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Machine Learning
 
Course Overview
A comprehensive course on comparative analysis of forecasting models for CO2 emissions prediction. The course teaches how to compare the forecasting performance of machine learning models. We begin by building a Python model that incorporates the 4 models that we have already discussed, namely the univariate linear regression, univariate ARIMA, univariate shallow neural network and univariate deep learning models. Then, we build the necessary code to store the outputs of these models and compare them. In practice, the forecasts are done using many machine learning models. For this reason, this course is important; due to its practical application.
 
Key Details
  • Prerequisite online courses: 
    • Univariate Linear Regression for CO₂-Emissions Forecasting
    • Univariate ARIMA Modelling for CO₂-Emissions Forecasting
    • Univariate Shallow Neural Networks for CO₂-Emissions Forecasting
    • Univariate Deep Neural Networks for CO₂-Emissions Forecasting
       
  • Prerequisites: None
  • Video Duration:  2 hours, 27 minutes
  • Last Updated: September 2025
  • Price: 100 GBP
  • Access: Lifetime with updates 
  • Downloads: All code, datasets, and slides included
  • Support: Access to instructor included
  • Certificate: Yes, upon watching all videos, at no extra cost
  • Format: Private, self-paced, focused learning with no distractions
  • Community: For networking with other learners, join the dedicated Skool community
  • Instructor: Dr. Spyros Giannelos, Imperial College London

Comparing Machine-Learning Forecasts: CO₂-Emissions Case Study

£100.00Price

    Courses

    Comparing Machine-Learning Forecasts: CO₂-Emissions Case Study

    £100.00

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    Lifetime access to all videos of all courses.

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    Lifetime access to all videos of all courses.

    Ellipse 130.png

    Lifetime access to all videos of all courses.

    Ellipse 130.png

    Lifetime access to all videos of all courses.

    Ellipse 130.png

    Lifetime access to all videos of all courses.

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    Lifetime access to all videos of all courses.

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    © 2026 Dr Spyros Giannelos
    London, United Kingdom
    spyros@spyrosgiannelos.com
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