The increasing use of big datasets by analytics applications for higher predictive power leads to higher processing overhead, and the overhead becomes more substantial when datasets are larger than memory capacity. In this paper, we focus on reducing I/O overhead for big data machine learning procedures, including both unsupervised and supervised learning. While I/O data are, in general, not reducible in well-developed applications, our approach to I/O overhead reduction is to overlap I/O's with computations so that when an application is performing an I/O, other useful computation is also processed. To this end, we develop an I/O latency-hiding (LaHiIO) strategy and an enabling easy-to-use API, a wrapper of existing asynchronous I/O operations, by hiding away features not likely needed for general data analytics applications and keeping only those necessary for computation-I/O overlapping. By doing so, we aim to increase the use of computation-I/O overlapping in big data applications by a broad range of developers who could be physicists, chemists, biologists, engineers, but not necessarily system programming experts. We apply the LaHiIO strategy to clustering and neural network procedures, the common choices for unsupervised and supervised learning, resulting in significant performance enhancement from about 10% to 150%, indicating the effectiveness of the LaHiIO strategy and its enabling user-friendly API for big data machine learning applications.