Students who will complete this course can achieve the following course learning outcomes (CLOs):
CLO1: Understand the basic principles of artificial intelligence and machine learning, including the difference between supervised and unsupervised learning, and the concept of deep learning.
CLO2: Analyse and interpret data using statistical methods and machine learning algorithms.
CLO3: Build and train basic machine learning models, such as linear regression and decision trees, using Python and popular machine learning libraries such as scikit-learn.
CLO4: Evaluate the performance of machine learning models and choose appropriate metrics for specific problems.
CLO5: Understand the ethical and social implications of AI and ML, including issues related to bias, fairness, and transparency.
CLO6: Identify potential applications of AI and ML in various industries and domains, such as healthcare, finance, and e-commerce.
Artificial Intelligence (AI) Machine Learning (ML) Python AI and ML techniques and applications.
Artificial Intelligence (AI) and Machine Learning (ML) are two of the most exciting and rapidly growing fields in computer science today. This module provides an introduction to these topics, covering key concepts, techniques, and applications of AI and ML. The module begins with an overview of AI and its various subfields, including machine learning, natural language processing, robotics, and more. Additionally, it explores how to evaluate machine learning models, and how to use them to solve real-world problems using python.
The importance of taking NOC courses:
This course is designed to train our students to find jobs in the Canadian labour market using the National Occupational Classification (NOC) and its codes. The Government of Canada developed the NOC to categorize occupational information in the Canadian labour market through a standardized framework and a system that can be easily managed, understood, and unified. Canadian Immigration (i.e., IRCC) uses the NOC to classify jobs and occupations according to specific skill levels. Canada's jobs are ranked according to a person's work and the roles and responsibilities of the job.
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Prof. Saranya is the Instructor
at NSRIC Inc. She is also the Associate Professor in Computer Science
Engineering (ENG) at Anna University Colleges. In addition to her current
affiliation with NSRIC, she holds freelance faculty positions in some other
universities. Prof. Saranya is the founder of Algorithmics Computing Centre in
India, She has mentored projects under the Smart India Hackathon for various ministries.
She has published journals in reputed articles such as Springer, and many
journals indexed by Elsevier. She has also published books in Amazon like
Octave by examples, Points to Ponder for Python, and so on. She has also
published book chapters about the updating of recent trends by IGI global
publishing. In 12 years professional career, Dr, Saranya has Served on academic
or administrative committees to deal with institutional policies along with preparing
and delivering lectures to undergraduate and graduate students on topics such
as programming languages, data structures, networking, software design, AI,
Blockchain technologies and so on.
Prof. Saranya earned a B.Tech
in Information Technology from Anna University, India in 2009, and an M.E in Software
Engineering from Anna University in 2011. Dr. Saranya was awarded PhD in Information
and Communication Engineering in 2019 and an MBA(Information Systems) degree in
2014 by Bharathiyar University, India.
| Section Name | Lecture Name | Lecture Date | Lecture Time (Toronto, Canada - EST Time) |
Lecture Time (Local Time) |
|---|---|---|---|---|
| Section I (Previous) | Session 1 | Mon-08-Jan-24 | 08:00 PM to 09:00 PM | 06:00 AM to 07:00 AM |
