Black Boys Code Montréal - Training the Machine

In celebration of Black History Month, this workshop invites students to explore how diversity, culture, and representation influence the technologies shaping our world today. Students learn that Artificial Intelligence is not neutral—it reflects the data it is trained on—and that inclusive data leads to smarter, fairer, and more accurate AI systems. Through hands-on learning and real-world examples, learners discover how machines recognize faces, emotions, and language, and why it is important that all communities are represented in the digital future. In the Beginner Track, students train an emotion-recognition AI model using images and expressions, gaining firsthand experience in how data quality and diversity affect machine learning outcomes, while in the Advanced Track, students design the personality, purpose, and conversation flow of a chatbot using prompt engineering and blueprint planning, laying the foundation for building intelligent, responsible, and user-friendly AI systems in future workshops.

This workshop is FREE, and all learning materials are provided.

Theme: Learning Through Data

Students dive deeper into AI training by creating a more sophisticated emotion-detection model. They learn the critical concept that AI learns from data — the more examples you give it, the smarter it becomes. Students explore what makes good data vs. bad data and understand why diversity in training examples matters. The session focuses on building a 3–4 class emotion recognition model (Happy, Sad, Surprised, and optionally Angry).

• Reviewing Month 1 progress

• Learning about data quality (lighting, angles, backgrounds)

• Collecting 20–30 photos per emotion class

• Training an emotion detection model with proper data

• Testing model accuracy with different expressions

• Exporting trained models for next month

Students will:

• Understand how AI learns from data

• Recognize the importance of diverse training data

• Collect and organize quality training images

• Train a 3–4 class emotion detection model

• Test and evaluate model accuracy

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