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Teachable Machine: Emotion or Object Recognizer

Real-world project · AICTE-aligned · AI-graded · Audit-ready certificate

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About this project

Teachable Machine: Emotion or Object Recognizer

Objective: Build a simple AI system that can recognize emotions or objects using a web-based tool.

Context: Automated recognition of emotions or objects is increasingly used in education, safety, and entertainment, making it important for students to understand how such AI works.

What you'll build: You will select a category (like happy/sad faces or common objects), collect and upload example images, and use Teachable Machine to train a classification model. You will then test your model and present how it makes decisions, gaining hands-on experience with supervised learning and web-based AI tools.

Deliverables: A trained AI model, a demonstration of its predictions, and a short presentation explaining your process and results.

Milestones
1. Choose what to recognize (e.g., happy/sad faces, books/pencils)
5 marks 2d
Decide on a recognition task: either distinguish between at least two emotions (e.g., happy vs. sad faces) or between two types of objects (e.g., books vs. pencils). Clearly state your chosen categories and provide a brief justification for your selection. Done means your submission specifies the categories and explains why they are suitable for image-based recognition.
2. Collect and upload examples
5 marks 2d
Gather at least 20 clear image examples for each chosen category using your webcam or uploaded files, ensuring variety in lighting, angles, and backgrounds. Upload these images to Teachable Machine, labeling them accurately. Done means your dataset is complete, well-labeled, and diverse enough to train a robust model.
3. Train and test the model
5 marks 2d
Use Teachable Machine to train your model with the collected images, then test it using at least five new images per category that were not in the training set. Report the model's accuracy and any misclassifications. Done means you submit screenshots or logs showing training results and a table summarizing test outcomes.
4. Present how AI makes decisions
5 marks 2d
Explain, in your own words, how your trained AI model distinguishes between the categories, referencing specific visual features it likely uses (e.g., mouth shape for emotions, object outlines for items). Support your explanation with example images and highlight any patterns you observed during testing. Done means your explanation is clear, evidence-based, and directly relates to your project's data.
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Upcoming sessions
SessionWindowEnrolled
Teachable Machine: Emotion or Object Recognizer 11 Jun 2026 to 10 Jun 2028 0
Skills you'll learn
data-start="1123" data-end="1162">Basic understanding of classificationdata-start="1165" data-end="1189">Use of web-based toolsdata-start="1192" data-end="1230">Simple image collection and labeling
Prerequisites
Camera-enabled device Supervision for image capture (privacy awareness)
Available mentors
Priyang Kumar
Free
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Teachable Machine: Emotion or Object Recognizer

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