Machine Learning Project Pipeline

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1 session

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About

In this session, learners will explore the complete Machine Learning project pipeline—from collecting datasets to deploying trained models. We will discuss how data is gathered from open sources, how it is cleaned and analyzed through exploratory data analysis, and how useful features are engineered for model training. The session will also introduce train-test splitting, model selection, evaluation metrics, and basic deployment approaches. Learners will actively participate through guided discussion, short conceptual questions, and walkthroughs of each stage of a real ML workflow. 

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

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✋ 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

23

Mar

SESSION 1

Study Spaces

Mon 7:00 AM - 8:30 AM UTCMar 23, 7:00 AM - 8:30 AM UTC

This session explains the complete Machine Learning project pipeline from start to finish. Learners will understand how datasets are collected from open sources, how data is cleaned and explored using EDA, and how features are engineered for better model performance. The session will also cover train-test splitting, model training, evaluation metrics, and a basic overview of deploying trained models. Interaction will include guided explanations, conceptual questions, and discussion around real ML workflow examples.

Public Discussion

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Mar 23

1 week

90 mins

/ session

Next session on March 23, 2026

SCHEDULE

Monday, Mar 23

7:00AM