ECAS Institute Training Courses

Big Data, AI & Machine Learning, Climate Modeling & Deep Learning

Big Data in Environmental Applications

Course Background

The digital age has led to an explosion of environmental data from satellites, sensors, citizen science, and climate models. ECAS Institute’s introductory course provides foundational knowledge on Big Data concepts, tools, and applications in environmental science.

Objectives

Define Big Data and its 5 Vs within an environmental context.

Identify common sources of environmental Big Data.

Understand challenges and opportunities in environmental data analysis.

Gain familiarity with Big Data processing and storage architectures.

Recognize entry-level tools for data exploration and visualization.

Modules

NoModuleDetails1Introduction to Big Data: The 5 VsDefinition, characteristics, relevance, and ecosystem overview.2Sources of Environmental Big DataRemote sensing, IoT sensors, GIS, citizen science, social media, climate models.3Challenges & OpportunitiesData quality, storage, governance, privacy, real-time monitoring, predictive modeling, case studies.4Big Data Processing & StorageDistributed computing, cloud concepts, data lakes, data warehouses, relevant formats.5Big Data Analytics & ToolsOverview of analytics, machine learning concepts, Hadoop/Spark, visualization tools.6Case Studies & Future TrendsClimate change, biodiversity, smart cities, AI integration, ethical considerations.

Artificial Intelligence (AI) & Machine Learning (ML) in Environmental Science

Course Background

AI and ML are revolutionizing environmental science by enabling predictive modeling, pattern recognition, and data-driven decision-making. This course bridges theory and practical application in environmental datasets.

Objectives

Understand AI/ML fundamentals and relevance to environment.

Apply supervised, unsupervised, and deep learning algorithms.

Use AI/ML tools for prediction, classification, and feature engineering.

Evaluate model performance and interpret outputs.

Explore real-world environmental applications and ethical considerations.

Modules

NoModuleDetails1Introduction to AI, ML & Environmental DataDefinitions, workflow, datasets, and importance of AI/ML.2Supervised LearningRegression, classification, key algorithms.3Unsupervised Learning & BeyondClustering, dimensionality reduction, ensemble methods, intro to deep learning.4Data Preprocessing & Feature EngineeringData cleaning, transformation, geospatial processing.5Practical AI/ML ToolsPython environment, libraries, cloud-based platforms overview.6Evaluating ModelsMetrics for regression & classification, overfitting, model interpretation.7ApplicationsClimate prediction, biodiversity, pollution monitoring, resource management.8Ethical Considerations & FutureBias, privacy, accountability, AI for SDGs, emerging trends.

Machine Learning for Climate Modeling and Prediction

Course Background

Machine Learning provides cutting-edge tools to analyze complex climate datasets and enhance predictions. This course equips professionals with practical ML skills to improve climate models and forecast extreme events.

Objectives

Understand ML concepts relevant to climate science.

Apply supervised and unsupervised learning to climate datasets.

Use deep learning models for complex climate phenomena.

Evaluate strengths and limitations of ML in climate modeling.

Develop predictive models for extreme events and climate trends.

Modules

NoModuleDetails1Introduction to ML for Climate ScienceAI/ML overview, challenges, paradigms.2Data Preprocessing & EDAClimate data sources, cleaning, normalization, exploratory analysis.3Unsupervised LearningClustering, dimensionality reduction, anomaly detection.4Supervised Learning (Regression)Predict continuous variables, time series forecasting, case studies.5Supervised Learning (Classification)Predict categorical outcomes, logistic regression, decision trees, SVMs.6Deep LearningNeural networks, CNNs for imagery, RNNs/LSTMs for time-series, physics-informed ML.7Model Evaluation & InterpretabilityAdvanced evaluation, Explainable AI, uncertainty quantification.8Applications & Future DirectionsRenewable energy, environmental monitoring, climate policy, emerging trends.

Deep Learning for Environmental Image Analysis

Course Background

This course equips professionals with deep learning skills to analyze complex environmental image datasets, using CNNs for classification, object detection, and segmentation, with applications in biodiversity, climate, and land-use monitoring.

Objectives

Understand deep learning concepts, particularly CNNs for image analysis.

Identify various types of environmental image data.

Implement full DL workflow: preparation, modeling, deployment.

Apply CNNs for classification, object detection, and segmentation.

Evaluate DL model performance with appropriate metrics.

Use frameworks like TensorFlow, Keras, and PyTorch.

Apply DL to real-world environmental problems.

Modules

NoModuleDetails1Introduction to DL for Image AnalysisAI, ML & DL recap, neural networks, environmental image sources.2CNN FundamentalsConvolutional layers, pooling, activation functions, fully connected layers, simple CNN architectures.3Image Preprocessing & AugmentationLoading, resizing, normalization, augmentation, handling multi-spectral images, transfer learning.4Image ClassificationCNN training, land cover/species classification, metrics, fine-tuning pre-trained networks.5Object DetectionBounding boxes, YOLO/Faster R-CNN/SSD overview, wildlife/plant detection, annotation, evaluation metrics.6Semantic SegmentationPixel-level classification, U-Net overview, wetland/deforestation mapping, mask preparation, evaluation metrics.7Advanced Topics & DeploymentRNNs for time-series images, GANs, XAI, cloud deployment, GPU/cloud computing.8Capstone Project & EthicsGuided project, data preparation, training, evaluation, ethical considerations, presenting findings.

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 focused on climate change, renewable energy, biodiversity conservation, agriculture, and food systems. Based in Nairobi, Kenya, ECAS operates across Africa and globally, partnering with organizations in the UK, Denmark, Italy, Sweden, Germany, and the USA.