AI & Data
Science
Build Canada's AI-powered workforce foundations with hands-on ML tools.
Structured learning journey.
Introduction to AI & Data Science
This module introduces the foundational concepts of Artificial Intelligence (AI), Machine Learning (ML), and Data Science. Learners will explore how AI systems work, common applications across industries, career pathways in AI and data science, and the ethical responsibilities associated with developing and using AI technologies. By the end of this module, learners will understand the core concepts that drive modern AI systems and the opportunities available in the growing field of data science.
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Python Programming Fundamentals
This module introduces Python programming fundamentals for AI and Data Science. Learners will explore Python syntax, variables, data types, control structures, functions, modules, libraries, and packages. By the end of this module, learners will be able to write basic Python programs, organize reusable code, and use external libraries commonly employed in data science and AI projects.
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Data Structures & Algorithms
This module introduces the foundational data structures and algorithmic concepts used in Python programming, data science, and artificial intelligence. Learners will explore lists, tuples, dictionaries, sets, string manipulation techniques, algorithm complexity basics, and practical problem-solving strategies. By the end of this module, learners will understand how to organize data efficiently, analyze algorithm performance, and develop logical approaches to solving programming problems.
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NumPy & Pandas
This module introduces two of the most important libraries in Python for data science and artificial intelligence: NumPy and Pandas. Learners will explore NumPy arrays, mathematical operations, Pandas DataFrames and Series, data loading from various sources, and data cleaning techniques used to prepare datasets for analysis and machine learning. By the end of this module, learners will understand how to efficiently work with structured data and perform common preprocessing tasks.
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Data Visualization
This module introduces the fundamentals of data visualization using Python. Learners will explore Matplotlib, Seaborn, and Plotly, three of the most widely used visualization libraries in data science and analytics. The module also covers how to select the most effective chart types for different kinds of data and business questions. By the end of the module, learners will be able to create clear, informative, and visually effective charts that communicate insights to technical and non-technical audiences.
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Exploratory Data Analysis (EDA)
This module introduces Exploratory Data Analysis (EDA), one of the most important stages of the data science lifecycle. Learners will explore descriptive statistics, correlation analysis, outlier detection, and data profiling techniques used to understand datasets before building machine learning models or conducting advanced analytics. By the end of the module, learners will be able to summarize data, identify patterns, detect anomalies, and uncover insights that support better decision-making and model development.
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Statistics for Data Science
This module introduces the statistical concepts that form the foundation of data science, machine learning, and analytics. Learners will explore probability fundamentals, common probability distributions, hypothesis testing, and confidence intervals. These concepts help data professionals make predictions, evaluate evidence, quantify uncertainty, and draw reliable conclusions from data. By the end of the module, learners will understand how statistics supports data-driven decision-making across industries.
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Machine Learning Essentials
This module introduces the core concepts of machine learning, including supervised learning, unsupervised learning, and reinforcement learning. Learners will explore how machine learning models are trained, validated, and tested, as well as common challenges such as overfitting and underfitting. The module also covers model evaluation metrics used to measure performance and determine whether a model is suitable for real-world deployment.
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Supervised Learning Algorithms
This module introduces some of the most widely used supervised machine learning algorithms: linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), and k-nearest neighbors (KNN). Learners will understand how these algorithms work, their strengths and limitations, and the types of business problems they solve. By the end of this module, learners will be able to identify which algorithm is appropriate for different predictive analytics tasks.
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Unsupervised Learning
This module introduces unsupervised learning, a branch of machine learning that identifies patterns, structures, and relationships within unlabeled data. Learners will explore K-means clustering, hierarchical clustering, Principal Component Analysis (PCA), and dimensionality reduction techniques. These methods help organizations discover hidden insights, segment customers, detect anomalies, and simplify complex datasets for analysis and visualization.
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Natural Language Processing (NLP)
This module introduces Natural Language Processing (NLP), a branch of artificial intelligence that enables computers to understand, analyze, and generate human language. Learners will explore text preprocessing techniques such as tokenization, stemming, and lemmatization, as well as key NLP applications including sentiment analysis, named entity recognition (NER), text classification, and topic modeling. By the end of this module, learners will understand how NLP transforms unstructured text into valuable insights for business, research, customer service, and AI applications.
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Computer Vision Basics
This module introduces Computer Vision, a field of artificial intelligence that enables computers to interpret and understand visual information from images and videos. Learners will explore image processing fundamentals, Convolutional Neural Networks (CNNs), object detection and image classification techniques, and transfer learning using pre-trained models. By the end of this module, learners will understand how computer vision powers technologies such as facial recognition, medical imaging, autonomous vehicles, quality inspection systems, and smart surveillance.
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Deep Learning Fundamentals
This module introduces Deep Learning, a specialized branch of machine learning that uses multi-layer neural networks to solve complex problems involving images, text, speech, and predictive analytics. Learners will explore neural network architecture, activation functions, backpropagation, TensorFlow and Keras frameworks, and the process of training deep learning models. By the end of this module, learners will understand how deep learning systems learn from data and power many modern AI applications.
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Model Deployment & MLOps
This module introduces Model Deployment and Machine Learning Operations (MLOps), the discipline of moving machine learning models from development environments into production systems. Learners will explore model serialization, saving and loading trained models, API development using Flask and FastAPI, Docker containerization, and cloud deployment using AWS, Microsoft Azure, and Google Cloud Platform (GCP). By the end of this module, learners will understand how machine learning solutions are deployed, maintained, monitored, and scaled in real-world environments.
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Portfolio Projects & Career
This module focuses on transforming technical data science skills into career opportunities. Learners will build end-to-end portfolio projects that demonstrate real-world problem-solving abilities, participate in Kaggle competitions to gain practical experience, develop professional GitHub portfolios, and prepare for technical interviews and assessments. By the end of this module, learners will understand how to showcase their skills effectively and position themselves for careers in data science, machine learning, analytics, and artificial intelligence.
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Professional Certification
Complete 15 modules to unlock your verified certificate.