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Statistics and Data Visualisation with Python
This book is intended to serve as a bridge in statistics for graduates and business practitioners interested in using their skills in the area of data science and analytics as well as statistical analysis in general. On the one hand, the book is intended to be a refresher for readers who have taken some courses in statistics, but who have not necessarily used it in their day-to-day work. On the other hand, the material can be suitable for readers interested in the subject as a first encounter with statistical work in Python. Statistics and Data Visualisation with Python aims to build statistical knowledge from the ground up by enabling the reader to understand the ideas behind inferential statistics and begin to formulate hypotheses that form the foundations for the applications and algorithms in statistical analysis, business analytics, machine learning, and applied machine learning. This book begins with the basics of programming in Python and data analysis, to help construct a solid basis in statistical methods and hypothesis testing, which are useful in many modern applications.
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Ethics, Machine Learning, and Python in Geospatial Analysis
In geospatial analysis, navigating the complexities of data interpretation and analysis presents a formidable challenge. Traditional methods often need to efficiently handle vast volumes of geospatial data while providing insightful and actionable results. Scholars and practitioners grapple with manual or rule-based approaches, hindering progress in understanding and addressing pressing issues such as climate change, urbanization, and resource management. Ethics, Machine Learning, and Python in Geospatial Analysis offers a solution to the challenges faced by leveraging the extensive library support and user-friendly interface of Python and machine learning. The book's meticulously crafted chapters guide readers through the intricacies of Python programming and its application in geospatial analysis, from fundamental concepts to advanced techniques. Scholars and practitioners can now enhance their analytical capabilities and unlock deeper insights from geospatial data with the guidance of this pivotal book. From manipulating data structures to employing machine learning algorithms, it equips readers with the tools and knowledge to tackle complex geospatial problems confidently and precisely. With Ethics, Machine Learning, and Python in Geospatial Analysis, the era of cumbersome manual processes gives way to a new frontier of efficient, data-driven analysis, empowering scholars to make meaningful contributions to geospatial science.
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Hands-On Machine Learning with scikit-learn and Scientific Python Toolkits
Integrate scikit-learn with various tools such as NumPy, pandas, imbalanced-learn, and scikit-surprise and use it to solve real-world machine learning problemsKey FeaturesDelve into machine learning with this comprehensive guide to scikit-learn and scientific PythonMaster the art of data-driven problem-solving with hands-on examplesFoster your theoretical and practical knowledge of supervised and unsupervised machine learning algorithmsBook DescriptionMachine learning is applied everywhere, from business to research and academia, while scikit-learn is a versatile library that is popular among machine learning practitioners. This book serves as a practical guide for anyone looking to provide hands-on machine learning solutions with scikit-learn and Python toolkits.The book begins with an explanation of machine learning concepts and fundamentals, and strikes a balance between theoretical concepts and their applications. Each chapter covers a different set of algorithms, and shows you how to use them to solve real-life problems. You’ll also learn about various key supervised and unsupervised machine learning algorithms using practical examples. Whether it is an instance-based learning algorithm, Bayesian estimation, a deep neural network, a tree-based ensemble, or a recommendation system, you’ll gain a thorough understanding of its theory and learn when to apply it. As you advance, you’ll learn how to deal with unlabeled data and when to use different clustering and anomaly detection algorithms.By the end of this machine learning book, you’ll have learned how to take a data-driven approach to provide end-to-end machine learning solutions. You’ll also have discovered how to formulate the problem at hand, prepare required data, and evaluate and deploy models in production.What you will learnUnderstand when to use supervised, unsupervised, or reinforcement learning algorithmsFind out how to collect and prepare your data for machine learning tasksTackle imbalanced data and optimize your algorithm for a bias or variance tradeoffApply supervised and unsupervised algorithms to overcome various machine learning challengesEmploy best practices for tuning your algorithm’s hyper parametersDiscover how to use neural networks for classification and regressionBuild, evaluate, and deploy your machine learning solutions to productionWho this book is forThis book is for data scientists, machine learning practitioners, and anyone who wants to learn how machine learning algorithms work and to build different machine learning models using the Python ecosystem. The book will help you take your knowledge of machine learning to the next level by grasping its ins and outs and tailoring it to your needs. Working knowledge of Python and a basic understanding of underlying mathematical and statistical concepts is required.
