International Training on AI & Machine Learning in Environmental Science
BackgroundObjectivesWhat You Will LearnModulesParticipantsContactCourse Background
The exponential growth of environmental data—from satellites, sensors, drones, and citizen science—demands advanced analytical methods. AI and ML provide transformative tools for pattern recognition, predictive modeling, and automated environmental decision-making.
ECAS Institute offers this course to equip participants with foundational knowledge and practical skills in AI/ML applied to environmental science, enabling learners to build predictive models, analyze complex data, and contribute to sustainable solutions.
Course Objectives
Understand core concepts of AI and ML and their relevance to environmental science.Identify various ML algorithms (supervised, unsupervised, deep learning) and applications.Apply AI/ML techniques for data preprocessing, feature engineering, and model training.Utilize AI/ML tools for prediction, classification, and pattern recognition in environmental datasets.Interpret and evaluate model performance and limitations.Explore real-world AI/ML applications in climate, biodiversity, pollution, and natural resource management.Understand ethical considerations in deploying AI/ML in environmental contexts.What You Will Learn
Core principles of AI/ML, including learning algorithms and predictions.Differences between machine learning types and their environmental applications.Data preparation and preprocessing for environmental AI/ML.Implement basic ML models for prediction, classification, and pattern detection.Familiarity with key programming libraries (Python, scikit-learn, TensorFlow/Keras) conceptually.Assess model strengths, weaknesses, and biases.Real-world case studies of AI/ML in climate, conservation, pollution control, and resource management.Ethical and societal implications of AI/ML in environmental science.Target Participants
Environmental scientists and researchers (ecologists, hydrologists, climate scientists).GIS analysts and remote sensing specialists.Data analysts transitioning to environmental domains.Conservationists and biodiversity specialists.Environmental consultants and policy analysts.Training Modules
| No | Module | Details |
|---|
| 1 | Introduction to AI, ML, and Environmental Data | Definitions, environmental relevance, typical datasets, AI/ML workflow for environmental problems. |
| 2 | Fundamentals of Machine Learning (Supervised Learning) | Supervised vs unsupervised learning, regression, classification, common algorithms (Linear, Logistic Regression, Decision Trees). |
| 3 | Fundamentals of Machine Learning (Unsupervised & Beyond) | Clustering, dimensionality reduction, ensemble methods, introduction to deep learning. |
| 4 | Data Preprocessing & Feature Engineering | Data cleaning, normalization, standardization, feature creation, geospatial preprocessing. |
| 5 | Practical AI/ML Tools & Platforms | Python environment, key ML libraries (scikit-learn, pandas, numpy), basic model implementation, cloud-based AI platforms overview. |
| 6 | Evaluating AI/ML Models | Training, validation, testing, evaluation metrics (RMSE, accuracy, F1-score), overfitting, model limitations. |
| 7 | Key Applications of AI/ML in Environmental Science | Climate prediction, biodiversity conservation, pollution monitoring, resource management, disaster management, GIS/remote sensing applications. |
| 8 | Ethical Considerations & Future of AI | Bias, data privacy, accountability, AI for SDGs, emerging trends in environmental AI/ML. |
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Contact & Registration
For registration or tailor-made courses, contact: info@ecasiafrica.org
Payment should be sent to our bank account before the training start date, with proof of payment sent to the same email.
About ECAS Institute
ECAS Institute delivers independent training, research, and consulting services focusing on climate change, renewable energy, biodiversity conservation, agriculture, and food systems. Based in Nairobi, Kenya, ECAS operates across Africa and globally, partnering with organizations from the UK, Denmark, Italy, Sweden, Germany, and the USA.
