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Mathematical Logic through Python
Using a unique pedagogical approach, this text introduces mathematical logic by guiding students in implementing the underlying logical concepts and mathematical proofs via Python programming. This approach, tailored to the unique intuitions and strengths of the ever-growing population of programming-savvy students, brings mathematical logic into the comfort zone of these students and provides clarity that can only be achieved by a deep hands-on understanding and the satisfaction of having created working code. While the approach is unique, the text follows the same set of topics typically covered in a one-semester undergraduate course, including propositional logic and first-order predicate logic, culminating in a proof of Gödel's completeness theorem. A sneak peek to Gödel's incompleteness theorem is also provided. The textbook is accompanied by an extensive collection of programming tasks, code skeletons, and unit tests. Familiarity with proofs and basic proficiency in Python is assumed.
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Python Data Analytics
Explore the latest Python tools and techniques to help you tackle the world of data acquisition and analysis. You'll review scientific computing with NumPy, visualization with matplotlib, and machine learning with scikit-learn. This third edition is fully updated for the latest version of Python and its related libraries, and includes coverage of social media data analysis, image analysis with OpenCV, and deep learning libraries. Each chapter includes multiple examples demonstrating how to work with each library. At its heart lies the coverage of pandas, for high-performance, easy-to-use data structures and tools for data manipulationAuthor Fabio Nelli expertly demonstrates using Python for data processing, management, and information retrieval. Later chapters apply what you've learned to handwriting recognition and extending graphical capabilities with the JavaScript D3 library. Whether you are dealing with sales data, investment data, medical data, web page usage, or other data sets, Python Data Analytics, Third Edition is an invaluable reference with its examples of storing, accessing, and analyzing data.What You'll LearnUnderstand the core concepts of data analysis and the Python ecosystemGo in depth with pandas for reading, writing, and processing dataUse tools and techniques for data visualization and image analysisExamine popular deep learning libraries Keras, Theano,TensorFlow, and PyTorchWho This Book Is ForExperienced Python developers who need to learn about Pythonic tools for data analysis
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Python Data Science Essentials
Gain useful insights from your data using popular data science toolsKey FeaturesA one-stop guide to Python libraries such as pandas and NumPyComprehensive coverage of data science operations such as data cleaning and data manipulationChoose scalable learning algorithms for your data science tasksBook DescriptionFully expanded and upgraded, the latest edition of Python Data Science Essentials will help you succeed in data science operations using the most common Python libraries. This book offers up-to-date insight into the core of Python, including the latest versions of the Jupyter Notebook, NumPy, pandas, and scikit-learn.The book covers detailed examples and large hybrid datasets to help you grasp essential statistical techniques for data collection, data munging and analysis, visualization, and reporting activities. You will also gain an understanding of advanced data science topics such as machine learning algorithms, distributed computing, tuning predictive models, and natural language processing. Furthermore, You’ll also be introduced to deep learning and gradient boosting solutions such as XGBoost, LightGBM, and CatBoost.By the end of the book, you will have gained a complete overview of the principal machine learning algorithms, graph analysis techniques, and all the visualization and deployment instruments that make it easier to present your results to an audience of both data science experts and business usersWhat you will learnSet up your data science toolbox on Windows, Mac, and LinuxUse the core machine learning methods offered by the scikit-learn libraryManipulate, fix, and explore data to solve data science problemsLearn advanced explorative and manipulative techniques to solve data operationsOptimize your machine learning models for optimized performanceExplore and cluster graphs, taking advantage of interconnections and links in your dataWho this book is forIf you’re a data science entrant, data analyst, or data engineer, this book will help you get ready to tackle real-world data science problems without wasting any time. Basic knowledge of probability/statistics and Python coding experience will assist you in understanding the concepts covered in this book.
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Object-oriented Python
Object-Oriented Python is an intuitive and thorough guide to mastering object-oriented programming from the ground up. You'll cover the basics of building classes and creating objects, and put theory into practice using the pygame package with clear examples that help visualize the object-oriented style. You'll explore the key concepts of object-oriented programming - encapsulation, polymorphism, and inheritance - and learn not just how to code with objects, but the absolute best practices for doing so. Finally, you'll bring it all together by building a complex video game, complete with full animations and sounds. The book covers two fully functional Python code packages that will speed up development of graphical user interface (GUI) programs in Python.
