You state your background is chemistry, and have aspiration for Python as a programming language aiming for ML without background in computer science.
Do you have some experience in programming at all? In case you don't I suggest to visit the inflammation classes by software-carpentry for the *nix shell, version control by git, and beginners lessons for Python. Perhaps their classes pass nearby your area, too (schedule). After this, you should be able to create the first small and simple programs on you own, and identify what can be useful in addition to this "starter set", both in terms of concepts (e.g. regular expressions), as well as implementations in your language of choice (e.g., matplotlib and plotting). But don't get lost there.
Instead turn early your attention to e.g. The Journal of Chemical Education to apply the acquired skill. There is for a compact 101 by Lafuente, and plenty examples of application which can serve as prompt for your training. Enter "machine learning" into the search mask of the journal (which suggests automatic completion to "Machine Learning in Chemistry", "Machine Learning for Drug Discovery", and "Machine Learning in Materials Science"). Don't automatically discharge them from your screen if - as St James' classification of spectra by ML - a different language of implementation (here: MATLAB) was used, but imagine how such a capstone project could be implemented in the language(s)* you are familiar with.
A pinch of salt: you are going to meet people overly excited about machine learning and artificial intelligence as if the underlying techniques finally were applied on large scale. Some of them are statistics / chemometrics already known for long (e.g. principal component analysis in IR spectroscopy) which now indeed enjoy a renaissance for every additional field of application gained. However, in part, the field's attraction equally is due because computation became so affordable (e.g., renting infrastructure in the cloud on demand), and often tasks can be split into multiple smaller ones run in parallel instead of a pure sequential approach (multicore CPUs). Or that computer programs can assist you in generating new computer code (literally like copilot) faster, where the chatbots like ChatGPT are one level on top. Similar to a chariot, you typically don't pull it over long distances, but you should know enough about it and the horses for a safe and reliable drive.
* Depending on the task ahead, some programming languages are more suitable than others. Multiple criteria should apply for a selection.
 Lafuente, D.; Cohen, B.; Fiorini, G.; García, A. A.; Bringas, M.; Morzan, E.; Onna, D. A Gentle Introduction to Machine Learning for Chemists: An Undergraduate Workshop Using Python Notebooks for Visualization, Data Processing, Analysis, and Modeling. J. Chem. Educ. 2021, 98, 2892-2898, doi 10.1021/acs.jchemed.1c00142 (and author's copy).
 St James, A. G.; Hand, L.; Mills, T.; Song, L.; Brunt, A. S. J.; Bergstrom Mann, P. E.; Worrall, A. F.; Stewart, M. I.; Vallance, C. Exploring Machine Learning in Chemistry through the Classification of Spectra: An Undergraduate Project. J. Chem. Educ. 2023, 100, 1343-1350, doi 10.1021/acs.jchemed.2c00682 (open access / author's choice).
The answer once was given on chemistry.stackexchange on April 20th, 2023. User Martin (a moderator) suggested the move to this site.