ML Hero Quest

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Become a fearless ML Hero

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Liam Isaiah Porteria

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ML Hero QuestVersión en línea Become a fearless ML Hero por Liam Isaiah Porteria 1 Slide 1: Welcome, ML Hero Meet the ML Hero, guiding you through data quests, model training, and epic predictions. 2 Slide 2: What is ML? In this quest, machine learning means teaching computers to learn from data and make useful decisions without explicit programming. 3 Slide 3: Data is King Your journey starts with data: quality, quantity, and representation determine how well your hero learns and generalizes to new problems. 4 Slide 4: Features and Labels Features are the input signals the model uses; labels are the ground truth answers you train against. 5 Slide 5: Training vs Evaluation Training optimizes the model with loss minimization, while evaluation tests its performance on unseen data to prevent overfitting. 6 Slide 6: Choosing Algorithms Choose an algorithm aligning with your task: linear models, trees, or neural networks, each with strengths and trade-offs. 7 Slide 7: Overfitting and Bias Be mindful of overfitting and bias; your hero must generalize well and treat all groups fairly. 8 Slide 8: Metrics Use appropriate metrics: accuracy, precision, recall, F1, or ROC-AUC to measure true performance. 9 Slide 9: Deployment Once ready, deploy the model responsibly, monitor performance, and update as data evolves in the real world. 10 Slide 10: Ethics & Explainability Ethics and transparency matter; explainability helps users trust your ML Hero and its decisions.

ML Hero QuestVersión en línea

Become a fearless ML Hero

por Liam Isaiah Porteria

Slide 1: Welcome, ML Hero

Meet the ML Hero, guiding you through data quests, model training, and epic predictions.

Slide 2: What is ML?

In this quest, machine learning means teaching computers to learn from data and make useful decisions without explicit programming.

Slide 3: Data is King

Your journey starts with data: quality, quantity, and representation determine how well your hero learns and generalizes to new problems.

Slide 4: Features and Labels

Features are the input signals the model uses; labels are the ground truth answers you train against.

Slide 5: Training vs Evaluation

Training optimizes the model with loss minimization, while evaluation tests its performance on unseen data to prevent overfitting.

Slide 6: Choosing Algorithms

Choose an algorithm aligning with your task: linear models, trees, or neural networks, each with strengths and trade-offs.

Slide 7: Overfitting and Bias

Be mindful of overfitting and bias; your hero must generalize well and treat all groups fairly.

Slide 8: Metrics

Use appropriate metrics: accuracy, precision, recall, F1, or ROC-AUC to measure true performance.

Slide 9: Deployment

Once ready, deploy the model responsibly, monitor performance, and update as data evolves in the real world.

Slide 10: Ethics & Explainability

Ethics and transparency matter; explainability helps users trust your ML Hero and its decisions.

Puedes integrar el juego en un LMS compatible con LTI 1.1 o LTI 1.3 como Canvas, Moodle, o Blackboard. De esta manera podrás guardar las puntuaciones automáticamente en el libro de calificaciones de esa plataforma.