• Online, Self-Paced
Course Description

Explore essential machine learning components used to learn, train, and build neural networks and prominent clustering and classification algorithms in this 12-video course. The use of hyperparameters and perceptrons in artificial neuron networks (ANNs) is also covered. Learners begin by studying essential ANN components required to process data, and also different paradigms of learning used in ANN. Examine essential clustering techniques that can be applied on ANN, and the roles of the essential components that are used in building neural networks. Next, recall the approach of generating deep neural networks from perceptrons; learn how to classify differences between models and hyperparameters and specify the approach of tuning hyperparameters. You will discover types of classification algorithm that can be used in neural networks, and features of essential deep learning frameworks for building neural networks. Explore how to choose the right neural network framework for neural network implementations from the perspective of usage scenarios and fitment model, and define computational models that can be used to build neural network models. The concluding exercise concerns ANN training and classification.

Learning Objectives

{"identify the key subject areas covered in this course"}

Framework Connections

The materials within this course focus on the NICE Framework Task, Knowledge, and Skill statements identified within the indicated NICE Framework component(s):

Specialty Areas

  • Systems Architecture