Deep Learning and Neural Networks Course

COURSE ID: CIS537

Course Overview

In this course, you will investigate the fundamental components of machine learning that are used to build a neural network. You will then construct a neural network and train it on a simple data set to make predictions on new data. We then look at how a neural network can be adapted for image data by exploring convolutional networks. You will have the opportunity to explore a simple implementation of a convolutional neural network written in PyTorch, a deep learning platform. Finally, you will yet again adapt neural networks, this time for sequential data. Using a deep averaging network, you will implement a neural sequence model that analyzes product reviews to determine consumer sentiment.

These courses are required to be completed prior to starting this course:*Problem-Solving with Machine Learning**Estimating Probability Distributions**Learning with Linear Classifiers**Decision Trees and Model Selection**Debugging and Improving Machine Learning Models**Learning with Kernel Machines*

Who should enroll in this course?

- Programmers
- Developers
- Data analysts
- Statisticians
- Data scientists
- Software engineers