Ai+ Training

In this course, we will explore one of the core concepts in unsupervised learning, autoencoders, and introduce semi-supervised learning. Autoencoders are a shallow neural network that learn representations of the original input data and output the newly learned representations. In other words, autoencoders perform automatic feature engineering, limiting the need for manual feature engineering and accelerating the build of machine learning systems. Autoencoders are also a means to leverage information in a partially labeled dataset. With autoencoders, we are able to turn unsupervised machine learning problems into semi- supervised ones. In this course, we build unsupervised, supervised, and semi-supervised (using autoencoders) credit card fraud detection systems. First, we will employ a pure unsupervised approach, without the use of any labels. Next, we will employ a supervised approach on a partially labeled dataset. Finally, we will apply autoencoders to the partially labeled dataset (an unsupervised learning technique) and combine this with a supervised approach, building a semi-supervised solution. To conclude, we will compare and contrast the results of all three approaches.” We will also introduce deep unsupervised learning and explore one of the hottest areas of unsupervised learning today: generative modeling using GANs (short for generative adversarial networks). We will conclude with a demonstration of text and image-based GANs in action.