A.I. & Deep Learning Accelerated Certificate Course (5 Days)

This course provides a highly applicable, hands-on learning experience involving Deep Learning (Convolution & Recurrent Networks, etc.) and other advanced forms of Artificial Intelligence.  Participants will learn to leverage such innovations for:

  • Predictive Analytics
  • Text Analytics & NLP
  • Pattern Recognition
  • Feature Engineering
  • Time Series Analysis
  • Computer Vision

What is Deep Learning?

Inspired by the way brains process information, the most important advancement in Artificial Intelligence today is Deep Learning (aka Deep Neural Nets). Imagine a progression of nodes (neurons), each containing aspects of knowledge and with the ability to communicate back and forth – only algorithmically. Deep Learning’s success lies primarily in the ability to learn hierarchical solutions to Pattern Recognition problems, which are more generic and transferable. This game-changer is enabling first adopters to more accurately predict the future, automate processes, harness untapped knowledge, and drive incredible results.


This is an intensive, one-week program designed to help working professionals and STEM grads become proficient in the most disruptive technologies ever developed in Data Science.  You will learn to apply Deep Neural Networks towards a variety of problems and how to ensure that what you build is fully leveraged by your company.

Who Should Attend?

Current and aspiring Data Scientists looking to increase their value, learn bleeding-edge skills, and solve real-world problems with A.I. & Deep Learning.  Though not required, it is recommended that participants hold at least a STEM Bachelor’s degree.  Lab sessions will take place in Python, so knowledge of that or other programming languages (e.g. R, Java, C#) is ideal.

Learn To...

  • Architect A.I. & Deep Learning solutions.
  • Build A.I. & Deep Learning applications.
  • Interface with business users who may struggle to understand the technology.
  • Recognize the limitations of AI & Deep Learning and avoid mistakes during production.
  • Utilize best practices to ensure proper implementation and adoption.
  • Demystify and visualize results for easy consumption by non-technical stakeholders.
  • Lead A.I. & Deep Learning projects.


  • Day 1:  Intro to Artificial Intelligence/Machine Learning & Learning Fundamentals
  • Day 2:  Deep Learning (Deep Neural Networks) | Multi-Layered Perceptron
  • Day 3:  Convolution Neural Networks
  • Day 4:  Recurrent Neural Networks
  • Day 5:  Scaling Deep Neural Networks
  • Final Project & Evaluation (Out of Class)


Michael N Thompson

Richard Scheufler

Suva Sharma

Hongcheng Li

Matthew Visnaw

Zhenchun Xia

Dean Allemang

Shravan Adapa