| Session 2 | Tue-09-Jan-24 | 08:00 PM to 09:00 PM | 06:00 AM to 07:00 AM | |
| Session 3 | Mon-15-Jan-24 | 08:00 PM to 09:00 PM | 06:00 AM to 07:00 AM | |
| Session 4 | Tue-16-Jan-24 | 08:00 PM to 09:00 PM | 06:00 AM to 07:00 AM | |
| Session 5 | Mon-22-Jan-24 | 08:00 PM to 09:00 PM | 06:00 AM to 07:00 AM | |
| Session 6 | Tue-23-Jan-24 | 08:00 PM to 09:00 PM | 06:00 AM to 07:00 AM | |
| Session 7 | Mon-29-Jan-24 | 08:00 PM to 09:00 PM | 06:00 AM to 07:00 AM | |
| Session 8 | Tue-30-Jan-24 | 08:00 PM to 09:00 PM | 06:00 AM to 07:00 AM | |
| Session 9 | Mon-05-Feb-24 | 08:00 PM to 09:00 PM | 06:00 AM to 07:00 AM | |
| Session 10 | Tue-06-Feb-24 | 08:00 PM to 09:00 PM | 06:00 AM to 07:00 AM | |
| Section I (Previous) | Session 1 | Mon-19-Feb-24 | 08:00 PM to 09:00 PM | 06:00 AM to 07:00 AM |
| Session 2 | Tue-20-Feb-24 | 08:00 PM to 09:00 PM | 06:00 AM to 07:00 AM | |
| Session 3 | Mon-26-Feb-24 | 08:00 PM to 09:00 PM | 06:00 AM to 07:00 AM | |
| Session 4 | Tue-27-Feb-24 | 08:00 PM to 09:00 PM | 06:00 AM to 07:00 AM | |
| Session 5 | Mon-04-Mar-24 | 08:00 PM to 09:00 PM | 06:00 AM to 07:00 AM | |
| Session 6 | Tue-05-Mar-24 | 08:00 PM to 09:00 PM | 06:00 AM to 07:00 AM | |
| Session 7 | Mon-11-Mar-24 | 08:00 PM to 09:00 PM | 06:00 AM to 07:00 AM | |
| Session 8 | Tue-12-Mar-24 | 08:00 PM to 09:00 PM | 06:00 AM to 07:00 AM | |
| Session 9 | Mon-18-Mar-24 | 08:00 PM to 09:00 PM | 06:00 AM to 07:00 AM | |
| Session 10 | Tue-19-Mar-24 | 08:00 PM to 09:00 PM | 06:00 AM to 07:00 AM | |
| Section I (Previous) | Session 1 | Mon-01-Apr-24 | 08:00 PM to 09:00 PM | 06:00 AM to 07:00 AM |
| Session 2 | Tue-02-Apr-24 | 08:00 PM to 09:00 PM | 06:00 AM to 07:00 AM | |
| Session 3 | Mon-08-Apr-24 | 08:00 PM to 09:00 PM | 06:00 AM to 07:00 AM | |
| Session 4 | Tue-09-Apr-24 | 08:00 PM to 09:00 PM | 06:00 AM to 07:00 AM | |
| Session 5 | Mon-15-Apr-24 | 08:00 PM to 09:00 PM | 06:00 AM to 07:00 AM | |
| Session 6 | Tue-16-Apr-24 | 08:00 PM to 09:00 PM | 06:00 AM to 07:00 AM | |
| Session 7 | Mon-22-Apr-24 | 08:00 PM to 09:00 PM | 06:00 AM to 07:00 AM | |
| Session 8 | Tue-23-Apr-24 | 08:00 PM to 09:00 PM | 06:00 AM to 07:00 AM | |
| Session 9 | Mon-29-Apr-24 | 08:00 PM to 09:00 PM | 06:00 AM to 07:00 AM | |
| Session 10 | Tue-30-Apr-24 | 08:00 PM to 09:00 PM | 06:00 AM to 07:00 AM | |
| Section I (Current) | Session 1 | Mon-13-May-24 | 08:00 PM to 09:00 PM | 06:00 AM to 07:00 AM |
| Session 2 | Tue-14-May-24 | 08:00 PM to 09:00 PM | 06:00 AM to 07:00 AM | |
| Session 3 | Mon-20-May-24 | 08:00 PM to 09:00 PM | 06:00 AM to 07:00 AM | |
| Session 4 | Tue-21-May-24 | 08:00 PM to 09:00 PM | 06:00 AM to 07:00 AM | |
| Session 5 | Mon-27-May-24 | 08:00 PM to 09:00 PM | 06:00 AM to 07:00 AM | |
| Session 6 | Tue-28-May-24 | 08:00 PM to 09:00 PM | 06:00 AM to 07:00 AM | |
| Session 7 | Mon-03-Jun-24 | 08:00 PM to 09:00 PM | 06:00 AM to 07:00 AM | |
| Session 8 | Tue-04-Jun-24 | 08:00 PM to 09:00 PM | 06:00 AM to 07:00 AM | |
| Session 9 | Mon-10-Jun-24 | 08:00 PM to 09:00 PM | 06:00 AM to 07:00 AM | |
| Session 10 | Tue-11-Jun-24 | 08:00 PM to 09:00 PM | 06:00 AM to 07:00 AM | |
| Section I (Upcoming) | Session 1 | Mon-24-Jun-24 | 08:00 PM to 09:00 PM | 06:00 AM to 07:00 AM |
| Session 2 | Tue-25-Jun-24 | 08:00 PM to 09:00 PM | 06:00 AM to 07:00 AM | |
| Session 3 | Mon-01-Jul-24 | 08:00 PM to 09:00 PM | 06:00 AM to 07:00 AM | |
| Session 4 | Tue-02-Jul-24 | 08:00 PM to 09:00 PM | 06:00 AM to 07:00 AM | |
| Session 5 | Mon-08-Jul-24 | 08:00 PM to 09:00 PM | 06:00 AM to 07:00 AM | |
| Session 6 | Tue-09-Jul-24 | 08:00 PM to 09:00 PM | 06:00 AM to 07:00 AM | |
| Session 7 | Mon-15-Jul-24 | 08:00 PM to 09:00 PM | 06:00 AM to 07:00 AM | |
| Session 8 | Tue-16-Jul-24 | 08:00 PM to 09:00 PM | 06:00 AM to 07:00 AM | |
| Session 9 | Mon-22-Jul-24 | 08:00 PM to 09:00 PM | 06:00 AM to 07:00 AM | |
| Session 10 | Tue-23-Jul-24 | 08:00 PM to 09:00 PM | 06:00 AM to 07:00 AM |
