Jamalreyhani / machine_learning_earth_sciences

Machine Learning for Earth Sciences

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Scope of the Repository

This is the code repository for the book titled "Machine Learning for Earth Sciences: Using Python to Solve Geological Problems" by Maurizio Petrelli (Springer)

Codes

Table of Contents:

• Chapter 1: Introduction to Machine Learning

• Chapter 2: Setting Up Your Python Environments for Machine Learning

• Chapter 3: Machine Learning Workflow

• Chapter 4: Unsupervised Machine Learning Methods]

• Chapter 5: Clustering and Dimensionality Reduction in Petrology

• Chapter 6: Clustering of Multi-Spectral Data

• Chapter 7: Supervised Machine Learning Methods

• Chapter 8: Classification of Well Log Data Facies by Machine Learning

• Chapter 9: Machine Learning Regression in Petrology

• Chapter 10: Parallel Computing and Scaling with Dask

• Chapter 11: Scale Your Models in the Cloud

• Chapter 12: Introduction to Deep Learning

License

The code in this repository is released under the MIT license. Read more about MIT license.

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