Machine Learning for Predictive Maps in Python and Leaflet

Overview Machine Learning for Predictive Maps in Python and Leaflet introduces participants to the dynamic field of predictive mapping, where …

Machine Learning for Predictive Maps in Python and Leaflet

Machine Learning for Predictive Maps in Python and Leaflet

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Machine Learning for Predictive Maps in Python and Leaflet Overview

Machine Learning for Predictive Maps in Python and Leaflet introduces participants to the dynamic field of predictive mapping, where machine learning meets cartography. This comprehensive course equips learners with the knowledge and skills needed to build predictive mapping applications using Python and Leaflet, from setting up the development environment to implementing machine learning algorithms for predictive modelling.

The course is structured into eight sections, beginning with an introduction to predictive mapping concepts and the setup and installation of necessary tools. Participants then delve into writing server-side code using Django and front-end code for interactive map interfaces. The machine learning section explores various algorithms for predictive modelling, while automation techniques are covered to streamline the machine learning pipeline. Leaflet programming enhances map visualisations, and participants have access to project source code for hands-on learning.

By the course’s conclusion, participants will have a deep understanding of predictive mapping principles and practical proficiency in building end-to-end predictive mapping applications. They will be able to analyse real-world datasets, generate predictive insights, and visualise them effectively using interactive maps.

This course is suitable for individuals interested in predictive analytics, machine learning, web development, and geographic information systems (GIS). Whether you’re a student, data analyst, web developer, GIS professional, or aspiring data scientist, this course provides a solid foundation in predictive mapping techniques, opening doors to exciting career opportunities in predictive analytics, GIS, and machine learning.

Machine Learning for Predictive Maps in Python and Leaflet – Learning Outcomes

  • Gain a comprehensive understanding of predictive mapping concepts.
  • Set up the development environment and install necessary tools.
  • Develop server-side code using Django for data management.
  • Implement front-end code for creating interactive map interfaces.
  • Utilise machine learning algorithms for predictive modelling.
  • Automate the machine learning pipeline for enhanced efficiency.
  • Leverage Leaflet programming to enrich map visualisations.
  • Access project source code for practical learning and practice.
  • Analyse real-world datasets and generate predictive insights.
  • Demonstrate expertise in constructing end-to-end predictive mapping applications.

Machine Learning for Predictive Maps in Python and Leaflet -Who Is This Course For

This course caters to individuals interested in predictive analytics, machine learning, web development, and geographic information systems (GIS). It is suitable for students, data analysts, web developers, GIS professionals, and anyone looking to explore the intersection of machine learning and cartography.

Entry Requirements

  • Python Skill: A basic grasp of Python, although extensive experience is not required.
  • ML Knowledge: No prior experience in predictive mapping or machine learning required.
  • Programming Skill: A rudimentary understanding of programming concepts.
  • Data Knowledge: A foundational understanding of data, although advanced expertise is not necessary.

Why Choose Us

  • Affordable, engaging & high-quality e-learning study materials;
  • Tutorial videos/materials from the industry-leading experts;
  • Study in a user-friendly, advanced online learning platform;
  • Efficient exam systems for the assessment and instant result;
  • The UK & internationally recognised accredited
  • Access to course content on mobile, tablet or desktop from anywhere, anytime;
  • The benefit of career advancement opportunities;
  • 24/7 student support via email.

Career Path

Upon completion of this course, participants can pursue careers as predictive analysts, machine learning engineers, GIS specialists, web developers, data scientists, and geospatial analysts.

Course Curriculum

Section 01: Introduction
Introduction 00:10:00
Section 02: Setup And Installations
Python Installation 00:04:00
Creating a Python Virtual Environment 00:07:00
Installing Django 00:09:00
Installing Visual Studio Code IDE 00:06:00
Installing PostgreSQL Database Server Part 1 00:03:00
Installing PostgreSQL Database Server Part 2 00:08:00
Section 03: Writing The Django Server-Side Code
Adding the settings.py Code 00:07:00
Creating a Django Model 00:10:00
Adding the admin.py Code 00:21:00
Section 04: Writing The Application Front-End Code
Creating Template Files 00:10:00
Creating Django Views 00:10:00
Creating URL Patterns for the REST API 00:09:00
Adding the index.html code 00:04:00
Adding the layout.html code 00:19:00
Creating our First Map 00:10:00
Adding Markers 00:16:00
Section 05: Machine Learning
Installing Jupyter Notebook 00:07:00
Data Pre-processing 00:31:00
Model Selection 00:20:00
Model Evaluation and Building a Prediction Dataset 00:11:00
Section 06: Automating The Machine Learning Pipeline
Creating a Django Model 00:04:00
Embedding the Machine Learning Pipeline in the Application 00:42:00
Creating a URL Endpoint for our Prediction Dataset 00:06:00
Section 07: Leaflet Programming
Creating Multiple Basemaps 00:09:00
Creating the Marker Layer Group 00:10:00
Creating the Point Layer Group 00:12:00
Creating the Predicted Point Layer Group 00:07:00
Creating the Predicted High Risk Point Layer Group 00:12:00
Creating the Legend 00:09:00
Creating the Prediction Score Legend 00:15:00
Section 08: Project Source Code
Resource 00:00:00
Order Certificate
Order Certificate 00:00:00

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