Name: Round Structure
Uploaded: 2025-06-02T21:50:22+00:00
Duration: 2 min 15 s
Description: In this section, we dive into one of the most important—and often most hands-on—parts of the ML interview process: the p

Round Structure

In this section, we dive into one of the most important—and often most hands-on—parts of the ML interview process: the practical machine learning coding and modeling round. This is where we demonstrate how we think and work like real ML practitioners.

- We’ll usually work in a **Jupyter notebook environment**, either in a take-home format or during a live 60–90 minute session.
- The task typically involves a **real-world problem** like text classification or demand forecasting, where we're expected to deliver an end-to-end ML workflow.
- Unlike algorithm rounds, we’re **encouraged to use standard ML libraries** (e.g., pandas, scikit-learn, TensorFlow, PyTorch) to solve the problem efficiently.
- We’ll be evaluated on how we **explore data, engineer features, choose models, tune hyperparameters**, and **communicate our results**.
- This round is about **thinking like an ML practitioner**, showing that we can handle messy, ambiguous problems using practical tools to deliver meaningful solutions.

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In this section, we dive into one of the most important—and often most hands-on—parts of the ML interview process: the p

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