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Mastering Python for Networking and Security
Tackle security and networking issues using Python libraries such as Nmap, requests, asyncio, and scapyKey FeaturesEnhance your Python programming skills in securing systems and executing networking tasksExplore Python scripts to debug and secure complex networksLearn to avoid common cyber events with modern Python scriptingBook DescriptionIt's now more apparent than ever that security is a critical aspect of IT infrastructure, and that devastating data breaches can occur from simple network line hacks. As shown in this book, combining the latest version of Python with an increased focus on network security can help you to level up your defenses against cyber attacks and cyber threats. Python is being used for increasingly advanced tasks, with the latest update introducing new libraries and packages featured in the Python 3.7.4 recommended version. Moreover, most scripts are compatible with the latest versions of Python and can also be executed in a virtual environment. This book will guide you through using these updated packages to build a secure network with the help of Python scripting. You'll cover a range of topics, from building a network to the procedures you need to follow to secure it. Starting by exploring different packages and libraries, you'll learn about various ways to build a network and connect with the Tor network through Python scripting. You will also learn how to assess a network's vulnerabilities using Python security scripting. Later, you'll learn how to achieve endpoint protection by leveraging Python packages, along with writing forensic scripts. By the end of this Python book, you'll be able to use Python to build secure apps using cryptography and steganography techniques.What you will learnCreate scripts in Python to automate security and pentesting tasksExplore Python programming tools that are used in network security processesAutomate tasks such as analyzing and extracting information from serversUnderstand how to detect server vulnerabilities and analyze security modulesDiscover ways to connect to and get information from the Tor networkFocus on how to extract information with Python forensics toolsWho this book is forThis Python network security book is for network engineers, system administrators, or any security professional looking to overcome networking and security challenges. You will also find this book useful if you're a programmer with prior experience in Python. A basic understanding of general programming structures and the Python programming language is required before getting started.
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Artificial Intelligence with Python
Build real-world Artificial Intelligence applications with Python to intelligently interact with the world around youAbout This Book• Step into the amazing world of intelligent apps using this comprehensive guide• Enter the world of Artificial Intelligence, explore it, and create your own applications• Work through simple yet insightful examples that will get you up and running with Artificial Intelligence in no timeWho This Book Is ForThis book is for Python developers who want to build real-world Artificial Intelligence applications. This book is friendly to Python beginners, but being familiar with Python would be useful to play around with the code. It will also be useful for experienced Python programmers who are looking to use Artificial Intelligence techniques in their existing technology stacks.What You Will Learn• Realize different classification and regression techniques• Understand the concept of clustering and how to use it to automatically segment data• See how to build an intelligent recommender system• Understand logic programming and how to use it• Build automatic speech recognition systems• Understand the basics of heuristic search and genetic programming• Develop games using Artificial Intelligence• Learn how reinforcement learning works• Discover how to build intelligent applications centered on images, text, and time series data• See how to use deep learning algorithms and build applications based on itIn DetailArtificial Intelligence is becoming increasingly relevant in the modern world where everything is driven by technology and data. It is used extensively across many fields such as search engines, image recognition, robotics, finance, and so on. We will explore various real-world scenarios in this book and you'll learn about various algorithms that can be used to build Artificial Intelligence applications.During the course of this book, you will find out how to make informed decisions about what algorithms to use in a given context. Starting from the basics of Artificial Intelligence, you will learn how to develop various building blocks using different data mining techniques. You will see how to implement different algorithms to get the best possible results, and will understand how to apply them to real-world scenarios. If you want to add an intelligence layer to any application that's based on images, text, stock market, or some other form of data, this exciting book on Artificial Intelligence will definitely be your guide!Style and approachThis highly practical book will show you how to implement Artificial Intelligence. The book provides multiple examples enabling you to create smart applications to meet the needs of your organization. In every chapter, we explain an algorithm, implement it, and then build a smart application.
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Learning Geospatial Analysis with Python
This is a tutorial-style book that helps you to perform Geospatial and GIS analysis with Python and its tools/libraries. This book will first introduce various Python-related tools/packages in the initial chapters before moving towards practical usage, examples, and implementation in specialized kinds of Geospatial data analysis. This book is for anyone who wants to understand digital mapping and analysis and who uses Python or another scripting language for automation or crunching data manually.This book primarily targets Python developers, researchers, and analysts who want to perform Geospatial, modeling, and GIS analysis with Python.
