Machine Learning (henceforth ML) refers to the set of algorithms and computational methods which enable computers to learn patterns from training data without being explicitly programmed to do so.1 ML uses training data to learn patterns by estimating a mathematical model and making predictions in out of sample based on new or unseen input data. ML has the tremendous capacity to discover complex, flexible and crucially generalisable structure in training data. Conceptually speaking, ML can be thought of as a set of complex function approximation techniques which help us learn the unknown and potentially highly nonlinear mapping between the data and prediction outcomes, outperforming traditional techniques.2 In this exposition, my aim is to provide a basic and non-technical overview of machine learning and its applications for economists including development economists. For more technical and complete treatments you may consult Alpaydin (2020) and James, et al. (2013). You may also wish to refer to my four lecture series on machine learning on YouTube https:// www.youtube.com/watch?v=E9dLEAZW3L4 and my GitHub page for detailed and more technical lecture slides https://github.com/sonanmemon/Introduction-to-ML-For-Economists.