Background and Fundamentals: Review of Machine Learning Algorithms, Introduction to Quantum Computing including state vectors, Hilbert space, quantum states, quantum entanglement and superposition, quantum gates, and quantum circuits, Representing, and Handling Data on a Quantum Computer. Quantum machine learning (QML): Quantum machine learning (QML) basics; including representing classical data on quantum systems, quantum data encoding and embedding, quantum data representation and quantum feature maps, Quantum Algorithms for Machine Learning; Quantum Classifiers, Quantum Kernel Methods, and Quantum Clustering, Characterising Performance in ML vs. QML Quantum Neural Network (QNN): Quantum Variational Circuits, Quantum Neural Networks (QNNs), Quantum Convolutional Neural Networks (QCNNs), Quantum Federated Learning (QFL), Quantum Reinforcement Learning (QFL), Quantum Multimodal Learning. Applications and future research directions: Applications of QML in natural language processing, computer vision, healthcare, drug design, transportation, cyber security and remote sensing, challenges and future research directions in QML.