Titlebook: Machine Learning for Earth Sciences; Using Python to Solv Maurizio Petrelli Textbook 2023 The Editor(s) (if applicable) and The Author(s),

悅耳

帳單

Textbook 2023ata, well-log data facies classification, and machine learning regression in petrology. Also, the book introduces the basics of parallel computing and how to scale ML models in the cloud. The book is devoted to Earth Scientists, at any level, from students to academics and professionals..

aspersion

外露

Machine Learning for Earth Sciences978-3-031-35114-3Series ISSN 2510-1307 Series E-ISSN 2510-1315

祖?zhèn)髫敭a

背書

暗語

https://doi.org/10.1007/978-3-031-35114-3Deep Learning; Application of Machine Learning; Python Tools and Techniques; Tree-Based Models; ML Tools

sundowning

Clustering of Multi-Spectral Dataions. It describes how to import, pre-process, describe, and analyze multi-spectral data that can be downloaded from access points such as USGS Earth Explorer, the Copernicus Open Access Hub, and Theia.

FRET

Introduction to Machine LearningThis chapter introduces the basics of machine learning to geologists. Toward this end, it first provides fundamental definitions and introduces common terminology. It then discusses the learning process and defines the different types of learning paradigms (i.e., supervised, unsupervised, and semisupervised).

spondylosis

Setting Up Your Python Environments for Machine LearningThis chapter details how to prepare a Python environment to start working with Machine Learning in Earth Sciences. First, it shows how to set up a local Python environment, and then how to create a remote Linux instance. Finally, it explains how to start working with cloud-based machine learning environments.