Tutorials

Research Synthesis using NLP in the Field of Climate Change

Explore how Natural Language Processing (NLP) can be used to identify and map climate-relevant literature.

Education Introductory Python Natural Language Processing Academic Literature Data Science

Building Load Forecasting with Machine Learning

Accurate forecasts of energy demand and supply are essential to mitigate climate change. Discover how to train and evaluate building load forecasts using off-the-shelf ML models.

Buildings & Cities Intermediate Python Time Series Analysis Energy Management

Building Control with RL using BOPTEST

Apply reinforcement learning to a building emulator to intelligently control HVAC systems.

Check out the recorded talk for more information.

Buildings & Cities Intermediate Python Reinforcement Learning Energy Management

Land Use and Land Cover (LULC) Classification using Deep Learning: Part I

Mapping the extent of land use and land cover categories over time is essential for better environmental monitoring, urban planning and nature protection. Train and fine-tune a deep learning model to classify satellite images into 10 LULC categories.

Check out the introductory slides for more information.

Farms & Forests Intermediate Python Deep Learning Computer Vision Remote Sensing Land Use Land Classification (LULC)

Land Use and Land Cover (LULC) Classification using Deep Learning: Part II

Use the neural network trained in LULC Classification Part I to generate land use and land cover maps with Python GIS.

Check out the introductory slides for more information.

Farms & Forests Intermediate Python Geospatial Deep Learning Computer Vision Remote Sensing Land Use Land Classification (LULC)

Practical ML Tools for CC

This series of tutorials will teach you to apply regression and classification techniques to problems in the climate space.
Module 0.0: Learn how to pattern match within images.
Module 0.1: Train a CNN to determine when a solar cell is defective.
Module 1.0: Learn the basics of working with Python in a Google Colab notebook.
Module 1.1: Explore the relationship between CO2 and worldwide temperatures.
Module 1.2: Estimate a function for the time variation of global temperature using periodic expressions and first principles.
Module 1.3: Determine the likelihood that current CO2 levels are consistent with recent history on Earth.
Module 1.4: Learn how to apply Bayesian time series modeling to predict rising global temperatures.

Check out the introductory slides for more information.

Climate Prediction Introductory Python Solar Energy Deep Learning Climate Modeling Time Series Analysis