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Learning Python Data Visualization
If you are a Python novice or an experienced developer and want to explore data visualization libraries, then this is the book for you. No prior charting or graphics experience is needed.
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Python 2 and 3 Compatibility
Discover clean ways to write code that will run on both Python 2 and 3.This book is tutorial-oriented with detailed examples of how to convert existing Python 2-compatible code to code that will run reliably on both Python 2 and 3. Although Python 3 is considered the future of Python, Python 2.x will be maintained for several more years, alongside Python 3, which is not backwards compatible. For those who need to support both versions, this book guides you through the process. Python 2 and 3 Compatibility explains the syntactical differences between Python 2 and 3, and how to use Python packages Python-Future and Six to implement neutral compatibility. Developers working on either small, medium, or large projects will appreciate the author's clear explanations, detailed examples, and clean techniques to help them extend support for both versions to their existing Python 2-compatible projects.What You'll LearnUnderstand the syntactical differences between Python 2 and 3Use the Six and Future librariesReview the new features in Python 3Choose which Python versions to support when doing neutral supportDecide on whether to port or provide support for both versionsWho This Book Is For Professional Python developers and enthusiasts that want to implement Python 3 support for their existing Python 2 compatible code.
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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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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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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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Building Machine Learning Systems with Python -
This book primarily targets Python developers who want to learn and use Python's machine learning capabilities and gain valuable insights from data to develop effective solutions for business problems.
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Learning Geospatial Analysis with Python -
An effective guide to geographic information systems and remote sensing analysis using Python 3About This Book• Construct applications for GIS development by exploiting Python• This focuses on built-in Python modules and libraries compatible with the Python Packaging Index distribution system—no compiling of C libraries necessary• This practical, hands-on tutorial teaches you all about Geospatial analysis in Python Who This Book Is ForIf you are a Python developer, researcher, or analyst who wants to perform Geospatial, modeling, and GIS analysis with Python, then this book is for you. Familarity with digital mapping and analysis using Python or another scripting language for automation or crunching data manually is appreciatedWhat You Will Learn• Automate Geospatial analysis workflows using Python• Code the simplest possible GIS in 60 lines of Python• Mold thematic maps with Python tools• Get hold of the various forms that geospatial data comes in• Produce elevation contours using Python tools• Create flood inundation models• Apply Geospatial analysis to find out about real-time data tracking and for storm chasingIn DetailGeospatial Analysis is used in almost every field you can think of from medicine, to defense, to farming. This book will guide you gently into this exciting and complex field. It walks you through the building blocks of geospatial analysis and how to apply them to influence decision making using the latest Python software.Learning Geospatial Analysis with Python, 2nd Edition uses the expressive and powerful Python 3 programming language to guide you through geographic information systems, remote sensing, topography, and more, while providing a framework for you to approach geospatial analysis effectively, but on your own terms. We start by giving you a little background on the field, and a survey of the techniques and technology used. We then split the field into its component specialty areas: GIS, remote sensing, elevation data, advanced modeling, and real-time data.This book will teach you everything you need to know about, Geospatial Analysis from using a particular software package or API to using generic algorithms that can be applied. This book focuses on pure Python whenever possible to minimize compiling platform-dependent binaries, so that you don't become bogged down in just getting ready to do analysis. This book will round out your technical library through handy recipes that will give you a good understanding of a field that supplements many a modern day human endeavors.Style and approach This is a practical, hands-on tutorial that teaches you all about Geospatial analysis interactively using Python.
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