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Practical Machine Learning for Data Analysis Using Python is a problem solver's guide for creating real-world intelligent systems. It provides a comprehensive approach with concepts, practices, hands-on examples, and sample code. The book teaches readers the vital skills required to understand and solve different problems with machine learning. It teaches machine learning techniques necessary to become a successful practitioner, through the presentation of real-world case studies in Python machine learning ecosystems. The book also focuses on building a foundation of machine learning knowledge to solve different real-world case studies across various fields, including biomedical signal analysis, healthcare, security, economics, and finance. Moreover, it covers a wide range of machine learning models, including regression, classification, and forecasting. The goal of the book is to help a broad range of readers, including IT professionals, analysts, developers, data scientists, engineers, and graduate students, to solve their own real-world problems. Offers a comprehensive overview of the application of machine learning tools in data analysis across a wide range of subject areas Teaches readers how to apply machine learning techniques to biomedical signals, financial data, and healthcare data Explores important classification and regression algorithms as well as other machine learning techniques Explains how to use Python to handle data extraction, manipulation, and exploration techniques, as well as how to visualize data spread across multiple dimensions and extract useful features.

Machine elements --- Information systems --- Machine learning. --- Data mining. --- Python (Computer program language) --- Electronic books.

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Continue your Python network automation journey and delve deeper into advanced techniques and methodologies. Volume 2 of this comprehensive guide takes you beyond the essentials, equipping you with advanced skills and strategies crucial for success in network automation. Building upon the knowledge gained in Volume 1, you’ll set the stage for mastery in this dynamic field. You’ll start by establishing a robust lab environment for advanced automation projects tailored to your needs and use practical exercises to gain valuable insights into essential networking protocols. Then automate repetitive tasks with precision and efficiency by leveraging powerful Python libraries and tools. You’ll also see how to streamline IP address management and data center infrastructure management tasks with Python. Discover advanced techniques for network management and monitoring to optimize network performance and security. Explore the development of custom tools and applications for Cisco IOS upgrade tasks in complex network environments and put your skills to the test with real-world scenarios. All this is designed to solidify your expertise and confidence in network automation practices. Your network management capabilities will be enhanced with advanced tools, such as NetBox. Introduction to Python Network Automation Volume 2 - Stepping up provides a comprehensive roadmap to elevate your skills and excel in the dynamic field of network automation. Whether you're a seasoned professional or a newcomer to the field, this guide equips you with the tools and knowledge needed to thrive in today's network automation landscape. You will: Apply Python fundamentals and network automation strategies effectively. Utilize Python for streamlined network administration, boosting productivity. Consolidate Linux fundamentals and IP network services for enhanced network management. Practice implementing regular expressions in Python for network application development. Develop working Cisco IOS upgrading Python application in PoC environment. Explore Python's extensive applications in enterprise network automation for versatile solutions.

Telecommunication technology --- Mass communications --- Programming --- Python (informatica) --- tekstverwerking --- communicatietechnologie --- Python (Computer program language) --- Virtual computer systems.

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Python is used in a wide range of geoscientific applications, such as in processing images for remote sensing, in generating and processing digital elevation models, and in analyzing time series. This book introduces methods of data analysis in the geosciences using Python that include basic statistics for univariate, bivariate, and multivariate data sets, time series analysis, and signal processing; the analysis of spatial and directional data; and image analysis. The text includes numerous examples that demonstrate how Python can be used on data sets from the earth sciences. The supplementary electronic material (available online through Springer Link) contains the example data as well as recipes that include all the Python commands featured in the book. The Author Martin H. Trauth studied geophysics and geology at the University of Karlsruhe. He obtained a doctoral degree from the University of Kiel in 1995 and subsequently became a permanent member of the scientific staff at the University of Potsdam. Following his habilitation in 2003, he became a lecturer, and in 2011, he was granted a titular professorship at the University of Potsdam. Since 1990, Martin H. Trauth has worked on various aspects of past changes in the climates of eastern Africa and South America. His projects have aimed to provide a better understanding of (1) the role that the tropics play in terminating ice ages, (2) the relationship between climatic changes and human evolution, and (3) the influence that climate anomalies have had on mass movements in the central Andes. Each of these projects has involved numerical and statistical methods (e.g., time series analysis and signal processing) with paleoclimate time series, lake balance modeling, stochastic modeling of bioturbation, age-depth modeling of sedimentary sequences, or satellite- and microscopic image processing. Martin H. Trauth has taught a variety of courses on data analysis in the earth sciences for more than 25 years both at the University of Potsdam and at other universities around the world.

