Machine Learning workflows. Multidisciplinary applications using Python

Streamlining Machine Learning workflows with pipelines

Data Science and Machine Learning algorithms find their best application when they are linked into efficient workflows. Indeed, these algorithms work optimally when they are combined into the same systemic approach. The good news is that Python programming language includes useful libraries and graphical interfaces that allow chaining an arbitrary number of transformation and learning steps. This allows speeding up the entire workflow.

A dedicated project

In order to open an interactive discussion with experts as well as with fans of Data Science, I created a new ResearchGate project focused on Machine Learning workflows.

In this project, I am progressively collecting two categories of documents:

1) papers describing applications of Machine Learning workflows to real data belonging to multidisciplinary scientific domains;

2) Python codes.

In the first case, I have already collected peer reviews papers published by me, pre-print articles, works in progress and extended abstracts presented at international conferences. In the second case, I have collected Python codes including concatenated libraries addressed to advanced analytics and Machine Learning.

Links to free download

The following are the direct links to some among many papers and related Python codes:

a)    Comparison of different Machine Learning algorithms for lithofacies classification from well logs

b)    Machine Learning for rock classification based on mineralogical and chemical composition. A tutorial

c)     Integrated Geophysics and Machine Learning for Risk Mitigation in Exploration Geosciences

d)    Streamlining workflows with pipelines

e)    PCA TUTORIAL SESSION

f)     Regression Analysis Tutorial

g)    Complessità, Machine Learning e Neuroscienze

h)    Complessità, Machine Learning e Neuroscienze-Parte 2

i)      …

Credits

I have done this work in my free time, using personal resources, mostly in the peace of my country house (picture above). Furthermore, I have profited of the impressive availability on the web of open source codes, open source databases, and Python libraries.

Finally, I have to credit the two principal scientific books that inspired and guided my work in this field.

The first one is the book written by Raschka, S. and Mirjalili, V. (2017): “Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow”, 2nd Edition, Packt Publications. This is a useful, well-written book where I found many theoretical and practical explanations, and many examples about the main algorithms and Python codes commonly used in Data Science and Machine Learning applications.

The second fundamental reference of my work is the book written by Russell, S. and Norvig, P., (2016): “Artificial Intelligence: A Modern approach, Global Edition”, published by Pearson Education, Inc. I suggest reading that book if you desire to acquire a deep knowledge of the background theory of Machine Learning and Artificial Intelligence in general.