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Master Certificate in AI and Machine Learning

Fast Track Your Career

Build intelligent systems using AI, ML, Deep Learning, and Generative AI
Master Python, data science, and key ML techniques such as supervised learning and ensemble models
Work with TensorFlow, Keras, PyTorch, and large language models (LLMs)
Deploy real-world AI projects including image, video, and text generation use cases

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Programs

Master Certificate in AI and Machine Learning

Duration

6 months

Duration

6 months

Duration

6 months

Start Date

Oct 13, 2025

Registration Deadline

Sep 15, 2025

Start Date

Oct 13, 2025

Registration Deadline

Sep 15, 2025

Start Date

Oct 13, 2025

Registration Deadline

Sep 15, 2025

Events

Attend an

<<Info session>>

Events

Attend an

<<Info session>>

Events

Attend an

<<Info session>>

Program Overview

Master AI, Machine Learning, and Generative AI through hands-on training in Python, deep learning, and agentic frameworks. Gain practical, real-world experience by working on projects that develop high-demand skills such as AI automation, large language model (LLM) proficiency, and advanced machine learning techniques. Equip yourself to lead in the AI-driven future and advance your career with this comprehensive program.

Enroll

6 months

Contact an advisor

Oct 13, 2025

Tuition Fee

Duration

Start Date

Oct 13, 2025

Duration

6 months

Tuition Fee

Contact The Advisor

Enroll

Start Date

Oct 13, 2025

Duration

6 months

Tuition Fee

Contact The Advisor

Enroll

Choose How You Learn

We’ve designed flexible learning formats to fit different student needs, learning styles, and budgets - giving you the freedom to choose how you want to build your career.

Step 1: Choose Your Career Path

Features:

Meet with the program director
Complete a Skill assessment
Begin with introductory and prerequisite courses

Price:

starting as low as 499 $ - credited towards tuition

Confirmation of admission into the diploma program

Instructor-Led (Online Live Classes)

Features:

Live online workshops classes led by experienced instructors
Interactive discussions, direct Q&A, and peer collaboration, customized labs and group projects
1:1 mentor support for career alignment
Job-readiness workshops (resume, interview prep, consulting exposure)

Price:

Diploma Program | call us

*Apply for Scholarship towards the tuition fees

Course Outline

Our curated Master Certificate in AI and Machine Learning program is designed to propel you toward a successful career in artificial intelligence and machine learning. Explore cutting-edge advancements such as ChatGPT, Generative AI (GenAI), and large language models (LLMs). Gain hands-on experience creating conversational chatbots, mastering content generation, and leveraging the latest AI technologies to solve real-world problems.

Download Outline

Python Fundamentals

Develop essential Python programming skills that form the foundation for AI and ML. Learn to implement algorithms, manage data, and build intelligent systems with Python.

Concepts Learned:

Fundamentals of programming with Python
Data types, operators, and string functions
Loops, conditionals, and variable scope
Functions, methods, attributes, and access modifiers
Multi-threading for performance optimization
Setting up Python IDEs and Jupyter Notebook environments

SKILLS GAINED

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SKILLS GAINED

……………………..

Python Fundamentals

Develop essential Python programming skills that form the foundation for AI and ML. Learn to implement algorithms, manage data, and build intelligent systems with Python.

Concepts Learned:

Fundamentals of programming with Python
Data types, operators, and string functions
Loops, conditionals, and variable scope
Functions, methods, attributes, and access modifiers
Multi-threading for performance optimization
Setting up Python IDEs and Jupyter Notebook environments

SKILLS GAINED

……………………..

Framer is a web builder for creative pros. Be sure to check out framer.com to learn more.

SKILLS GAINED

……………………..

Applied Data Science with Python

Gain a strong foundation in data science using Python. Learn data preparation, statistical analysis, visualization, and model building through real-world scenarios.

Concepts Learned:

Core principles of data science and analytics
Python essentials for data analysis (strings, lists, Lambda functions)
NumPy and linear algebra applications
Probability and statistical methods (Z-test, T-test, ANOVA)
Data manipulation and wrangling with Pandas
Data visualization using Matplotlib, Seaborn, Plotly, and Bokeh
End-to-end statistical analysis pipelines

SKILLS GAINED

……………………..

Framer is a web builder for creative pros. Be sure to check out framer.com to learn more.

SKILLS GAINED

……………………..

