Artificial Intelligence (AI), Machine Learning (ML), and Python programming are transforming the way geoscientists analyze, interpret, and model Earth science data. From seismic interpretation and lithology classification to reservoir characterization, environmental monitoring, weather prediction, and mineral exploration, modern data-driven techniques have become essential tools for both research and industry. This course is designed to bridge the gap between geoscience and artificial intelligence by providing a practical, hands-on approach to Python programming and machine learning using real-world geoscience datasets.
The course begins with Python fundamentals and gradually progresses to data analysis, visualization, and machine learning. You will learn how to work with scientific libraries such as NumPy for numerical computing, Pandas for data manipulation, Matplotlib and Seaborn for data visualization, SciPy for scientific computing and statistical analysis, and Scikit-learn for building machine learning models. You will also receive an introduction to deep learning with TensorFlow and Keras to solve more advanced geoscience problems.
