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Study guide: feature engineering for ML v1.0.0

Use this resource to learn feature engineering for machine learning, from theory to code

New

  • 463 downloads
  • 10 likes
  • 235 saves

Free

Install with the CLI

In a terminal, from the folder you want:

npx --yes palatekit@latest install ml-feature-engineering-study-guide

First time: connect the CLI with

npx --yes palatekit@latest login

The prompt asks for a structured explanation: the mathematical intuition behind the algorithm, a commented step-by-step code example, then ideas for experiments. It asks for references to academic sources and a path for further study.

Mostly picked by

Updated November 2, 2026

Card for the “Study guide: feature engineering for ML” prompt

Details

Works in
Install guides
Generates with
Your agent's model
Licence
CC0-1.0

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What you get

  • The full prompt, ready to paste
  • Theory, code example and extension ideas
  • References for further reading

Good fit for

  • Preparing a course on data preparation pipelines
  • Reviewing feature preparation before a modelling project

Not for

    Tags

    • machine learning
    • features
    • Python
    • training

    About

    You say

    “Explain target encoding for a high-cardinality categorical variable, with a Python example.”

    Card for the “Study guide: feature engineering for ML” prompt

    Prompt published by abhishekojha.edu@gmail.com under the CC0-1.0 license, delivered in its original version.

    Show more

    How to use it

    1. Paste the prompt into your assistant or agent
    2. Ask it to learn feature engineering for machine learning, from theory to code
    3. Review and adjust the result

    Content preview

    Files

    • LICENSE0.6 KB
    • ml-feature-engineering-study-guide.md1.6 KB
    • NOTICE-PALATEKIT.md0.5 KB

    Origin and licence

    Listed unmodified

    Author
    abhishekojha.edu@gmail.com
    Source
    github.com/f/prompts.chat/blob/cecef6fc73c2c389770695a56252d331981727de/prompts.csv
    Licence
    CC0-1.0

    Notice

    « Generating Effective Study references for AI/ML Learning Concepts » (abhishekojha.edu@gmail.com, CC0-1.0) livré dans sa version d'origine : fiche en français et en anglais par PalateKit.

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