithms. A reference guide to popular algorithms for data science and machine learning

Giuseppe Bonaccorso

In this book, you will learn all the important machine learning algorithms that are commonly used in the field of data science. These algorithms can be used for supervised as well as unsupervised learning, reinforcement learning, and semi-supervised learning. The algorithms that are covered in this book are linear regression, logistic regression, SVM, naïve Bayes, k-means, random forest, TensorFlow and feature engineering.In this book, you will how to use these algorithms to resolve your problems, and how they work. This book will also introduce you to natural language processing and recommendation systems, which help you to run multiple algorithms simultaneously.On completion of the book, you will know how to pick the right machine learning algorithm for clustering, classification, or regression for your problem