I know that is a very general question but I would like to start a ML project in Python to predict some chemical properties with a large set of experimental data. The compounds I would like to study are transition metal complexes so I have difficult generating SMILES, for example. I would like to know if there are any books (or other sources) on which to learn well what descriptors are, which ones to use, how to apply them, which databases to use, how to choose a regression model or how to faithfully convert coordination bonds of complexes.
Admittedly, there are tons of materials on the chemistry + machine learning topics. Let me give one:
An introductory text I find useful is Machine Learning in Chemistry from Janet and Kulik in ACS in Focus series. If you check the papers of the Kulik group at MIT you will find much insightful information on which descriptors of coordination compounds work and ties up nicely with the above-mentioned tutorial by the same authors.