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NLP for Project Managers
Method and process are important in project management, but knowing how to use them is even more so. Project managers can increase effectiveness most by developing soft skills. You will find that these skills are transferable across project types and industry sectors. This book illustrates the application of NLP to develop competencies - better equipping you to communicate across cultures, reframe problems, manage stakeholder groups, resolve conflicts, motivate teams and become an even better leader.
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Python for Signal Processing
This book covers the fundamental concepts in signal processing illustrated with Python code and made available via IPython Notebooks, which are live, interactive, browser-based documents that allow one to change parameters, redraw plots, and tinker with the ideas presented in the text. Everything in the text is computable in this format and thereby invites readers to “experiment and learn” as they read. The book focuses on the core, fundamental principles of signal processing. The code corresponding to this book uses the core functionality of the scientific Python toolchain that should remain unchanged into the foreseeable future. For those looking to migrate their signal processing codes to Python, this book illustrates the key signal and plotting modules that can ease this transition. For those already comfortable with the scientific Python toolchain, this book illustrates the fundamental concepts in signal processing and provides a gateway to further signal processing concepts.
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BiteSize Python for Intermediate Learners
This book is an introduction to Python for intermediate learners. It allows readers to take a slow and steady approach to building upon their understanding of Python code. While there are many books, websites, and online courses about the topic, Python programming is broken down here into easily digestible lessons of less than 5 minutes each, following a tried and tested BiteSize approach.Each lesson begins with a clear and concise introduction to the topic, giving the reader a strong base to start from and gets them ready for deeper learning. This is followed by coding demonstrations that further explore the ideas discussed. The book offers practice tasks in different difficulty levels, so readers can test their knowledge and grow their confidence. The reader will also play with case studies to solve real-world problems. Tips on how to incorporate Generative AI into a learning toolkit are provided, for purposes like feedback, practice exercises, code reviews, and exploring advanced topics. Recommended AI prompts can help readers identify areas for improvement, review key concepts, and track progress.This book is designed for intermediate learners with a basic understanding of Python. It is ideal for individuals with busy schedules or limited time for studying.
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Applied Supervised Learning with Python
Explore the exciting world of machine learning with the fastest growing technology in the worldKey FeaturesUnderstand various machine learning concepts with real-world examplesImplement a supervised machine learning pipeline from data ingestion to validationGain insights into how you can use machine learning in everyday lifeBook DescriptionMachine learning—the ability of a machine to give right answers based on input data—has revolutionized the way we do business. Applied Supervised Learning with Python provides a rich understanding of how you can apply machine learning techniques in your data science projects using Python. You'll explore Jupyter Notebooks, the technology used commonly in academic and commercial circles with in-line code running support.With the help of fun examples, you'll gain experience working on the Python machine learning toolkit—from performing basic data cleaning and processing to working with a range of regression and classification algorithms. Once you’ve grasped the basics, you'll learn how to build and train your own models using advanced techniques such as decision trees, ensemble modeling, validation, and error metrics. You'll also learn data visualization techniques using powerful Python libraries such as Matplotlib and Seaborn. This book also covers ensemble modeling and random forest classifiers along with other methods for combining results from multiple models, and concludes by delving into cross-validation to test your algorithm and check how well the model works on unseen data.By the end of this book, you'll be equipped to not only work with machine learning algorithms, but also be able to create some of your own!What you will learnUnderstand the concept of supervised learning and its applicationsImplement common supervised learning algorithms using machine learning Python librariesValidate models using the k-fold techniqueBuild your models with decision trees to get results effortlesslyUse ensemble modeling techniques to improve the performance of your modelApply a variety of metrics to compare machine learning modelsWho this book is forApplied Supervised Learning with Python is for you if you want to gain a solid understanding of machine learning using Python. It'll help if you to have some experience in any functional or object-oriented language and a basic understanding of Python libraries and expressions, such as arrays and dictionaries.
