网上的机器学习教程常见两种极端：要么满屏公式、讲得抽象难啃；要么只教你调框架、原理一笔带过。结果学完能跑代码，却抓不住算法的核心。

我在 GitHub 上挖到一本开源免费电子书《Applied Machine Learning in Python》，路线清晰、体系完整，把数学推导和 Python 实现紧密绑在一起，真正做到“懂原理，也会写”。

从线性回归一路到神经网络，每个算法不仅有完整推导，还给出纯 Python 的手工实现；再配上交互式可视化，把原本晦涩的数学概念变成看得见、摸得着的训练过程。

GitHub：http://github.com/GeostatsGuy/MachineLearningDemos…

在线阅读：http://geostatsguy.github.io/MachineLearningDemos_Book…

你将学到：

• 覆盖 30+ 算法：回归、分类、聚类、降维一网打尽
• 每个算法：详细推导 + 纯 Python 手写实现，不止会用，更能理解
• 深度学习：ANN、CNN、自编码器、GAN 等主流架构系统讲透
• 交互式可视化：训练过程、参数变化一目了然
• 配套资源齐全：YouTube 讲座 + 完整代码仓库

全书免费在线读、代码全部开源，适合想系统入门并真正吃透机器学习原理的开发者。

GeostatsGuy/MachineLearningDemos

Source: https://github.com/GeostatsGuy/MachineLearningDemos

MachineLearningDemos: Python Machine Learning Demonstration Workflows Repository (0.0.1)

Approximately 20 Well-Documented Machine Learning Workflows!

It is challenging to learn machine learning. For me, great examples for common workflows are crtical. So I built out over 20 well-documented demonstration workflows that apply machine learning to accomplish common data science tasks to support my students in my Data Analytics and Geostatistics, Spatial Data Analytics and Machine Learning courses and anyone else learning data analytics and machine learning.

Michael Pyrcz, Professor, The University of Texas at Austin, Data Analytics, Geostatistics and Machine Learning

Twitter | GitHub | Website | GoogleScholar | Book | YouTube | LinkedIn

Cite As:

Pyrcz, M.J., 2024, MachineLearningDemos: Python Machine Learning Demonstration Workflows Repository (0.0.1) (0.0.1). Zenodo. TBD

To Be Updated

Recent Updates

Here’s some highlights from recent updates:

What’s New with Version 0.0.1

I spent quite a bit of time checking, updating and improving all of the workflows for this first release.

• improved documentation with concepts and theory from my courses to motivate the workflows
• improved code comments
• improved data and model visualization

I’m quite happy with the current state. I feel that this set of well-documented workflow for machine learning in Python now lives up to its goal - to launch anyone into building machine learning workflows! I’m stoked to help out, Michael

Setup

A minimum environment includes:

• Python 3.7.10 - due to the depdendency of GeostatsPy on the Numba package for code acceleration
• GeostatsPy - I am continuously testing these workflow with the most current version, GeostatsPy(Pyrcz et al., 2021)
• MatPlotLib - plotting
• NumPy - gridded data and array math
• Pandas - tabulated data
• SciPy - statistics module
• scikit-learn - most of the machine learnng models

The required datasets are available in the GeoDataSets repository and linked in the workflows.

Repository Summary

More than 20 well-documented demonstration workflow for common machine learning workflows in Python.

• utilizing synthetic data from my GeoDataSets repository
• small and often 2D examples for fast run times and ease of interpretation
• often used and cited in my courses for repeatable educational content

Common geostatistical workflows that are included:

• multivariate analysis
• feature selection
• feature transformations
• cluster analysis
• principal component analysis
• linear regression
• ridge regression
• LASSO regression
• Bayesian linear regression
• naive Bayes classification
• polynomial regression
• k-nearest neighbours
• decision trees
• bagging trees and random forest
• gradiate boosting
• support vector machines

Installing GeostatsPy

Firstly, if you haven’t installed GeostatsPy, here’s the GitHub repository GeostatsPy GitHub. GeostatsPy is available on the Python Package Index (PyPI) GeostatsPy PyPI.

To install GeostatsPy, use pip

pip install geostatspy

GeostatsPy Package Dependencies

The functions rely on the following packages:

1. numpy - for ndarrays
2. pandas - for DataFrames
3. numpy.linalg - for linear algebra
4. numba - for numerical speed up
5. scipy - for fast nearest neighbor search
6. matplotlib.pyplot - for plotting
7. tqdm - for progress bar
8. statsmodels - for weighted (debiased) statistics

These packages should be available with any modern Python distribution (e.g. https://www.anaconda.com/download/).

If you get a package import error, you may have to first install some of these packages. This can usually be accomplished by opening up a command window on Windows and then typing ‘python -m pip install [package-name]’. More assistance is available with the respective package docs.

GeostatsPyDemos Repository Author:

Michael Pyrcz, Professor, The University of Texas at Austin

Novel Data Analytics, Geostatistics and Machine Learning Subsurface Solutions

With over 17 years of experience in subsurface consulting, research and development, Michael has returned to academia driven by his passion for teaching and enthusiasm for enhancing engineers’ and geoscientists’ impact in subsurface resource development.

For more about Michael check out these links:

Twitter | GitHub | Website | GoogleScholar | Book | YouTube | LinkedIn

Want to Work Together?

I hope this content is helpful to those that want to learn more about subsurface modeling, data analytics and machine learning. Students and working professionals are welcome to participate.

• 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!

• 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 [email protected].

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

More Resources Available at: Twitter | GitHub | Website | GoogleScholar | Book | YouTube | LinkedIn

More functionality will be added soon.