In the past decades, we have seen a drastic evolution of the communication networks, from the conventional homogenous computer networks to the advanced heterogonous networks. As the demands on the communication systems become more stringent, the problems faced by the communication engineers also become more complex, as well as the solutions to these problems. We have seen that there are more and more solutions based on artificial intelligence, machine learning, and deep learning in the systems. This is motivated by the great success of machine learning algorithms in supporting big data analytics, parameter estimation, and complex decision-making. This talk aims to give a brief overview of the current trends of deploying machine learning algorithms in solving problems and challenges in communication systems. This talk is divided into three parts. First, a general introduction of machine learning and deep learning is given. The second part focuses on using machine learning algorithms in solving various problems in future wireless communication networks. Last but not least, the third part discusses on using deep reinforcement learning and deep federated learning to support the operation and services of internet-of-thing (IoT).