Graphs are increasingly employed in various artificial intelligence (AI) tasks where the input data is commonly in the form of a graph. Machine learning is a fundamental component of any AI system with deep learning residing at the core of machine learning. In this chapter, we first review the basic AI concepts; supervised learning, unsupervised learning and reinforcement learning briefly. We then describe neural networks that are commonly used for machine learning tasks in various forms. Graph neural networks which are a type of neural networks are employed in various tasks that input graphs as we review. Cooperation of knowledge graphs and graph neural networks provides many applications whether a knowledge graph is input to a graph neural network or a graph neural network is used to solve a knowledge graph task. We lastly look at quantum machine learning concepts and how these are incorporated using quantum neural networks.