Deep Learning for time-series forecasting on Carbon Dioxide

Use Machine Learning methodologies in Python - a step by step methodology for accurate forecasts

Created by: Research Lead - Dr. SGiannelos
Data Science, Optimization, ML applied to Energy

What Will I Learn?

• CODE: ECFF BF56 13866 6C8DD2C : Use this code at check-out (remove spaces!) if you want to buy the course AND want me to email you my book + my publications!
• Download all the code with just one click!
• Download many academic publications, for extra insights!
• If you need help, just send me a personal message, or ask in the Q&A forum. I reply within hours!
• The course teaches how to develop deep learning models for time-series forecasting of CO2 emissions. The real-world model is developed in Python.
• The course is regularly updated. Come back here every 6-12 months to download the updated code & publications!

Requirements

• No prerequisites except basic Python.

Target audience

• Data scientists and analysts interested in applying deep learning techniques to environmental data and forecasting.
• Climate researchers and environmental professionals looking to enhance their skills in predictive modeling for CO2 emissions.
• Python programmers and developers eager to learn how to build time-series forecasting models using deep learning frameworks.
• Policy makers, energy analysts, and sustainability consultants who need accurate long-term CO2 forecasts to inform decision-making.
• Graduate students or academics in fields such as environmental science, data science, or machine learning, seeking practical applications in forecasting.