Machine Learning

Learn the core concepts of machine learning, including supervised and unsupervised models, the ML pipeline, and ensemble techniques. Gain hands-on experience with TensorFlow, Keras, and PyTorch.

Concepts Learned:

Supervised learning: regression and classification
Unsupervised learning: clustering and anomaly detection
Ensemble learning: bagging, boosting, stacking
Overfitting, underfitting, and model evaluation
ML pipeline and MLOps concepts
Recommendation engine design
Comparing frameworks: TensorFlow, Keras, PyTorch

SKILLS GAINED

……………………..

Machine Learning

Learn the core concepts of machine learning, including supervised and unsupervised models, the ML pipeline, and ensemble techniques. Gain hands-on experience with TensorFlow, Keras, and PyTorch.

Concepts Learned:

Supervised learning: regression and classification
Unsupervised learning: clustering and anomaly detection
Ensemble learning: bagging, boosting, stacking
Overfitting, underfitting, and model evaluation
ML pipeline and MLOps concepts
Recommendation engine design
Comparing frameworks: TensorFlow, Keras, PyTorch

SKILLS GAINED

……………………..

Deep Learning Specialization

Master the fundamentals of deep learning, from neural networks to advanced architectures like CNNs, RNNs, and transformers. Learn optimization techniques and practical applications in NLP and computer vision.

Concepts Learned:

Artificial and deep neural networks
Forward and backward propagation in DNNs
Hyperparameter tuning and performance optimization
Convolutional neural networks (CNNs) for vision tasks
Recurrent neural networks (RNNs) for sequential data
Transfer learning and object detection
Autoencoders and model interpretability
Transformer models for NLP

SKILLS GAINED

……………………..

Deep Learning Specialization

Concepts Learned:

Artificial and deep neural networks
Forward and backward propagation in DNNs
Hyperparameter tuning and performance optimization
Convolutional neural networks (CNNs) for vision tasks
Recurrent neural networks (RNNs) for sequential data
Transfer learning and object detection
Autoencoders and model interpretability
Transformer models for NLP

SKILLS GAINED

……………………..

Generative AI Literacy

Build literacy in Generative AI by exploring core algorithms and their applications. Learn how LLMs, GANs, and VAEs power chatbots, content generation, and creative AI tools.

Concepts Learned:

Transformer algorithms and neural networks
Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs)
Large Language Models (LLMs) including ChatGPT
AI-driven image and video generation
Tools and frameworks for GenAI experimentation

SKILLS GAINED

……………………..

Generative AI Literacy

Build literacy in Generative AI by exploring core algorithms and their applications. Learn how LLMs, GANs, and VAEs power chatbots, content generation, and creative AI tools.

Concepts Learned:

Transformer algorithms and neural networks
Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs)
Large Language Models (LLMs) including ChatGPT
AI-driven image and video generation
Tools and frameworks for GenAI experimentation

SKILLS GAINED

……………………..

Generative AI Essentials

Explore the fundamentals, evolution, and applications of Generative AI across multiple domains. Gain practical experience with industry-leading tools like GPT, DALL·E, Stable Diffusion, and Synthesia.

Concepts Learned:

Generative AI vs. discriminative AI
Text, image, code, audio, and video generation
ChatGPT and conversational AI applications
Implementing models with Hugging Face, TensorFlow, and PyTorch
Industry-wide applications of GenAI

SKILLS GAINED

……………………..

Generative AI Essentials

Concepts Learned:

Generative AI vs. discriminative AI
Text, image, code, audio, and video generation
ChatGPT and conversational AI applications
Implementing models with Hugging Face, TensorFlow, and PyTorch
Industry-wide applications of GenAI

SKILLS GAINED

……………………..

Foundation Models & GenAI Platforms

Learn how foundation models and pre-trained platforms power the next wave of AI. Explore IBM Watsonx, Hugging Face, Granite, GPT, and FLAN models for enterprise and open-source use cases.

Concepts Learned:

Deep learning and LLM fundamentals
Foundation models and pre-trained architectures
Text-to-text, text-to-image, and text-to-code generation
IBM Watsonx for enterprise AI applications
Hugging Face as an open-source hub for GenAI

SKILLS GAINED

……………………..

Foundation Models & GenAI Platforms

Learn how foundation models and pre-trained platforms power the next wave of AI. Explore IBM Watsonx, Hugging Face, Granite, GPT, and FLAN models for enterprise and open-source use cases.

