Machine Learning applied to geosciences and mining | Training course

In this hands-on course, you’ll unlock the power of machine learning to elevate mineral resource modeling and geoscientific workflows. You’ll learn how to define geological or geometallurgical domains, apply classification and regression algorithms, and seamlessly integrate Python’s scikit-learn with Isatis.neo—all tailored for mining applications. Through a balanced mix of theory and practical exercises, you’ll build routines that boost exploration accuracy, enhance resource characterization, and support smarter, data-driven decisions throughout the mining lifecycle.

• Module I: General aspects of Machine Learning and introduction to the Python language
• Module II: Unsupervised learning
  Data transformations, clustering techniques – theory and practice, cluster quality evaluation.
• Module III: Supervised learning
  Predictive models – theory and practice, model validation, hyper-parameter tuning, model application. 

** The course can be reduced to two days by removing Module II or Module III from the program.

• Balanced learning approach: The course combines theory with practical applications, ensuring concepts are understood and applied effectively.
• Hands-on software training: Engage in computer-based exercises using Isatis.neo software, reinforcing learning through real-world data scenarios.
• Personalized feedback: Receive individualized guidance and feedback from experienced trainers during online sessions to support your learning journey.
• Comprehensive resources: Access detailed course materials, including documentation, journal files, and datasets, to reinforce learning and facilitate application post-training.

This course targets professionals seeking to gain both theoretical and practical knowledge of Machine Learning and its applications in geosciences and the mining industry.

Basic knowledge of statistics, algebra, and geostatistics is recommended. Familiarity with Python is optional.