Introduction The purpose of this research paper is to conduct a review of concept drift with reference to machine learning. A concept is defined as a quantity that needs to be predicted where the concept is unstable and its changes over a certain period of time. Common types of concepts are weather patterns, customer preferences, temperature and behavioral changes. The underlying data distribution that is used in explaining concepts will also be subject to some changes as a result of the unstable nature of concepts. Such changes in the underlying data distribution cause the models built on old data to be inconsistent with the new concept’s data which will lead to the updating of the model. This creates a problem known as concept drift which complicates the task of learning the new model and the new data that makes up the concept (Tsymbal 1). Machine learning in concept drifts involves the learning of a target that is shifting or data that has time changing data streams. It is also the learning of non-stationary environments that have unstable concepts to ensure that the approaches used in dealing with concept drift problems develop the final concept. Since the 1990s, various learning approaches have been developed and implemented to deal with the problem of concept drifts as these problems have become common in every concept. Such learning approaches include the AQ algorithm and the stagger concept which were developed in the 1990s to deal with the problem of concept drifts (Koronacki 23). The discussion in this research paper will therefore be focused on the aspect of concept drifts and machine learning by examining these two concepts. Machine Learning Machine learning is a branch of artificial intelligence as it involves the use of cognitive science, probability theory, behavioral science and adaptive control disciplines to determine changing behaviors of certain concepts. The major focus of machine learning is to identify and learn complex behavioral patterns that precede concept changes so as to develop intelligent decisions that are based on data. Machine learning involves the use of human cognitive processes when performing data analysis and also collaborative approaches that exist between the machine and the user (Bishop 2). There various types of machine learning that are used to determine the desired outcomes of algorithms. These include supervised learning, unsupervised learning, semi-supervised learning, transduction and reinforcement learning. Supervised learning is where machine learning converts inputs into outputs, unsupervised learning where the machine learning inputs are clustered, reinforcement learning where the machine algorithms are used to input observations and semi-supervised learning where the labeled and unlabeled examples of the target concept are used to generate an appropriate function. The other type of machine learning algorithm is referred to as transduction where the learning algorithm tries to predict new outputs that are based on the training inputs and outputs as well as the testing inputs (Bishop 3). The main theory that is used in explaining machine learning is referred to as the computational learning theory where the l