Machine Learning - Model Evaluation, Validation & Deployment

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1 session

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About

Explore how machine learning models are evaluated, validated, and deployed into real-world systems. Learn key metrics like accuracy, precision, recall, and F1-score, understand validation strategies such as cross-validation, and see how trained models transition from experiments to production systems.

Tutored by

Dwij V 🇮🇳

Certified in 2 topics

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I'm a high schooler from India. I’m a motivated learner with a strong interest in mathematics, problem-solving, and analytical thinking. I enjoy breaking down complex concepts into clear, logical steps and helping others build confidence through understanding rather than memorization. I’m joining Schoolhouse to both sharpen my own foundations and contribute by supporting peers in structured, concept-driven learning. I value discipline, consistency, and depth, and I believe collaborative learning is one of the fastest ways to grow intellectually while giving back to a serious academic community.

Sessions

Upcoming

All

✋ ATTENDANCE POLICY

Students must join the session on time and remain present for the full duration. Active participation is expected during questions, discussions, and the neural network design activity. Repeated late arrivals, extended inactivity, or leaving early without notice may lead to removal from the session.

SESSION 1

20

Mar

SESSION 1

Study Spaces

Fri 7:00 AM - 8:30 AM UTCMar 20, 7:00 AM - 8:30 AM UTC

Understand how machine learning models are evaluated, validated, and deployed in real-world systems. We will cover evaluation metrics, validation strategies like train/test splits and cross-validation, error analysis using confusion matrices, and the fundamentals of deploying models in batch and real-time environments.

Public Discussion

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Mar 20

1 week

90 mins

/ session

Next session on March 20, 2026

SCHEDULE

Friday, Mar 20

7:00AM