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Hands-On Image Processing and Computer Vision with Python
Explore the world of image processing, computer vision, and generative AI with Python—from fundamental concepts and classical methods to deep learning, modern vision systems, and real-world visual content generation.Free with your book: DRM-free PDF version + access to Packt's next-gen Reader*Key FeaturesMaster end-to-end image processing and computer vision workflows using PythonBuild visual AI systems with classical, deep learning, and generative AI techniquesApply theory with production-ready implementations using leading Python librariesPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionAnalyzing and understanding visual data has become essential in modern applications such as healthcare, security, remote sensing, manufacturing, and digital media. This book provides a hands-on guide to image processing and computer vision using Python, following a practical approach that bridges theory with implementation.As you progress through the chapters, you will develop proficiency in Python 3 and implement algorithms spanning classical image processing, modern computer vision, and state-of-the-art (SOTA) deep learning and generative AI. The book covers image enhancement, restoration, filtering, segmentation, feature extraction, classification, and object detection using libraries including NumPy, OpenCV, PIL, SciPy, scikit-image, scikit-learn, TensorFlow, Keras, and PyTorch.Advanced chapters introduce CNNs, Vision Transformers, transformer-based segmentation, modern detection frameworks, GANs, diffusion models, foundation models, image-to-image translation, super-resolution, and multimodal vision-language understanding. Real-world applications span medical imaging, remote sensing, banking, augmented reality, autonomous driving, industrial inspection, and intelligent visual analytics. By the end of the book, you will be equipped to design and implement real-world visual computing solutions.*Email sign-up and proof of purchase requiredWhat you will learnBuild image processing and computer vision pipelinesApply image enhancement, restoration, and segmentationImplement image classification and object detection modelsExplore CNNs, Vision Transformers, and attention modelsGenerate and edit images using GANs and diffusion modelsDevelop multimodal vision-language AI applicationsApply visual AI across diverse real-world domainsImplement super-resolution, style transfer, and image-to-image translationWho this book is forPython developers, engineers, applied researchers, students, and AI practitioners who want to build end-to-end image processing and computer vision systems. A working knowledge of Python is required, while familiarity with linear algebra, calculus, and basic machine learning concepts will help you get the most from the advanced topics.
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BiteSize Python for Intermediate Learners
This book is an introduction to Python for intermediate learners. It allows readers to take a slow and steady approach to building upon their understanding of Python code. While there are many books, websites, and online courses about the topic, Python programming is broken down here into easily digestible lessons of less than 5 minutes each, following a tried and tested BiteSize approach.Each lesson begins with a clear and concise introduction to the topic, giving the reader a strong base to start from and gets them ready for deeper learning. This is followed by coding demonstrations that further explore the ideas discussed. The book offers practice tasks in different difficulty levels, so readers can test their knowledge and grow their confidence. The reader will also play with case studies to solve real-world problems. Tips on how to incorporate Generative AI into a learning toolkit are provided, for purposes like feedback, practice exercises, code reviews, and exploring advanced topics. Recommended AI prompts can help readers identify areas for improvement, review key concepts, and track progress.This book is designed for intermediate learners with a basic understanding of Python. It is ideal for individuals with busy schedules or limited time for studying.
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Numerical Python
Learn how to leverage the scientific computing and data analysis capabilities of Python, its standard library, and popular open-source numerical Python packages like NumPy, SymPy, SciPy, matplotlib, and more. This book demonstrates how to work with mathematical modeling and solve problems with numerical, symbolic, and visualization techniques. It explores applications in science, engineering, data analytics, and more.Numerical Python, Third Edition, presents many case study examples of applications in fundamental scientific computing disciplines, as well as in data science and statistics. This fully revised edition, updated for each library's latest version, demonstrates Python's power for rapid development and exploratory computing due to its simple and high-level syntax and many powerful libraries and tools for computation and data analysis. After reading this book, readers will be familiar with many computing techniques, including array-based and symbolic computing, visualization and numerical file I/O, equation solving, optimization, interpolation and integration, and domain-specific computational problems, such as differential equation solving, data analysis, statistical modeling, and machine learning.What You'll LearnWork with vectors and matrices using NumPyReview Symbolic computing with SymPyPlot and visualize data with MatplotlibPerform data analysis tasks with Pandas and SciPyUnderstand statistical modeling and machine learning with statsmodels and scikit-learnOptimize Python code using Numba and CythonWho This Book Is ForDevelopers who want to understand how to use Python and its ecosystem of libraries for scientific computing and data analysis.
