Course Description

What may be natural for you may not be so easy for Artificial Intelligence. Acquire the necessary deep learning knowledge and skills required to work on an AI-related project.

Develop the mathematical foundations and key concepts of deep learning which will allow you to build and train deep neural networks. Use popular frameworks such as Tensorflow and PyTorch in real-world use cases within Computer Vision and Natural Language Processing (NLP).

This course is delivered in collaboration with WeCloudData.

Course Details

Learning Outcomes

By the completion of this course, successful students will be able to:

  • Explain deep neural network algorithms, the mathematics concepts, and optimization techniques
  • Evaluate three use cases of deep learning techniques in structured data analysis, computer vision, and natural language processing (NLP)
  • Apply deep learning model training, tuning, and optimization using popular open-source tools such as Tensorflow/Keras or PyTorch for computer vision tasks
  • Apply Transformer techniques such as BERT and GPT-3 for common NLP tasks


  • Math Foundations for Deep Neural Networks
  • Introduction to Neural Networks
  • Introduction to Deep Learning Frameworks: Tensorflow or PyTorch
  • Specialized Topic: Computer Vision
  • Specialized Topic: Natural Language Processing
  • GPUs and Model Tunings
  • Build your first deep learning application

Who is this course for?

This course is designed for:

  • Data and business analytics professionals who wish to learn about the latest AI tools to stay ahead of the curve
  • IT and Engineering Professionals looking to receive hands-on training in ML and AI
  • Recent graduates and academics in Computer Science


Software Requirements

To complete the lab activities, you will require:

A Google Colab account (Gmail)


There are no mandatory prerequisites for this course. However, you are required to perform a self-assessment to ensure you meet the requirements to enroll.

Self-assessment for enrolment

  • A minimum of 1.5 years of experience with the following skillsets: python programming, machine learning algorithms and deployment in cloud, working with big data, Scikit-learn library or equivalent, linear algebra, and statistics

Recommended Pre-requisites

  • DAT 210 Cloud Computing for Data Scientists
  • DAT 220 Big Data for Data Scientists

Applies Towards the Following Program(s)

Enrol Now - Select a section to enrol in
Online Synchronous
5:00PM to 8:00PM
Sep 17, 2024 to Oct 15, 2024
Online Synchronous
8:00AM to 4:00PM
Sep 21, 2024 to Oct 19, 2024
Schedule and Location
Delivery Options
Course Fees
Flat Fee non-credit $1,495.00
Required Software
Zoom web conferencing software Laptop or computer installed with Windows or Mac OS A Google Colab account
Reading List / Textbook

No textbook required.

Section Notes

Classes are held online in real time (Mountain Time) at the specified time and dates.

This course uses:

  • D2L Learning Management System
  • Zoom web conferencing software

This course is delivered in an online blended format, meaning that some classes are taught in a live virtual session using Zoom, and some work have to be completed in a designated online e-learning platform on your own time.

For the best experience, you will require access to a computer with Internet connection, a headset with speakers and microphone, webcam, and a monitor large enough to display multiple applications (or the use of two monitors). Your computer and internet connection should meet certain requirements. See the recommended requirements.

For more information, please visit our Online Learning Resources.

Students unfamiliar with online learning are encouraged to take our free Digital Skills for Learning Online course.

Unless otherwise stated, notice of withdrawal or transfer from a course must be received at least seven calendar days prior to the start date of the course.

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