Official Description

6.0 Continuing Education Units (CEUs)

This course aims to introduce participants to essential machine learning methods and techniques through an end-to-end machine learning project. The emphasis is placed on practical experience with machine learning using Python programming language, scikit-learn and TensorFlow, as well as on understanding classification and training models.  The course will provide an introduction to artificial Neural Networks, deep learning, convolutional and recurrent neural nets and reinforcement learning.


Supplementary Information

35 hours in class plus at least 25 hours of assignments.

Topics Covered

  • Introduction to Deep Learning and Keras (TensorFlow V2)
  • Hyperparameters and Performance
  • Convolutional Neural Networks
  • Recurrent Neural Networks and Time Series  
  • Autoencoders and GANs
  • Natural Language Processing
  • Reinforcement Learning
  • Deploying Models in Production
  • Machine Learning Project Structure: Stages, Roles, and Tools

Learning Outcomes

The course is designed to enable you to:

  • Explain the fundamental concepts of Deep Learning
  • Apply effectively different tools that enable Deep Learning in own machine  
  • Hands-on experience in Keras and Python coding
  • Understand Machine Learning Performance measures in different applications
  • Understand Hyper-parameters and own adjustment to reach better performance   
  • Differentiate between different architectures, such as Convolution, LSTM, GAN, Autoencoder
  • Attention-based models, Transformer
  • Hands-on experience in Transfer Learning, Fine Tuning and, Learning from scratch
  • Build and deploy Deep Learning Models in Production with Web Interface
  • Describe advanced concepts and trends in Machine Learning
  • Introduction to Machine Learning in the cloud


This course is supported by DataCamp, the most intuitive learning platform for data science. Learn R, Python and SQL the way you learn best through a combination of short expert videos and hands-on-the-keyboard exercises. Take over 100+ courses by expert instructors on topics such as importing data, data visualization or machine learning and learn faster through immediate and personalized feedback on every exercise.

Prerequisite(s) and Corequisite(s)

Applies Towards the Following Programs

Section(s) offered
Section Title
Practical Machine Learning
Language of Delivery
Online Course
6:00PM to 9:00PM
Sep 14, 2021 to Nov 23, 2021
Online Course
6:00PM to 8:00PM
Nov 30, 2021
Schedule and Location
Contact Hours
Delivery Format(s)
Course Fee(s)
Fee non-credit $1,478.80 Click here to get more information
Drop Request Deadline
May 07, 2021 to Sep 21, 2021
Transfer Request Deadline
May 07, 2021 to Sep 21, 2021
Withdrawal Request Deadline
Sep 21, 2021 to Sep 28, 2021
Section Notes

A minimum number of registrations is required for this course section to be offered. The School reserves the right to cancel any course section when a minimum number of registrations has not been reached 7 days prior to the start date. In the event of a cancellation, the course fee will be refunded in full.

Course Format

This online course is a combination of weekly live online instructor-led sessions from 6:00 - 7:30 PM and self-directed learning activities and assignments.

Course Fee Description

  • Tuition Fee: $1459.00
  • SCS Career Development Success package (SCSD) fee:  $19.80

Course Drop/Withdrawal Policy

  • Any time prior to the 1st class: Course Drop Period with Full Refund.
  • After the 1st and before the 2nd class: Course Withdrawal Period with Full Refund.
  • After the 2nd class before the 3rd class: Course Withdrawal with No Refund.


Required fields are indicated by .