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Introduction to Python for Science and Engineering
Introduction to Python for Science and Engineering offers a quick and incisive introduction to the Python programming language for use in any science or engineering discipline. The approach is pedagogical and “bottom up,” which means starting with examples and extracting more general principles from that experience. No prior programming experience is assumed.Readers will learn the basics of Python syntax, data structures, input and output, conditionals and loops, user-defined functions, plotting, animation, and visualization. They will also learn how to use Python for numerical analysis, including curve fitting, random numbers, linear algebra, solutions to nonlinear equations, numerical integration, solutions to differential equations, and fast Fourier transforms.Readers learn how to interact and program with Python using JupyterLab and Spyder, two simple and widely used integrated development environments.All the major Python libraries for science and engineering are covered, including NumPy, SciPy, Matplotlib, and Pandas. Other packages are also introduced, including Numba, which can render Python numerical calculations as fast as compiled computer languages such as C but without their complex overhead.
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Foundational Python for Data Science
Data science and machine learning—two of the world’s hottest fields—are attracting talent from a wide variety of technical, business, and liberal arts disciplines. Python, the world's #1 programming language, is also the most popular language for data science and machine learning. This is the first guide specifically designed to help millions of people with widely diverse backgrounds learn Python so they can use it for data science and machine learning. Leading data science instructor and practitioner Kennedy Behrman first walks through the process of learning to code for the first time with Python and Jupyter notebook, then introduces key libraries every Python data science programmer needs to master. Once you've learned these foundations, Behrman introduces intermediate and applied Python techniques for real-world problem-solving. Throughout, Foundational Python for Data Science presents hands-on exercises, learning assessments, case studies, and more—all created with Colab (Jupyter compatible) notebooks, so you can execute all coding examples interactively without installing or configuring any software.
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Python Audio Cookbook
The Python Audio Cookbook offers an introduction to Python for sound and multimedia applications, with chapters that cover writing your first Python programs, controlling Pyo with physical computing, and writing your own GUI, among many others.Guiding the reader through a variety of audio synthesis techniques, the book empowers readers to combine their projects with popular platforms, from the Arduino to Twitter, and state-of-the-art practices such as AI. The Python Audio Cookbook balances accessible explanations for theoretical concepts, including Python syntax, audio processing and machine learning, with practical applications. This book is an essential introductory guide to Python for sound and multimedia practitioners, as well as programmers interested in audio applications.
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Causal Inference and Discovery in Python
Demystify causal inference and casual discovery by uncovering causal principles and merging them with powerful machine learning algorithms for observational and experimental dataPurchase of the print or Kindle book includes a free PDF eBookKey FeaturesExamine Pearlian causal concepts such as structural causal models, interventions, counterfactuals, and moreDiscover modern causal inference techniques for average and heterogenous treatment effect estimationExplore and leverage traditional and modern causal discovery methodsBook DescriptionCausal methods present unique challenges compared to traditional machine learning and statistics. Learning causality can be challenging, but it offers distinct advantages that elude a purely statistical mindset. Causal Inference and Discovery in Python helps you unlock the potential of causality.You’ll start with basic motivations behind causal thinking and a comprehensive introduction to Pearlian causal concepts, such as structural causal models, interventions, counterfactuals, and more. Each concept is accompanied by a theoretical explanation and a set of practical exercises with Python code.Next, you’ll dive into the world of causal effect estimation, consistently progressing towards modern machine learning methods. Step-by-step, you’ll discover Python causal ecosystem and harness the power of cutting-edge algorithms. You’ll further explore the mechanics of how “causes leave traces” and compare the main families of causal discovery algorithms.The final chapter gives you a broad outlook into the future of causal AI where we examine challenges and opportunities and provide you with a comprehensive list of resources to learn more.What you will learnMaster the fundamental concepts of causal inferenceDecipher the mysteries of structural causal modelsUnleash the power of the 4-step causal inference process in PythonExplore advanced uplift modeling techniquesUnlock the secrets of modern causal discovery using PythonUse causal inference for social impact and community benefitWho this book is forThis book is for machine learning engineers, data scientists, and machine learning researchers looking to extend their data science toolkit and explore causal machine learning. It will also help developers familiar with causality who have worked in another technology and want to switch to Python, and data scientists with a history of working with traditional causality who want to learn causal machine learning. It’s also a must-read for tech-savvy entrepreneurs looking to build a competitive edge for their products and go beyond the limitations of traditional machine learning.
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