ML Hero Quest

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Become a fearless ML Hero

Créé par

Liam Isaiah Porteria

Philippines

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ML Hero QuestVersion en ligne Become a fearless ML Hero par 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 QuestVersion en ligne

Become a fearless ML Hero

par 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.

Vous pouvez intégrer le jeu dans un LMS compatible avec LTI 1.1 ou LTI 1.3 comme Canvas, Moodle ou Blackboard. Les scores seront ainsi automatiquement enregistrés dans le carnet de notes de la plateforme.

ML Hero Quest

Présentation

À propos de cette activité

Créé par

Téléchargez la version pour jouer sur papier

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ML Hero QuestVersion en ligne

par Liam Isaiah Porteria

Slide 1: Welcome, ML Hero

Slide 2: What is ML?

Slide 3: Data is King

Slide 4: Features and Labels

Slide 5: Training vs Evaluation

Slide 6: Choosing Algorithms

Slide 7: Overfitting and Bias

Slide 8: Metrics

Slide 9: Deployment

Slide 10: Ethics & Explainability

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