Predicting Extreme Climate Behavior with Machine Learning
Learn ML techniques to analyze and predict extreme climate events using Python.This course explores machine learning techniques.
Learn ML techniques to analyze and predict extreme climate events using Python.This course explores machine learning techniques.
This course explores machine learning techniques for predicting extreme climate behavior. Students will learn both unsupervised and supervised learning algorithms, including dimensionality reduction, clustering, regression, and neural networks. The curriculum covers practical applications of these techniques to real-world climate datasets using Python. Participants will gain hands-on experience in implementing various ML algorithms, from PCA and SVD to decision trees and SVMs. The course emphasizes the analysis and prediction of extreme climate events, providing a strong foundation in both theoretical concepts and practical skills for data scientists interested in climate modeling.
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What you'll learn
Analyze and apply various machine learning algorithms to climate data
Implement dimensionality reduction techniques like PCA and SVD
Develop clustering methods for climate data segmentation
Apply supervised learning algorithms including regression and classification
Create and train neural networks for climate prediction tasks
Evaluate and interpret machine learning model performance
Skills you'll gain
This course includes:
4 Hours PreRecorded video
4 assignments
Access on Mobile, Tablet, Desktop
FullTime access
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There are 5 modules in this course
This course provides a comprehensive exploration of machine learning techniques applied to climate data analysis and prediction. Students will learn both unsupervised and supervised learning algorithms, including dimensionality reduction (PCA/SVD), clustering, regression, classification, and neural networks. The curriculum covers practical applications of these techniques to real-world climate datasets using Python. Participants will gain hands-on experience in implementing various ML algorithms and evaluating their performance through case studies focused on extreme climate events. The course emphasizes both theoretical understanding and practical skills in applying machine learning to climate science challenges.
Unsupervised Learning: Dimensionality Reduction
Module 1 · 4 Hours to complete
Unsupervised Learning: Clustering
Module 2 · 4 Hours to complete
Supervised Learning: Regressions
Module 3 · 3 Hours to complete
Supervised Learning: Logistic Regression, Decision Trees, and SVMs
Module 4 · 7 Hours to complete
Supervised Learning: Neural Networks
Module 5 · 4 Hours to complete
Fee Structure
Instructor
Assistant Professor at the University of Colorado Boulder
Dr. Osita Onyejekwe is an Assistant Professor at the University of Colorado Boulder, where he specializes in multivariate regression models and machine learning techniques. His research focuses on estimating weather patterns, analyzing glacier recession behavior, and developing financial models related to profit gains, losses, and revenue. In addition to his quantitative research interests, Dr. Onyejekwe explores topics in planetary systems, abiogenesis, philosophy, and theology, reflecting a diverse academic curiosity that bridges the sciences and humanities. His interdisciplinary approach aims to contribute valuable insights across various fields while enhancing the understanding of complex systems and their interactions.
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