Conclusions

Conclusions#

Michael J. Pyrcz, Professor, The University of Texas at Austin

Chapter of e-book “Applied Machine Learning in Python: a Hands-on Guide with Code”.

Cite this e-Book as:

Pyrcz, M.J., 2024, Applied Machine Learning in Python: A Hands-on Guide with Code [e-book]. Zenodo. doi:10.5281/zenodo.15169138

The workflows in this book and more are available here:

Cite the MachineLearningDemos GitHub Repository as:

Pyrcz, M.J., 2024, MachineLearningDemos: Python Machine Learning Demonstration Workflows Repository (0.0.3) [Software]. Zenodo. DOI: 10.5281/zenodo.13835312. GitHub repository: GeostatsGuy/MachineLearningDemos

By Michael J. Pyrcz
© Copyright 2024.

Parting Thoughts#

I sincerely hope that this e-book has been helpful. To maximize your learning experience please remember to review the YouTube lectures linked to each workflow to better understand the essential theory and for guidance on best practice and possible applications.

This is my second effort to convert one of my GitHub repositories of well-documented Python workflows into an online, accessible e-book. For those interested in spatial data analytics and geostatistics, I welcome you to visit my other e-book, Applied Geostatistics in Python: a Hands-on Guide with GeostatsPy[]. The great response to this e-book from students and working professionals all over the world motivated me to compile, build and release this e-book. The original repository for the workflows in this book are found at MachineLearningDemos: Python Machine Learning Demonstration Workflows Repository [Pyr24d], but rest assured every chapter in this book is a Jupyter Notebook or Jupyter Lab .ipynb file that may be downloaded and run locally.

All I ask is that you retain my authorship and cite the source when my work is used. Share the links not the work. Do not modify, build new educational products or translations from it, nor repost. My motivation is to attract more people to all of the resources that I share online! Please consider citing and linking these resources in your work.

Welcome!

The goal of this e-book is to teach the application of machine learning in Python, for those new to machine learning I provide theory and links to my course content, and for those experienced practitioners I provide example workflows and plots that you can implement in their work and studies.

I am only partially successful in this goal. More can be done! I welcome your feedback and errata, return often and watch this project continue to grow. Yet, I am pleased with this opportunity to expand the reach of my educational content to support stuudents and working professions.

Stay well,

Michael

About the Author#

Michael Pyrcz is a professor in the Cockrell School of Engineering, and the Jackson School of Geosciences, at The University of Texas at Austin, where he researches and teaches subsurface, spatial data analytics, geostatistics, and machine learning. Michael is also,

the principal investigator of the Energy Analytics freshmen research initiative and a core faculty in the Machine Learn Laboratory in the College of Natural Sciences, The University of Texas at Austin
an associate editor for Computers and Geosciences, and a board member for Mathematical Geosciences, the International Association for Mathematical Geosciences.

Michael has written over 70 peer-reviewed publications, a Python package for spatial data analytics, co-authored a textbook on spatial data analytics, Geostatistical Reservoir Modeling and author of two recently released e-books, Applied Geostatistics in Python: a Hands-on Guide with GeostatsPy and Applied Machine Learning in Python: a Hands-on Guide with Code.

All of Michael?s university lectures are available on his YouTube Channel with links to 100s of Python interactive dashboards and well-documented workflows in over 40 repositories on his GitHub account, to support any interested students and working professionals with evergreen content. To find out more about Michael?s work and shared educational resources visit his Website.

Want to Work Together?#

I hope this content is helpful to those that want to learn more about subsurface, spatial modeling, data analytics and geostatistics. Students and working professionals are welcome to participate. I’m happy to collaborate with your organization.

Want to invite me to visit your company for training, mentoring, project review, workflow design and / or consulting? I’d be happy to drop by and work with you! I am a cofounder of a data science education company.
Interested in partnering, supporting my graduate student research or my Subsurface Data Analytics and Machine Learning consortium (co-PIs including Profs. Foster, Torres-Verdin and van Oort)? My research combines data analytics, stochastic modeling and machine learning theory with practice to develop novel methods and workflows to add value. We are solving challenging subsurface problems!
I can be reached at mpyrcz@austin.utexas.edu.

I’m always happy to discuss,

Michael

Michael Pyrcz, Ph.D., P.Eng. Professor, Cockrell School of Engineering and The Jackson School of Geosciences, The University of Texas at Austin