Not sure that machine learning is the best approach to predict antibiotic resistance. Since most resistance genes are well-known, it is simply a matter of finding a reliable homolog in existing databases.
But to answer your question: modern machine learning (ML) is all about proper data representation. So you need to find a way to represent protein sequences such that those that convey antibiotic resistance are different from those that don't. That can't be simple sequence, because protein lengths differ and ML methods don't handle well inputs of different length.
So how do you represent your protein sequences with vectors of the same length, regardless of protein size? One way would be to do sequence embedding like here, which gives a constant-size vector (1024) for each sequence. There are many other sequence embedding approaches, it should be easy to find them on GitHub. Or you can pick 200 representative antibiotic resistance proteins (or whatever number is needed) and BLAST-compare your proteins of interest to this mini database one at a time. That will give you 200 BLAST E-values which can be used as 200 features for training and classification. There are many other approaches I am sure, and it is up to you to come up with a creative and meaningful representation, even though I think ML methods are an overkill in this instance because sequence comparison methods work fine.
If you run out of ideas I suggest you search the literature for other papers that used ML for antibiotic resistance prediction, and try to emulate how they created their training and validation datasets.
