Requirements: Basic knowledge of math, statistics and databases.
Through the understanding of basic concepts regarding the processing level required by common algorithms in advanced analytics, and the selection of the appropriate one according to the problem presented, course attendees will be able to ensure that the generated models are operational. In this course, topics relevant to statistical and advanced analytics processes are covered, as well as the conceptualization of neural networks and their applications, so that complex classificatory models, nonlinear trends, predictive and pattern recognition systems can be created. The topic of training level and the concepts of under-fitting and over-fitting, which allow understanding the balance between bias and variance to achieve a useful model, is explained, as well as the operation of neural networks using concepts of vector space transformation, thus clarifying that linear models are still applicable to spaces that show non-linear behaviors, simply by transforming the space.