📚 Syllabus & Course Curriculum
Agriculture & Food Technology
Module-by-module breakdown of Applied Machine Learning for Agriculture and Environmental Data, from foundations to a certified capstone project.
Outline
Design a research‑to‑data pipeline that ingests satellite and sensor inputs. • Implement automated outlier detection and KNN‑based imputation for noisy environmental logs. • Create vegetation indices (NDVI, EVI) and perform atmospheric corrections with Geopandas & Rasterio. • Apply PCA and RFE to isolate minimal‑viable feature sets for high‑impact models.
Outline
Develop high‑performance Gradient Boosted models (XGBoost, LightGBM) for crop yield and soil carbon forecasts. • Design spatial validation using Group K‑Fold to mitigate autocorrelation across regions. • Execute Bayesian hyper‑parameter tuning with Optuna to maximize R² and minimize RMSE. • Train a multi‑stage regressor on multivariate climate datasets.
Outline
Interpret model decisions with SHAP to satisfy peer‑review causality standards. • Generate Partial Dependence Plots to visualize non‑linear variable effects. • Deploy an interactive Gradio interface in Google Colab for real‑time model demonstration. • Produce a publish‑ready Feature Importance Report for scientific manuscripts.
Earn government-registered certification in Applied Machine Learning for Agriculture and Environmental Data
e-Certificate and e-Marksheet issued on successful completion.
