Machine Learning for Predictive Maps in Python and Leaflet

Using the power of machine learning to build predictive map applications

Created by : Rated :4.6 812 views

Last Updated: 2023-03-17 09:03:42 English Course Type: Self Learning

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

Section 1 : Introduction 1 Lectures 00:10:03
Lecture 1 :
Introduction Preview
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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