Machine Learning on Geographical Data Using Python: Introduction into Geodata w,

Description: About this productProduct IdentifiersPublisherApress L. P.ISBN-101484282868ISBN-139781484282861eBay Product ID (ePID)26057278524Product Key FeaturesNumber of PagesXv, 312 PagesLanguageEnglishPublication NameMachine Learning on Geographical Data Using Python : Introduction into Geodata with Applications and Use CasesPublication Year2022SubjectIntelligence (Ai) & Semantics, Probability & Statistics / General, Programming Languages / PythonTypeTextbookSubject AreaMathematics, ComputersAuthorJOOS KorstanjeFormatTrade PaperbackDimensionsItem Weight22 OzItem Length10 inItem Width7 inAdditional Product FeaturesDewey Edition23CLASSIFICATION_METADATA{"IsNonfiction":["No"],"IsOther":["Yes"],"IsAdult":["No"],"MuzeFormatDesc":["Trade Paperback"],"IsChildren":["No"],"Genre":["MATHEMATICS","COMPUTERS"],"Topic":["Intelligence (AI) & Semantics","Probability & Statistics / General","Programming Languages / Python"],"IsTextBook":["No"],"IsFiction":["No"]}Number of Volumes1 vol.IllustratedYesDewey Decimal910.2855133Table Of ContentChapter 1: Introduction to Geodata.- Chapter 2: Coordinate Systems and Projections.- Chapter 3: Geodata Data Types: Points, Lines, Polygons, Raster.- Chapter 4: Creating Maps.- Chapter 5: Basic Operations 1: Clipping and Intersecting in Python.- Chapter 6: Basic Operations 2: Buffering in Python.- Chapter 7: Basic Operations 3: Merge and Dissolve in Python.- Chapter 8: Basic Operations 4: Erase in Python.- Chapter 9: Machine Learning: Interpolation.- Chapter 10: Machine Learning: Classification.- Chapter 11: Machine Learning: Regression.- Chapter 12: Machine Learning: Clustering.- Chapter 13: Conclusion.SynopsisGet up and running with the basics of geographic information systems (GIS), geospatial analysis, and machine learning on spatial data in Python. This book starts with an introduction to geodata and covers topics such as GIS and common tools, standard formats of geographical data, and an overview of Python tools for geodata. Specifics and difficulties one may encounter when using geographical data are discussed: from coordinate systems and map projections to different geodata formats and types such as points, lines, polygons, and rasters. Analytics operations typically applied to geodata are explained such as clipping, intersecting, buffering, merging, dissolving, and erasing, with implementations in Python. Use cases and examples are included. The book also focuses on applying more advanced machine learning approaches to geographical data and presents interpolation, classification, regression, and clustering via examples and use cases. This book is your go-to resource for machine learning on geodata. It presents the basics of working with spatial data and advanced applications. Examples are presented using code (accessible at github.com/Apress/machine-learning-geographic-data-python) and facilitate learning by application. What You Will Learn Understand the fundamental concepts of working with geodata Work with multiple geographical data types and file formats in Python Create maps in Python Apply machine learning on geographical data Who This Book Is For Readers with a basic understanding of machine learning who wish to extend their skill set to analysis of and machine learning on spatial data while remaining in a common data science Python environmentLC Classification NumberQ325.5-.7Copyright Date2022ebay_catalog_id4

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