The practise of dermatologists, from diagnosis to individualised care, has the potential to be enhanced by machine learning (ML). Recent developments in faster computers, less expensive data storage, and access to big datasets (such as electronic medical records, picture databases, and omics) have stimulated the development of ML algorithms with human-like intelligence in dermatology. This article provides an overview of machine learning fundamentals, current uses, potential drawbacks, and ideas to keep in mind as machine learning technology develops. The five current areas of use for ML in dermatology that we have identified are: (1) disease classification using clinical images; (2) disease classification using dermatopathology images; (3) assessment of skin diseases using mobile applications and personal monitoring devices; (4) facilitation of large-scale epidemiology research; and (5) precision medicine. To assist dermatologists better assess the possible benefits and drawbacks of machine learning, this overview aims to demystify the foundations of ML and its vast variety of applications.