Deep Learning and Neural Networks Course
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
Machine Learning Certificate