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  • Web Mapping
  • Data Transformation and Manipulation
  • Python and GeoDjango
  • Geospatial Machine Learning
  • Data Mapping and Visualization
  • Web GIS Programming

Welcome to the Machine Learning for Predictive Maps in Python and Leaflet course.

In this course we will be building a earthquake forecasting map application,

by using a variety of independent tools and then integrate them to produce a full stack web gis application.

 

We will be writing code in multiple programming languages, which gives us experience

with different stacks of an application and different tools.

 

We will be covering various topics ranging from web gis, python programming, data analysis,

machine learning and geo data visualization. All of our development will be done on windows 10.

 

  • You will learn how to build a full stack web gis application

  • You will learn how to build predictive models

  • You will learn how to build a prediction engine that's embedded in the application

  • You will learn how to build and automate a machine learning pipeline

  • You will learn how to use multiple basesmaps and layers

  • You will learn programming in leaflet.js

  • You will learn how to create REST API endpoints and call them with Ajax and JQUERY

  • You will learn how to use the Django template engine to pass data from the back-end to the front-end of the application

  • You will learn how to integrate a PostgreSQL database with Django

  • You will also learn how to visualize data on a map

  • Basic Understanding of Python
  • Little or no understanding of GIS
  • Basic understanding of Programming concepts
  • Basic understanding of Data
  • Basic understanding of what Machine Learning is
  • Python Developers at any level
  • GIS Developers at any level
  • Developers at any level
  • Machine Learning engineers at any level
  • The curious mind
View More...
  • Section 1 : Introduction 1 Lectures 00:10:03

    • Lecture 1 :
  • Section 2 : Setup and Installations 6 Lectures 00:37:50

    • Lecture 1 :
    • Python Installation
    • Lecture 2 :
    • Creating a Python Virtual Environment
    • Lecture 3 :
    • Installing Django
    • Lecture 4 :
    • Installing Visual Studio Code IDE
    • Lecture 5 :
    • Installing PostgreSQL Database Server Part 1
    • Lecture 6 :
    • Installing PostgreSQL Database Server Part 2
  • Section 3 : Writing the Django Server-Side Code 3 Lectures 00:17:28

    • Lecture 1 :
    • Adding the settings.py Code
    • Lecture 2 :
    • Creating a Django Model
    • Lecture 3 :
    • Adding the admin.py Code
  • Section 4 : Writing the Application Front-end Code 7 Lectures 00:58:54

    • Lecture 1 :
    • Creating Template Files
    • Lecture 2 :
    • Creating Django Views
    • Lecture 3 :
    • Creating URL Patterns for the REST API
    • Lecture 4 :
    • Adding the index.html code
    • Lecture 5 :
    • Adding the layout.html code
    • Lecture 6 :
    • Creating our First Map
    • Lecture 7 :
    • Adding Markers
  • Section 5 : Machine Learning 4 Lectures 00:18:01

    • Lecture 1 :
    • Data Pre-processing
    • Lecture 2 :
    • Installing Jupyter Notebook
    • Lecture 3 :
    • Model Selection
    • Lecture 4 :
    • Model Evaluation and Building a Prediction Dataset
  • Section 6 : Automating the Machine Learning Pipeline 3 Lectures 00:10:05

    • Lecture 1 :
    • Embedding the Machine Learning Pipeline in the Application
    • Lecture 2 :
    • Creating a Django Model
    • Lecture 3 :
    • Creating a URL Endpoint for our Prediction Dataset
  • Section 7 : Leaflet Programming 7 Lectures 01:12:24

    • Lecture 1 :
    • Creating Multiple Basemaps
    • Lecture 2 :
    • Creating the Marker Layer Group
    • Lecture 3 :
    • Creating the Point Layer Group
    • Lecture 4 :
    • Creating the Predicted Point Layer Group
    • Lecture 5 :
    • Creating the Predicted High Risk Point Layer Group
    • Lecture 6 :
    • Creating the Legend
    • Lecture 7 :
    • Creating the Prediction Score Legend
  • Section 8 : Project Source Code 1 Lectures

    • Lecture 1 :
    • Source Code and Notebook
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