Geophysics --- Geology. Earth sciences --- Geography --- GIS (geografisch informatiesysteem) --- geografie --- geologie --- aarde (astronomie) --- geofysica --- Earth sciences --- Python (Computer program language) --- Data processing.

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This book describes Python3 programming resources for implementing decision aiding algorithms in the context of a bipolar-valued outranking approach. These computing resources, made available under the name Digraph3, are useful in the field of Algorithmic Decision Theory and more specifically in outranking-based Multiple-Criteria Decision Aiding (MCDA). The first part of the book presents a set of tutorials introducing the Digraph3 collection of Python3 modules and its main objects, such as bipolar-valued digraphs and outranking digraphs. In eight methodological chapters, the second part illustrates multiple-criteria evaluation models and decision algorithms. These chapters are largely problem-oriented and demonstrate how to edit a new multiple-criteria performance tableau, how to build a best choice recommendation, how to compute the winner of an election and how to make rankings or ratings using incommensurable criteria. The book's third part presents three real-world decision case studies, while the fourth part addresses more advanced topics, such as computing ordinal correlations between bipolar-valued outranking digraphs, computing kernels in bipolar-valued digraphs, testing for confidence or stability of outranking statements when facing uncertain or solely ordinal criteria significance weights, and tempering plurality tyranny effects in social choice problems. The fifth and last part is more specifically focused on working with undirected graphs, tree graphs and forests. The closing chapter explores comparability, split, interval and permutation graphs. The book is primarily intended for graduate students in management sciences, computational statistics and operations research. The chapters presenting algorithms for ranking multicriteria performance records will be of computational interest for designers of web recommender systems. Similarly, the relative and absolute quantile-rating algorithms, discussed and illustrated in several chapters, will be of practical interest to public and private performance auditors.

Statistical science --- Operational research. Game theory --- Discrete mathematics --- Mathematical statistics --- Mathematics --- Planning (firm) --- Computer. Automation --- grafieken --- informatica --- mathematische modellen --- statistiek --- econometrie --- wiskunde --- operationeel onderzoek --- Python (Computer program language)

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Use Visual Studio Code to write and debug code quickly and efficiently on any platform, for any device, using any programming language, and on the operating system of your choice. Visual Studio Code is an open source and cross-platform development tool that focuses on code editing across a variety of development scenarios, including web, mobile, and cloud development. This third edition has been updated to match the latest releases of the environment. Additional updates relate to .NET 7 and explain how to consume AI services from code you write in Visual Studio Code. This new edition is also refreshed with the latest updates, such as enhanced merge editor, creating extensions, support for the Julia language, and much more. The book teaches you how to be immediately productive with Visual Studio Code, from the basics to some of the more complex topics. You will learn how to work on individual code files, complete projects, and come away with an understanding of advanced code-editing features that will help you focus on productivity, and source code collaboration with Git. What You Will Learn Get started with practical guidance on Visual Studio Code, including expansive guidance on writing apps with C# and Python Understand how to edit individual files and how to work with folders by working on the latest workspace trust and multi-root workspaces Debug code on multiple platforms through real-world guidance, such as working under corporate networks Package code into Docker containers and deploy these to Azure Acquire valuable tips, tricks, and suggestions from hard-earned, real-world experience to be more productive.

Programming --- Computer architecture. Operating systems --- Computer. Automation --- websites --- Python (informatica) --- computers --- computerprogramma's --- programmeren (informatica) --- programmeertalen --- Microsoft software. --- Microsoft .NET Framework. --- Internet programming. --- Computer programming. --- Python (Computer program language). --- Microsoft. --- Web Development. --- Programming Techniques. --- Python. --- Internet Programming --- Computer Programming --- Python (Computer Program Language) --- Computers --- Application software --- Cross-platform software development. --- Development. --- Microsoft Visual studio. --- Microsoft Windows (Computer file) --- Mac OS. --- Linux.