Artificial Intelligence and Machine Learning Specialist

The Artificial Intelligence and Machine Learning Specialist (AIMLS) program introduces students to the theoretical basis and practical applications of machine learning and data science. The use of machine learning technologies in data science is seeing exploding growth across many industries, performing a wide range of functions such as forecasting, optimization, and complex decision-making. Students will learn how to apply supervised and supervised learning in processing and analyzing data in their professional settings, as well as topics such as statistics, data visualization, and deep learning. This program is suitable for business professionals and managers who work directly or indirectly with any type of data and wish to stay competitive by leveraging the power of machine learning and data science.

NEXT START DATE:
July 4th, 2022
*In special circumstances, there might be an additional cohort with an earlier start date. Contact us for more details.

What you'll learn

Basic Python and statistics for machine learning
Create data visualization using Tableau, Power BI, Matplotlib, and Ggplot2
How to tell a compelling story using data visualization
The trends and use cases of machine learning in the business world
Implement supervised and unsupervised machine learning techniques using real-world datasets
Build predictive models using deep learning
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Introduction to Python for Data Science

The objective of this course is to introduce the course participant to the Python programming language for data science applications. By the end of this course, participants will be able to set up Python and have learnt the fundamental concepts required to derive actionable insights from data. Concepts such as data types, data analysis, data visualizations, database connectivity and file manipulation will be covered in detail. In addition, Python development using Jupyter Notebooks and Google Colab will also be explored. There will be lots of opportunities to apply the concepts learned in the course in the form of assignments and a group project.

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Statistics for Data Science

This course covers what you need to know about probability and statistics to succeed in business and the data science field. This is a very practical course and will go over both theory and implementations of statistics to real-world problems. Each section has multiple example problems and some sections have homework at the end.

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Storytelling Using Data

The objective of this course is to enable the participant to tell more effective stories using data. The foundational principles of narrating impactful stories, supported with the appropriate visualizations, will be discussed in detail. This course will cover the core phases of a data analytics project including clarification of the business outcome, data understanding, visualization, deployment, and presentation. Practical examples and mock-ups will be provided using some of the popular tools such as Tableau, Power BI, Matplotlib (Python) and Ggplot2 (R). There will be lots of opportunities to apply the concepts learned in the course in the form of assignments and a group project.

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Artificial Intelligence in Business

This course presents the current landscape of AI technologies, their core branches, and their applications in business. It provides an overview of the history and evolution of AI and the multiple paradigm shifts making AI today. An overview of the contribution of AI to markets and industries. The current advancements of AI in business domains and the spectrum of AI as a Service available in the cloud today.

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Introduction to Machine Learning

This course introduces the fundamentals of machine learning and data science. The theory part of the course is designed to explain the lifecycle of real-life machine learning projects with the help of established CRISM-DM (Cross-industry standard process for data mining) framework along with the underlying concepts that are fundamental to machine learning and deep learning such as types of machine learning, model training and selection, and evaluation. The lab section is designed to give you an opportunity to implement machine learning techniques learned throughout the course on some real-world datasets with an emphasis on practical application in business.

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Unsupervised Learning: The Power of Unlabeled Data

This module introduces the fundamentals of unsupervised learning and discusses its applications in clustering, dimensionality reduction, and anomaly detection.

Introduction to Deep Learning

This course introduces the fundamentals of artificial neural networks and deep learning. The course consists of two parts: theory of deep learning using slides as well as hands-on lab section for deep learning using Jupyter notebooks. The theory focuses on the conceptual understanding of deep learning as opposed to complex mathematical equations. The lab section covers several areas of deep learning that a data scientist is supposed to be familiar with. Upon completion of this course, one should be ready to start building predictive models at basic and fairly advanced levels using the Tensorflow/Keras framework in Python.

Maris Sekar

Maris Sekar is a professional computer engineer, Certified Information Systems Auditor (ISACA), and Senior Data Scientist (Data Science Council of America). He has a passion for using storytelling to communicate on high-risk items within an organization to enable better decision-making and drive operational efficiencies. He has cross-functional work experience in distinct domains such as risk management, data analysis, and consulting. Maris’ love for data has motivated him to win awards, write LinkedIn articles and publish research papers with IEEE on applied machine learning and
data science.

Dr. Mohammed Ashtari

My name is Mo Ashtari and I am a senior Data Scientist at the Bank of Canada. I started machine learning in 2003 where I managed to model the performance of rechargeable commercial batteries using artificial neural networks in MATLAB. Since then, I have been doing machine learning and have enjoyed every second of it. I continued my Ph.D. (building predictive models for pharmaceutical companies), and my postdoc (building predictive models to increase the encapsulation efficiency of m-RNA into cancerous cells) at Queen’s University, and the University of Calgary, respectively. Then I joined Thermo Fisher Scientific, one of the largest biotech companies in the world, working on a very large project named “virtual column”, which was a winning factor in the success of the “separation science” product line. At the same time, I was a Data Consultant at Cloudera, one of the most well-known Big Data companies in the world which brought me a lot of experience in different business sectors. During all these 7 years, I have been teaching at Southern Alberta Institute of Technology several courses for Python, machine learning, business analytics, and deep learning. Currently, I am working with the Bank of Canada, mostly on economic models to provide answers for the macroeconomic challenges we face in Canada as well as globally. Besides, I am teaching at several institutes and Universities/colleges.
I can assure you if you enjoy learning, this field is for you. About ten years ago, when I started to seriously consider machine learning as my career, I had a shortlist of topics I had to learn. Since then, I have been learning every single day. Today, my list is even longer.

