Security Breaches in computer networks have increased a lot in the last decade due to the profitable underground cybercrime economy. Many researches have been working on finding efficient techniques for detecting intrusions. Many surveys were present on different Machine Learning and Deep Learning Techniques in the last decade. Solutions proposed for dealing with network intrusions can be broadly classified as signature based and anomaly based. In this paper, a critical survey of Machine Learning (ML) and Deep learning (DL) techniques presented in the literature in the last ten years is presented. This survey would serve as a supplement to other general surveys on intrusion detection as well as a reference to recent work done in the area for researches working in ML and DL based intrusion detection systems. Some open issues are also discussed that are needed to be addressed.