To some, NLP = natural language processing, a form of text analytics arising from the field of computational linguistics. In natural language processing, computer programs and algorithms parse and decode individual sentences and paragraphs of English and other languages, much as humans learn languages. It is contrasted with predictive text mining dealing with large quantities of documents, an easier task, in which the documents are converted to quantitative data so that predictive data mining methods can be applied to them. We have courses in Natural Language Processing and Text Mining.

To others, NLP = nonlinear programming, an optimization technique in the operations research field. In linear programming, you seek to optimize an objective function (e.g. profit), subject to some constraints (e.g. limited supply of some input), with both the object function and the constraints being linear functions. In nonlinear programming, the idea is still the same, but the objective function and constraints need not be linear. For example, the more programmers you put on a project, the greater the probability that it will complete on time, but that probability does not increase linearly. Since most users encounter both linear and nonlinear programming as a matter of putting inputs into an optimization software program, the superficial difference to the user may not be great, since the structure of the problem is similar in either case. Nonetheless, an understanding of the differences in the methods helps you debug difficulties encountered.

Statistics.com has courses in both Linear Programming and Nonlinear Programming.