Udemy - Geospatial APIs For Data Science Applications In Python

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[ CourseBoat.com ] Udemy - Geospatial APIs For Data Science Applications In Python
  • Get Bonus Downloads Here.url (0.2 KB)
  • ~Get Your Files Here ! 1. Welcome to the Course
    • 1. What Is This Course About.mp4 (25.8 MB)
    • 2. Data and Code.html (0.2 KB)
    • 3. Python Installation.mp4 (39.3 MB)
    • 4. What Is Google CoLab.mp4 (36.7 MB)
    • 5. Google Colabs and GPU.mp4 (27.6 MB)
    • 6. Google Colab Packages.mp4 (26.5 MB)
    • 7. Introduction To Basic Spatial Data Concepts.mp4 (101.7 MB)
    2. Introduction to Geospatial APIs (and Other Sources of GIS Data)
    • 1. What Are APIs.mp4 (30.7 MB)
    • 10. Retrieve the Venues Corresponding To Mumbai's Neighbourhoods.mp4 (53.4 MB)
    • 2. Singapore MRT.mp4 (28.9 MB)
    • 3. Basic Geocoding.mp4 (21.3 MB)
    • 4. Geocode A Dataframe of Cities.mp4 (20.5 MB)
    • 5. Introduction To The Foursquare API.mp4 (44.5 MB)
    • 6. Get Started With the Foursquare API.mp4 (24.9 MB)
    • 7. Obtain Venues and Their Details Around a Particular Location.mp4 (34.1 MB)
    • 8. Visualise the Foursquare Venues.mp4 (115.9 MB)
    • 9. Retrieve Venues On the Basis of Lat Long Coordinates.mp4 (35.7 MB)
    3. Other Source of Geospatial Data
    • 1. Access Open Street Data.mp4 (41.6 MB)
    • 2. Obtain World Bank Data.mp4 (52.6 MB)
    4. Introduction To Google Earth Engine (GE)
    • 1. What is GEE.mp4 (36.2 MB)
    • 2. Sign Up For GEE.mp4 (32.7 MB)
    • 3. Datasets Within GEE.mp4 (100.2 MB)
    5. Obtaining GEE Data Via API To Use With Python
    • 1. Accessing GEE API Within Python.mp4 (4.6 MB)
    • 10. Upload External Data On GEE.mp4 (49.8 MB)
    • 2. Introduction To Geemap.mp4 (46.1 MB)
    • 3. Start Exploring Feature Collections.mp4 (33.2 MB)
    • 4. Filter and Visualise Shapefiles.mp4 (57.1 MB)
    • 5. Identify the Biggest Country.mp4 (19.8 MB)
    • 6. Filter Based on Numerical Attributes.mp4 (48.7 MB)
    • 7. Grouping Feature Collections By Attributes.mp4 (36.6 MB)
    • 8. Create a GeoJSON Bounding Box.mp4 (27.8 MB)
    • 9. Clip Image To Shapefile Extent.mp4 (29.8 MB)
    6. Working With GEE's Imagery Data
    • 1. Access Image Collections Within Google Colab.mp4 (15.9 MB)
    • 10. Why Do We Need Radar Data.mp4 (30.6 MB)
    • 11. Obtaining Sentinel-1 Data From GEE.mp4 (31.7 MB)
    • 12. Visualise Sentinel-1 Data.mp4 (29.7 MB)
    • 13. Obtain Time Series Landsat Data From GEE.mp4 (52.2 MB)
    • 2. See Images Side By Side.mp4 (45.0 MB)
    • 3. Topographic Computations.mp4 (13.9 MB)
    • 4. Clip Image Collection To Shapefile Extent.mp4 (39.5 MB)
    • 5. Improve Your Clipped Image.mp4 (21.3 MB)
    • 6. Time Series Visualization.mp4 (32.2 MB)
    • 7. What Are Multispectral Data.mp4 (45.1 MB)
    • 8. Using Multispectral Data Case of Tonle Sap.mp4 (28.0 MB)
    • 9. Flood Mapping.mp4 (36.5 MB)
    7. Getting a Sense of Our Data
    • 1. What Are Pandas.mp4 (69.7 MB)
    • 2. Principles of Data Visualisation.mp4 (94.2 MB)
    • 3. Some Theoretical Principles Behind Data Visualisation.mp4 (71.6 MB)
    • 4. Visualise Time Series Geospatial Data With Pandas.mp4 (11.2 MB)
    • 5. Where Are Singapore's MRT Stations Located.mp4 (58.7 MB)
    • 6. Let's Colour Code These Stations-Part 1.mp4 (38.9 MB)
    • 7. Let's Colour Code These Stations- Part 2.mp4 (39.0 MB)
    8. Machine Learning
    • 1. What is Machine Learning (ML).mp4 (108.5 MB)
    • 10. How Good Are My Results.mp4 (18.3 MB)
    • 11. Accuracy.mp4 (58.1 MB)
    • 12. Spectral Unmixing.mp4 (45.6 MB)
    • 13. Supervised Classification With Geolocations Introduction (Part 1).mp4 (37.0 MB)
    • 14. Supervised Classification Geolocation Training Data.mp4 (58.8 MB)
    • 15. Classify The Image.mp4 (48.0 MB)
    • 16. Combine EO Data From Different Sensors-Problem.mp4 (37.8 MB)
    • 17. Supervised Classification Sentinel-1 and Sentinel-2.mp4 (42.6 MB)
    • 18. Supervised Classification Sentinel VIs.mp4 (37.9 MB)
    • 19. Visualise the Classification results.mp4 (37.9 MB)
    • 2. Training Data.mp4 (58.6 MB)
    • 3. Unsupervised LearningTheory.mp4 (28.4 MB)
    • 4. k-means.mp4 (18.2 MB)
    • 5. Clustering Landcovers in Cambodia-Part1.mp4 (76.4 MB)
    • 6. Clustering Landcovers in Cambodia-Part 2.mp4 (55.8 MB)
    • 7. Supervised Classification.mp4 (69.7 MB)
    • 8. Random Forest.mp4 (28.9 MB)
    • 9. Basic Supervised Classification With MODIS For Training Samples.mp4 (77.0 MB)
    • Bonus Resources.txt (0.3 KB)

Description

Geospatial APIs For Data Science Applications In Python



MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English | Duration: 71 lectures (5h 33m) | Size: 2.84 GB
Deploy Application Programming Interfaces (APIs) For Obtaining Geospatial Data and Carry Out Data Science Based Analysis
What you'll learn:
Learn how to work with online Jupyter notebooks through
Gain robust grounding in working with geospatial APIs using Python
Apply data science methods on geospatial data
Deploy the Google Earth Engine (GEE) API within the Python ecosystem
Use GEE's datasets for visualisation and geospatial analysis

Requirements
Prior exposure to geospatial concepts
Prior knowledge of Python programming concepts
Desire to use geospatial APIs for data science applications

Description
ENROLL IN MY LATEST COURSE ON HOW TO LEARN ALL ABOUT OBTAINING AND WORKING WITH WITH FREE GEOSPATIAL DATA OBTAINED VIA APPLICATION PROGRAMMING INTERFACES (APIs) USING DATA SCIENCE TECHNIQUES.

Are you currently enrolled in any of my GIS and remote sensing related courses?

https://CourseBoat.com



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Udemy - Geospatial APIs For Data Science Applications In Python


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3 GB
seeders:11
leechers:7
Udemy - Geospatial APIs For Data Science Applications In Python


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