Build and evaluate linear regression models using Python and scikit-learn. Practice with housing price data from Toronto and Vancouver to predict market trends and understand model performance metrics.

Implement decision trees, random forests, and support vector machines for classification tasks. Work with Canadian census data to classify demographic patterns and evaluate model accuracy using cross-validation techniques.

Explore unsupervised learning through K-means, hierarchical clustering, and DBSCAN algorithms. Analyze customer segmentation data from Canadian retail companies to identify market patterns and business insights.

Build your first neural network from scratch using TensorFlow and Keras. Learn about perceptrons, activation functions, and backpropagation through hands-on coding exercises with Canadian weather prediction data.

Master image recognition using convolutional neural networks (CNNs). Practice with Canadian wildlife image datasets to build models that can identify different animal species and understand computer vision principles.

Implement LSTM and GRU networks for sequence prediction and natural language processing. Work with Canadian news articles and social media data to build sentiment analysis and text generation models.

Develop AI solutions for Canadian healthcare challenges including medical image analysis, patient data processing, and diagnostic assistance systems. Learn about privacy regulations and ethical considerations in medical AI.

Build AI systems for Canadian financial institutions including fraud detection, risk assessment, and algorithmic trading. Work with real market data from TSX and understand regulatory compliance requirements.

Create AI applications for Canadian smart city initiatives including traffic optimization, energy management, and urban planning. Use data from Toronto, Montreal, and Vancouver to solve real municipal challenges.