📚 Syllabus & Course Curriculum
Agriculture & Food Technology
Module-by-module breakdown of Space to Soil: Practical AI Applications in Satellite Imagery, from foundations to a certified capstone project.
Outline
Explore satellite data types, multispectral & hyperspectral imagery • Apply AI preprocessing techniques (filtering, normalization, feature extraction) • Prepare satellite imagery for machine‑learning pipelines • Hands‑on: Preprocess and analyze imagery using Python (OpenCV, scikit‑image)
Outline
Design regression, neural‑network, and LSTM models for weather prediction • Utilize satellite data to monitor long‑term climate trends • Evaluate model accuracy and robustness for real‑time forecasts • Hands‑on: Build and test a weather‑forecasting model with real datasets
Outline
Implement AI for crop‑health monitoring and precision agriculture • Apply AI to analyze urban growth, land‑cover, and environmental management • Create decision‑support systems that turn satellite data into policy insights • Hands‑on: Develop a decision‑support tool for agriculture and urban planning
Earn government-registered certification in Space to Soil: Practical AI Applications in Satellite Imagery
e-Certificate and e-Marksheet issued on successful completion.
