Experience the future of geographical analysis with our Machine Learning for Predictive Maps in Python and Leaflet course. Master the unique blend of programming, machine learning, and geographic information systems, all while honing your ability to predict and visualise spatial data in a powerful and effective way. This course offers you an unparalleled understanding of modern map creation, combined with the magic of prediction using machine learning models.
Starting from the ground up, you’ll be introduced to all the necessary setups and installations. After that, you will be diving into the depth of Django server-side code and front-end application code writing. The heart of the course lies in learning how to automate the machine learning pipeline, leading you to easily create predictive models.
Improve your maps with Leaflet programming, making your predictions accurate and also visually striking. By the end of this course, you will be armed with experience furnished by our comprehensive project source code and assignments, empowering you to drive data-driven decisions and insightful spatial analysis. Join us and map your way to success! Sign up today.
Learning Outcomes:
Upon completion of the Machine Learning course, you will be able to:
Understand how to set up and install relevant software and libraries.
Master Django server-side and application front-end code writing.
Gain proficiency in the concepts and implementation of Machine Learning.
Learn to automate Machine Learning pipelines for efficient workflows.
Acquire skills in Leaflet programming for enhanced map visuals.
Handle project source code effectively for real-world projects.
Apply knowledge practically via assignments and gain experience.
Who is this course for?
This Machine Learning course is ideal for:
Aspiring Data Scientists keen on harnessing geographical data.
GIS professionals aiming to integrate Machine Learning into their skill set.
Software Developers interested in creating geographically-focused applications.
Analysts keen on enhancing their data visualisation skills with mapping.
Certification
After studying the course materials of the Machine Learning for Predictive Maps in Python and Leaflet course, there will be a written assignment test which you can take either during or at the end of the course. After successfully passing the test, you have a range of certification options to choose from. You can claim a CPD Accredited PDF Certificate for £4.99, a CPD Accredited Hardcopy Certificate at £8.00, or you may choose to receive a PDF Transcript for £4.99 or a Hardcopy Transcript for £9.99. Select according to your needs, and we assure timely delivery of your chosen certificate.
Requirements
This professionally designed Machine Learning for Predictive Maps in Python and Leaflet course does not require you to have any prior qualifications or experience. It is open to everyone. You will be able to access the course from anywhere at any time. Just enrol and start learning!
Career Path:
Our Machine Learning course will help you to pursue a range of career paths, such as:
Junior Data Analyst: £25,000 – £35,000 annually.
Data Scientist: £40,000 – £60,000 annually.
GIS Analyst: £30,000 – £45,000 annually.
Geospatial Software Developer: £35,000 – £55,000 annually.
Machine Learning Engineer: £50,000 – £80,000 annually.
Lead Data Scientist (GIS speciality): £70,000 – £100,000+ annually.
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 | 00:09:00 | ||
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 | ||
Assignment | |||
Assignment – Machine Learning for Predictive Maps in Python and Leaflet | 00:00:00 |
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