Concepts Learned:

Deep learning and LLM fundamentals
Foundation models and pre-trained architectures
Text-to-text, text-to-image, and text-to-code generation
IBM Watsonx for enterprise AI applications
Hugging Face as an open-source hub for GenAI

SKILLS GAINED

……………………..

SKILLS GAINED

……………………..

SKILLS GAINED

……………………..

SKILLS GAINED

……………………..

SKILLS GAINED

……………………..

SKILLS GAINED

……………………..

Applied Data Science with Python

Gain a strong foundation in data science using Python. Learn data preparation, statistical analysis, visualization, and model building through real-world scenarios.

Concepts Learned:

Core principles of data science and analytics
Python essentials for data analysis (strings, lists, Lambda functions)
NumPy and linear algebra applications
Probability and statistical methods (Z-test, T-test, ANOVA)
Data manipulation and wrangling with Pandas
Data visualization using Matplotlib, Seaborn, Plotly, and Bokeh
End-to-end statistical analysis pipelines

SKILLS GAINED

Machine Learning

Learn the core concepts of machine learning, including supervised and unsupervised models, the ML pipeline, and ensemble techniques. Gain hands-on experience with TensorFlow, Keras, and PyTorch.

Concepts Learned:

Supervised learning: regression and classification
Unsupervised learning: clustering and anomaly detection
Ensemble learning: bagging, boosting, stacking
Overfitting, underfitting, and model evaluation
ML pipeline and MLOps concepts
Recommendation engine design
Comparing frameworks: TensorFlow, Keras, PyTorch

SKILLS GAINED

Deep Learning Specialization

Concepts Learned:

Artificial and deep neural networks
Forward and backward propagation in DNNs
Hyperparameter tuning and performance optimization
Convolutional neural networks (CNNs) for vision tasks
Recurrent neural networks (RNNs) for sequential data
Transfer learning and object detection
Autoencoders and model interpretability
Transformer models for NLP

SKILLS GAINED

Generative AI Literacy

Build literacy in Generative AI by exploring core algorithms and their applications. Learn how LLMs, GANs, and VAEs power chatbots, content generation, and creative AI tools.

Concepts Learned:

Transformer algorithms and neural networks
Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs)
Large Language Models (LLMs) including ChatGPT
AI-driven image and video generation
Tools and frameworks for GenAI experimentation

SKILLS GAINED

Generative AI Essentials

Concepts Learned:

Generative AI vs. discriminative AI
Text, image, code, audio, and video generation
ChatGPT and conversational AI applications
Implementing models with Hugging Face, TensorFlow, and PyTorch
Industry-wide applications of GenAI

SKILLS GAINED

Foundation Models & GenAI Platforms

Learn how foundation models and pre-trained platforms power the next wave of AI. Explore IBM Watsonx, Hugging Face, Granite, GPT, and FLAN models for enterprise and open-source use cases.

Concepts Learned:

Deep learning and LLM fundamentals
Foundation models and pre-trained architectures
Text-to-text, text-to-image, and text-to-code generation
IBM Watsonx for enterprise AI applications
Hugging Face as an open-source hub for GenAI

SKILLS GAINED

How to Apply

Scholarships

Financial Aid

Admission
process

Eligibility

Admission Process

Step 1: Book an Appointment with an Advisor
Step 2: Prepare Your Documents

Diploma and transcripts (High School, CEGEP, College, or University)
Birth Certificate (in English or French)
Proof of Canadian status
French language proficiency proof
Current resume
Two government-issued photo IDs
Step 3: Pay Application Fees
$50 application + $150 registration.

Step 4: Submit Your Application Form

Talk to an Advisor

Financial Aid

Scholarships

Admission process

Eligibility

Our financial partners offer loans and personalized support to local entrepreneurs and internationally trained professionals.
You can also apply with the government to get financial aid through the AFE loan program (Aide financière aux études/Student financial assistance).

➔ Attend an Info Session  

➔ Meet an Advisor  

➔ Submit Documents  

➔ Get Scholarship  

➔ Begin your Career

Attend an infosession

Scholarships

Financial Aid

Scholarships

Admission process

Eligibility

➔ Meet an Advisor  

➔ Submit Documents  

➔ Get Scholarship  

➔ Begin your Career

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