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5.0 (1,874)·34h 18m · 323 lectures
$3.00$12–$199up to −98%
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CO₂ Emissions Forecasting with Shallow Neural Nets in Python
Build Accurate Time Series Forecasts with Python - Energy Sector Application
5.0166 reviews1,500 learners
- All Levels
- 4h 28m video
- 29 lectures
- English
- Captions
- Updated 2026
Taught by Algorithmic Economist, PhD
- 4h 28m of on-demand video
- 29 lectures across 7 sections
- One-off payment — no subscription
- Lifetime access on web and mobile
- Closed captions (English)
Curriculum
7 sections29 lectures4h 28m total
- Introduction4:28
- Additional Case Studies0:31
- Multivariate versus univariate models5:45
- Overview of the ten-step methodology4:33
About this course
WHO I AM: I hold a PhD from Imperial College London. My expertise is in power system economics, energy finance and energy markets, using machine learning, data science and optimisation. BONUS: After you enrol, visit the very last lecture for a special gift. What You'll Learn: How to build a Shallow Neural Network model in Python that can forecast CO₂ emissions How to achieve high accuracy in the forecasts that you will produce How to work with World Bank historical data How to implement advanced statis…
Before you start
- Absolute beginners welcome!
- You'll receive the full Python code, which you can adjust to your own projects
- No programming experience? Follow along and learn by doing
- No statistics background needed
- Just need a computer and enthusiasm
Who it’s for
- Environmental/Climate Analysts seeking quantitative forecasting skills
- Sustainability Professionals needing to project emissions for reporting
- Energy Sector Professionals wanting data-driven analytical methods
- Graduate Students & Researchers in environmental science, energy, or climate studies
- Data Scientists/ML Engineers moving into climate and energy applications
- ESG Analysts & Consultants requiring emissions projection capabilities
- Policy Analysts working on climate strategies and carbon reduction plans
- Anyone transitioning to climate tech who needs practical forecasting skills
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