Quantum machine learning can help you to enhance classical machine learning algorithms by outsourcing difficult calculations to a quantum computer. You can also optimise quantum algorithms using classical machine learning architectures.
IBM researchers have developed a series of quantum algorithms that show how entanglement can improve AI classification accuracy. It is demonstrating a Quantum Classifier. It is available online on IBM Bluemix in the following link. Also IBM has demonstrated Hybrid quantum - classical neural networks with PyTorch and Qiskit online in the following documentation.
When you are working on a Quantum Classifier for a dataset, we need to first encode the data into the amplitudes of a quantum state. In fact, one needs to first normalise the data such that it can be represented as a vector on a high-dimensional Bloch sphere. Quantum routines to encode data in amplitudes, so called arbitrary state preparation routines, are known to do this with a runtime that is linear in the data size, and this is arguably the best our algorithm can do in terms of runtime, since the data is the input to the problem.