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DevOps in Python
Take advantage of Python to automate complex systems with readable code. This new edition will help you move from operations/system administration into easy-to-learn coding.You'll start by writing command-line scripts and automating simple DevOps-style tasks followed by creating reliable and fast unit tests designed to avoid incidents caused by buggy automation. You’ll then move on to more advanced cases, like using Jupyter as an auditable remote-control panel and writing Ansible and Salt extensions.The updated information in this book covers best practices for deploying and updating Python applications. This includes Docker, modern Python packaging, and internal Python package repositories. You'll also see how to use the AWS API, and the Kubernetes API, and how to automate Docker container image building and running. Finally, you'll work with Terraform from Python to allow more flexible templating and customization of environments.What You'll LearnUnderstand operating system automation with PythonPackage Python applicationsUse Python as a DevOps consoleReview Cloud automation with PythonWho This Book Is ForDevOps engineer. Site Reliability Engineer, or similar (including Platform, Production, and Systems), and whose organization uses Python.
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Andre Gide
Andre Gide, renowned French essayist, novelist, and playwright, was also a homosexual apologist whose sexuality was central to the whole of his literary and political discourse. This book by Patrick Pollard—the first serious study of homosexuality in Gide’s theater and fiction—analyzes his ideas and traces the philosophical, anthropological, scientific, and literary movements that influenced his thought. Pollard begins by discussing Corydon, a defense of pederasty that Gide felt was his most important book. He then provided a historical and analytical survey of books that contributed to Gide’s perception of homosexuality, including works on philosophy, social theory, natural history, and medicolegal questions. Pollard goes on to investigate works of fiction—ancient and modern, European and Oriental—in which Gide saw homosexual elements. He concludes by considering the homosexual themes in Gide’s own works, analyzing the ways that Gide constantly tried to resolve conflicts between nature and culture, hypocrisy and honesty, corruption and sound moral judgment, anomaly and conformity, and sexual freedom and religious constraint. The book provides a new perspective on Gide’s work, a reconstruction of the moral and intellectual climate in Europe at the beginning of the twentieth century, and a substantial contribution to the cultural history of homosexuality.
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Developing Graphics Frameworks with Python and OpenGL
Developing Graphics Frameworks with Python and OpenGL shows you how to create software for rendering complete three-dimensional scenes. The authors explain the foundational theoretical concepts as well as the practical programming techniques that will enable you to create your own animated and interactive computer-generated worlds.You will learn how to combine the power of OpenGL, the most widely adopted cross-platform API for GPU programming, with the accessibility and versatility of the Python programming language. Topics you will explore include generating geometric shapes, transforming objects with matrices, applying image-based textures to surfaces, and lighting your scene. Advanced sections explain how to implement procedurally generated textures, postprocessing effects, and shadow mapping. In addition to the sophisticated graphics framework you will develop throughout this book, with the foundational knowledge you will gain, you will be able to adapt and extend the framework to achieve even more spectacular graphical results.
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Statistics and Data Visualization in Climate Science with R and Python
A comprehensive overview of essential statistical concepts, useful statistical methods, data visualization, and modern computing tools for the climate sciences and many others such as geography and environmental engineering. It is an invaluable reference for students and researchers in climatology and its connected fields who wish to learn data science, statistics, R and Python programming. The examples and exercises in the book empower readers to work on real climate data from station observations, remote sensing and simulated results. For example, students can use R or Python code to read and plot the global warming data and the global precipitation data in netCDF, csv, txt, or JSON; and compute and interpret empirical orthogonal functions. The book's computer code and real-world data allow readers to fully utilize the modern computing technology and updated datasets. Online supplementary resources include R code and Python code, data files, figure files, tutorials, slides and sample syllabi.
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Numerical Methods in Chemical Engineering Using Python® and Simulink®
Numerical methods are vital to the practice of chemical engineering, allowing for the solution of real-world problems. Written in a concise and practical format, this textbook introduces readers to the numerical methods required in the discipline of chemical engineering and enables them to validate their solutions using both Python and Simulink.Introduces numerical methods, followed by the solution of linear and nonlinear algebraic equationsDeals with the numerical integration of a definite function and solves initial and boundary value ordinary differential equations with different ordersWeaves in examples of various numerical methods and validates solutions to each with Python and Simulink graphical programmingFeatures appendices on how to use Python and SimulinkAimed at advanced undergraduate and graduate chemical engineering students, as well as practicing chemical engineers, this textbook offers a guide to the use of two of the most widely used programs in the discipline.The textbook features numerous video lectures of applications and a solutions manual for qualifying instructors.
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André Malraux and the Metamorphosis of Death
-- Gary Varner, Environmental Ethics
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