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Python Machine Learning
Applied machine learning with a solid foundation in theory. Revised and expanded for TensorFlow 2, GANs, and reinforcement learning.Purchase of the print or Kindle book includes a free eBook in the PDF format.Key FeaturesThird edition of the bestselling, widely acclaimed Python machine learning bookClear and intuitive explanations take you deep into the theory and practice of Python machine learningFully updated and expanded to cover TensorFlow 2, Generative Adversarial Network models, reinforcement learning, and best practicesBook DescriptionPython Machine Learning, Third Edition is a comprehensive guide to machine learning and deep learning with Python. It acts as both a step-by-step tutorial, and a reference you'll keep coming back to as you build your machine learning systems.Packed with clear explanations, visualizations, and working examples, the book covers all the essential machine learning techniques in depth. While some books teach you only to follow instructions, with this machine learning book, Raschka and Mirjalili teach the principles behind machine learning, allowing you to build models and applications for yourself.Updated for TensorFlow 2.0, this new third edition introduces readers to its new Keras API features, as well as the latest additions to scikit-learn. It's also expanded to cover cutting-edge reinforcement learning techniques based on deep learning, as well as an introduction to GANs. Finally, this book also explores a subfield of natural language processing (NLP) called sentiment analysis, helping you learn how to use machine learning algorithms to classify documents.This book is your companion to machine learning with Python, whether you're a Python developer new to machine learning or want to deepen your knowledge of the latest developments.What you will learnMaster the frameworks, models, and techniques that enable machines to 'learn' from dataUse scikit-learn for machine learning and TensorFlow for deep learningApply machine learning to image classification, sentiment analysis, intelligent web applications, and moreBuild and train neural networks, GANs, and other modelsDiscover best practices for evaluating and tuning modelsPredict continuous target outcomes using regression analysisDig deeper into textual and social media data using sentiment analysisWho this book is forIf you know some Python and you want to use machine learning and deep learning, pick up this book. Whether you want to start from scratch or extend your machine learning knowledge, this is an essential resource. Written for developers and data scientists who want to create practical machine learning and deep learning code, this book is ideal for anyone who wants to teach computers how to learn from data.
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Advanced Machine Learning with Python
Solve challenging data science problems by mastering cutting-edge machine learning techniques in PythonAbout This Book• Resolve complex machine learning problems and explore deep learning• Learn to use Python code for implementing a range of machine learning algorithms and techniques• A practical tutorial that tackles real-world computing problems through a rigorous and effective approachWho This Book Is ForThis title is for Python developers and analysts or data scientists who are looking to add to their existing skills by accessing some of the most powerful recent trends in data science. If you've ever considered building your own image or text-tagging solution, or of entering a Kaggle contest for instance, this book is for you!Prior experience of Python and grounding in some of the core concepts of machine learning would be helpful.What You Will Learn• Compete with top data scientists by gaining a practical and theoretical understanding of cutting-edge deep learning algorithms• Apply your new found skills to solve real problems, through clearly-explained code for every technique and test• Automate large sets of complex data and overcome time-consuming practical challenges• Improve the accuracy of models and your existing input data using powerful feature engineering techniques• Use multiple learning techniques together to improve the consistency of results• Understand the hidden structure of datasets using a range of unsupervised techniques• Gain insight into how the experts solve challenging data problems with an effective, iterative, and validation-focused approach• Improve the effectiveness of your deep learning models further by using powerful ensembling techniques to strap multiple models togetherIn DetailDesigned to take you on a guided tour of the most relevant and powerful machine learning techniques in use today by top data scientists, this book is just what you need to push your Python algorithms to maximum potential. Clear examples and detailed code samples demonstrate deep learning techniques, semi-supervised learning, and more - all whilst working with real-world applications that include image, music, text, and financial data.The machine learning techniques covered in this book are at the forefront of commercial practice. They are applicable now for the first time in contexts such as image recognition, NLP and web search, computational creativity, and commercial/financial data modeling. Deep Learning algorithms and ensembles of models are in use by data scientists at top tech and digital companies, but the skills needed to apply them successfully, while in high demand, are still scarce.This book is designed to take the reader on a guided tour of the most relevant and powerful machine learning techniques. Clear descriptions of how techniques work and detailed code examples demonstrate deep learning techniques, semi-supervised learning and more, in real world applications. We will also learn about NumPy and Theano.By this end of th
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Scientific Computing with Python
Leverage this example-packed, comprehensive guide for all your Python computational needsKey FeaturesLearn the first steps within Python to highly specialized conceptsExplore examples and code snippets taken from typical programming situations within scientific computing.Delve into essential computer science concepts like iterating, object-oriented programming, testing, and MPI presented in strong connection to applications within scientific computing.Book DescriptionPython has tremendous potential within the scientific computing domain. This updated edition of Scientific Computing with Python features new chapters on graphical user interfaces, efficient data processing, and parallel computing to help you perform mathematical and scientific computing efficiently using Python.This book will help you to explore new Python syntax features and create different models using scientific computing principles. The book presents Python alongside mathematical applications and demonstrates how to apply Python concepts in computing with the help of examples involving Python 3.8. You'll use pandas for basic data analysis to understand the modern needs of scientific computing, and cover data module improvements and built-in features. You'll also explore numerical computation modules such as NumPy and SciPy, which enable fast access to highly efficient numerical algorithms. By learning to use the plotting module Matplotlib, you will be able to represent your computational results in talks and publications. A special chapter is devoted to SymPy, a tool for bridging symbolic and numerical computations.By the end of this Python book, you'll have gained a solid understanding of task automation and how to implement and test mathematical algorithms within the realm of scientific computing.What you will learnUnderstand the building blocks of computational mathematics, linear algebra, and related Python objectsUse Matplotlib to create high-quality figures and graphics to draw and visualize resultsApply object-oriented programming (OOP) to scientific computing in PythonDiscover how to use pandas to enter the world of data processingHandle exceptions for writing reliable and usable codeCover manual and automatic aspects of testing for scientific programmingGet to grips with parallel computing to increase computation speedWho this book is forThis book is for students with a mathematical background, university teachers designing modern courses in programming, data scientists, researchers, developers, and anyone who wants to perform scientific computation in Python.
