This year's IBM Quantum Spring Challenge (2024) had a problem on a Variational Classifiers with bird classification based on their Quantum embeddings which have been already provided. Generally that is not the case since data handling and analysis is one of the most important fields or tasks in machine learning implementation.
Quantum Machine Learning had seen a meteoric rise since the last couple of years and so has research into the same. Their can be several branches for this kind of a guide with specialized focus on one. However, I would like to keep this as general for publicreference as possible. The possible diversions might be Graph Quantum Machine Learning, Quantum Chemistry Simulation, Quantum Neural Networks, Quantum Tensor Networks etc.
This reposiroty takes serious help and reference from the book Supervised Learning with Quantum Computers by Francesco Petruccione and Maria Schuld.