Moez Ali

Moez Ali is a data scientist with more than a decade of experience in solving complex business problems through data, particularly in the domain of Healthcare, Education, Fintech, and Professional Consulting. Moez has a background in Finance and he is an associate member of the Chartered Institute of Management Accountants (UK), Chartered Global Management Accountant (CGMA), Chartered Professional Accountant, Ontario (CPA, CMA). He also has an undergrad degree in Commerce and Business with a dual Masters in Economics and Management Analytics.
He is currently leading a team of data scientists at Moneris Canada where he is building sophisticated data science solutions at scale to solve some of the most complex problems. Alongside the full-time day job, Moez is very passionate about teaching data science and open-source community contribution that’s where he dedicates over 40% of his time. Moez has also created an open-source, low-code machine learning library (PyCaret) which is used by over half a million data scientists around the world including some major tech companies and research organizations.
Moez was awarded, most-read writer two times in the area of Artificial Intelligence and Machine Learning in 2020. He was also nominated for Highly Influential Data Scientists in Canada and has an impressive following on social media.

Dr. Nathan Nifco

As an experienced AI practitioner and the architect of multiple high-tech entrepreneurial innovations and executive accomplishments in several organizations as a founder and CEO, Dr. Nifco’s doctoral Ph.D. studies on AI’s human and organizational systems implications, including four master’s degrees and multiple recognized industry certifications, have contributed to the practice, advancement and understanding of the road ahead with AI, ML and DL. As a passionate thinker in the systemic, ethical, organizational, and societal implications of AI, he has a keen interest in maintaining a balance between technological advancement and the evolution of our human consciousness. Dr. Nifco has developed the notion of ‘Organic thinking’ within the constructs of Knowledge, Awareness and Meaning to formulate potential models to understand and project the implications of AI in our own humanity.

Dr. Mostafa El Gamal

Dr. Mostafa El Gamal received the B.Sc. degree from Cairo University, Egypt, in 2004, the M.Sc. degree from the University of Waterloo, Canada, in 2012, and the Ph.D. degree from Worcester Polytechnic Institute, USA, in 2017. He has over 10 years of academic and industry experience in Artificial Intelligence and Information Theory. Mostafa is currently managing the Data Science team in a FinTech company.

Who is IGS?


The Institute of Global Specialists (IGS) is a not-for-profit college located in St. John, New Brunswick (CAN). However, IGS students may complete their courses from anywhere in the world, provided they have access to a reliable internet connection.

How are the classes delivered?

 

Our programs are 100% online and are accessible by all mobile devices. Students will simply need access to a reliable internet connection to access their student account. Study materials are pre-recorded or readings, slides, so you get to schedule your own study time. As long as you are meeting the deadlines for your assignments, quizzes and discussions, you’re good to go. Final course exams are typically scheduled on the last weekend of the course.


Instructors are available via email for additional guidance regarding course materials, and IGS staff will assist with any technical questions. While some courses don’t have live classes, they are designed to accommodate live interactions in the form of virtual office hours, as needed.

When do the IGS programs start?

 

We have monthly start dates. In special circumstances, there might be an additional cohort with an earlier start date. Contact us for more details!

What are the entrance requirements? How do I apply?

 

A demonstrated ability in English and prior studies at a post-secondary level or relevant professional experience: submission of a resume. We may request further documentation.

 

To apply, click here.

  • Select your program of choice
  • Fill out the required information
  • Submit a copy of your resume
  • Pay the $175.00 (plus tax) Application Feenon-refundable

 

Once you have done this, a member of our admissions team will contact you within 24-48 hours to confirm your application has been received and discuss the next steps.

What is the Cost of the IGS programs? Is financial assistance available?

 

Each module for every program costs CAD$586+tax where applicable. Students can pay as they go (course-by-course).

A non-refundable CAD$175 (plus tax) application fee is also required.

Financing options for 18, 24 or 30 months are also available. CAD$586+tax initial payment is required before the start of the first module.

How long are the IGS programs?

 

The IGS programs have 6, 7, 8 or 9 modules; and each module is 4-5 weeks long.

 

  • Foreign Service and International Development Specialist program (FSIDS): 6 modules (24 weeks)
  • Global Immigration Specialist program (GIS): 6 modules (26 weeks)
  • Global Supply Chain Specialist program (GPS): 7 modules (26 weeks)
  • Canada Border Service Specialist program (CBSS): 7 modules (29 weeks)
  • Artificial Intelligence and Machine Learning Specialist Program (AIML): 7 modules (28 weeks)
  • Global Education Specialist program (GES): 8 modules (39 weeks)
  • Canadian Immigration Law Specialist program (CIL): 9 modules (42 weeks)

Is career support available?

 

IGS students can join our free career placement module. This module is designed to support IGS students to transition seamlessly to a job, and understand the numerous pathways to various career opportunities. From writing their resume, cover letters, to tips on how to best navigate the job application process and handling a job interview, students will gain practical skills to help them succeed in their post-program endeavours. The module also focuses on helping IGS students develop engagement strategies, and identify networks and job opportunities with the public, private and not-for-profit sectors.

Try the first Module for FREE
A demo has been made available to you so that you can get familiar with online learning at IGS.

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