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Python for SAS Users
Business users familiar with Base SAS programming can now learn Python by example. You will learn via examples that map SAS programming constructs and coding patterns into their Python equivalents. Your primary focus will be on pandas and data management issues related to analysis of data.It is estimated that there are three million or more SAS users worldwide today. As the data science landscape shifts from using SAS to open source software such as Python, many users will feel the need to update their skills. Most users are not formally trained in computer science and have likely acquired their skills programming SAS as part of their job.As a result, the current documentation and plethora of books and websites for learning Python are technical and not geared for most SAS users. Python for SAS Users provides the most comprehensive set of examples currently available. It contains over 200 Python scripts and approximately 75 SAS programs that are analogs to the Python scripts. The first chapters are more Python-centric, while the remaining chapters illustrate SAS and corresponding Python examples to solve common data analysis tasks such as reading multiple input sources, missing value detection, imputation, merging/combining data, and producing output. This book is an indispensable guide for integrating SAS and Python workflows.What You’ll LearnQuickly master Python for data analysis without using a trial-and-error approachUnderstand the similarities and differences between Base SAS and PythonBetter determine which language to use, depending on your needsObtain quick resultsWho This Book Is ForSAS users, SAS programmers, data scientists, data scientist leaders, and Python users who need to work with SAS
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Mastering Python
Use advanced features of Python to write high-quality, readable code and packagesKey FeaturesExtensively updated for Python 3.10 with new chapters on design patterns, scientific programming, machine learning, and interactive PythonShape your scripts using key concepts like concurrency, performance optimization, asyncio, and multiprocessingLearn how advanced Python features fit together to produce maintainable codeBook DescriptionEven if you find writing Python code easy, writing code that is efficient, maintainable, and reusable is not so straightforward. Many of Python's capabilities are underutilized even by more experienced programmers. Mastering Python, Second Edition, is an authoritative guide to understanding advanced Python programming so you can write the highest quality code. This new edition has been extensively revised and updated with exercises, four new chapters and updates up to Python 3.10. Revisit important basics, including Pythonic style and syntax and functional programming. Avoid common mistakes made by programmers of all experience levels. Make smart decisions about the best testing and debugging tools to use, optimize your code's performance across multiple machines and Python versions, and deploy often-forgotten Python features to your advantage. Get fully up to speed with asyncio and stretch the language even further by accessing C functions with simple Python calls. Finally, turn your new-and-improved code into packages and share them with the wider Python community. If you are a Python programmer wanting to improve your code quality and readability, this Python book will make you confident in writing high-quality scripts and taking on bigger challengesWhat you will learnWrite beautiful Pythonic code and avoid common Python coding mistakesApply the power of decorators, generators, coroutines, and metaclassesUse different testing systems like pytest, unittest, and doctestTrack and optimize application performance for both memory and CPU usageDebug your applications with PDB, Werkzeug, and faulthandlerImprove your performance through asyncio, multiprocessing, and distributed computingExplore popular libraries like Dask, NumPy, SciPy, pandas, TensorFlow, and scikit-learnExtend Python's capabilities with C/C++ libraries and system callsWho this book is forThis book will benefit more experienced Python programmers who wish to upskill, serving as a reference for best practices and some of the more intricate Python techniques. Even if you have been using Python for years, chances are that you haven't yet encountered every topic discussed in this book. A good understanding of Python programming is necessary
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