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docs: fix spelling errors in README - #4

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mayuriphad:fix/readme-typos
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mayuriphad wants to merge 1 commit into
masinoa:masterfrom
mayuriphad:fix/readme-typos

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@mayuriphad

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Fixed a few typos in the README.md file:

  • algorithims -> algorithms
  • avialable -> available
  • orginal -> original
  • ommissions -> omissions
  • attribued -> attributed
  • aforemention -> aforementioned

Copilot AI lite review requested due to automatic review settings September 3, 2026 05:37

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🟢 Approval recommended

Changes are limited to documentation spelling fixes, with only a minor optional Markdown link-formatting improvement suggested.

Pull request overview

Updates the repository’s README to correct several spelling mistakes in the introductory description of the project.

Changes:

  • Corrects multiple typos in the README’s opening paragraph (e.g., “algorithims” → “algorithms”, “ommissions” → “omissions”).
  • Improves wording accuracy in the attribution sentence (e.g., “attribued” → “attributed”, “aforemention” → “aforementioned”).
File summaries
File Description
README.md Fixes spelling errors in the project’s introductory README text.
Review details
  • Files reviewed: 1/1 changed files
  • Comments generated: 1
  • Review effort level: Lite

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Comment thread README.md
================

This repo contains a collection of IPython notebooks detailing various machine learning algorithims. In general, the mathematics follows that presented by Dr. Andrew Ng's Machine Learning course taught at Stanford University (materials available from [ITunes U] (http://www.apple.com/education/itunes-u/), Stanford Machine Learning), Dr. Tom Mitchell's course at Carnegie Mellon (materials avialable [here](http://www.cs.cmu.edu/~tom/10701_sp11/)), and Christopher M. Bishop's "Pattern Recognition And Machine Learning". Unless otherwise noted, the Python code is orginal and any errors or ommissions should be attribued to me and not the aforemention authors.
This repo contains a collection of IPython notebooks detailing various machine learning algorithms. In general, the mathematics follows that presented by Dr. Andrew Ng's Machine Learning course taught at Stanford University (materials available from [ITunes U] (http://www.apple.com/education/itunes-u/), Stanford Machine Learning), Dr. Tom Mitchell's course at Carnegie Mellon (materials available [here](http://www.cs.cmu.edu/~tom/10701_sp11/)), and Christopher M. Bishop's "Pattern Recognition And Machine Learning". Unless otherwise noted, the Python code is original and any errors or omissions should be attributed to me and not the aforementioned authors.
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2 participants