Machine Learning Basics Data Science & Python

Prompts

• Frame a Problem for Machine Learning

  Decide whether ML is needed and what to predict.

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• Train-Test Split and Leakage Audit

  Make sure evaluation scores are honest.

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• Evaluation Metrics Chooser

  Pick metrics that match the real costs.

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• Baseline Model Plan Before Complexity

  Build the simplest model that could work.

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• Feature Engineering Brainstorm

  Create features from raw data thoughtfully.

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• Overfitting Diagnosis Walkthrough

  Recognize and fix a model that memorizes.

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• Explain a Model's Predictions to Stakeholders

  Communicate model behavior honestly.

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• Clustering Analysis Plan

  Group similar records and judge the result.

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• Model Documentation Card

  Document what a model is for and where it fails.

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• Time Series Forecasting Baselines

  Beat the naive forecast honestly.

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• Class Imbalance Handling Guide

  Deal with rare events without fooling yourself.

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• Recommendation or Ranking Prototype Plan

  Plan a simple recommender and its evaluation.

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• Cross-Validation Design for My Data

  Choose a validation scheme that matches reality.

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• Hyperparameter Tuning Plan

  Tune sensibly without overfitting the validation set.

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• Probability Calibration Check

  See whether predicted probabilities can be trusted.

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• Model Error Analysis Session

  Learn from the mistakes of a model.

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• Data Drift Monitoring Plan

  Detect when data or performance changes.

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• Text Classification Baseline

  Build a simple, honest baseline for text.

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• Model Fairness Check (Defensive Audit)

  Check a model for uneven performance across groups.

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• Model Deployment Readiness Checklist

  Check before a model goes live.

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