Udemy - Gold Prospectivity Mapping - Random Forest vs. XGBoost

Gold Prospectivity Mapping: Random Forest vs. XGBoost
https://WebToolTip.com
Published 8/2026

Created by Dr. Yusuf Abdullahi Musa

MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch

Level: Beginner | Genre: eLearning | Language: English | Duration: 17 Lectures ( 2h 55m ) | Size: 2.8 GB
Master machine learning for mineral exploration using aeromagnetic and radiometric data to delineate gold targets.
What you'll learn

⚡ Build end-to-end Python machine learning workflows for gold prospectivity mapping.

⚡ Process and integrate high-resolution aeromagnetic and airborne radiometric datasets.

⚡ How to extract, rank, and compare feature importance to identify which geophysical layers contribute most to gold deposit targeting.

⚡ How to statistically evaluate and compare RF vs. XGBoost performance using cross-validation, confusion matrices and F1-scores.

⚡ Implement, tune, and compare two top-performing ensemble algorithms: Random Forest and XGBoost.

⚡ Convert raw model probabilities into actionable, 4-class gold prospectivity maps (Very Low, Low, Moderate, High).

⚡ How to export publication-ready 2D prospectivity maps and vector/raster target files directly for GIS integration and drill-targeting decisions.
Requirements

❗ Basic understanding of geology, geophysics, or spatial data concepts.

❗ Introductory knowledge of Python programming.

❗ A computer running Windows, or macOS with Python 3.x installed (Jupyter Notebook or VS Code recommended).