/ML-Info-Frontend

A lightweight, frontend-only, multi-page static site introducing core machine learning concepts—learning paradigms, algorithms, model evaluation, and future trends—using HTML5 and CSS3 (no build step).

Primary LanguageHTML

ML Info Frontend

A lightweight, frontend-only, multi-page static site introducing core machine learning concepts—learning paradigms, algorithms, model evaluation, and future trends—using HTML5 and CSS3 (no build step).

Pages

  • index.html – Overview of machine learning and key learning paradigms.
  • algorithms.html – Survey of common ML algorithms across paradigms with an illustrative code snippet.
  • evaluation.html – Model evaluation metrics (classification/regression) and validation strategies.
  • future.html – Trends like Transformers, Generative AI, and Quantum ML.

Features

  • Static, no-build: open index.html directly—no tooling or server required.
  • Consistent navigation across pages.
  • Responsive images via CSS (max-width: 100%) and viewport meta tag.
  • Accessible alt text on images.
  • Academic context: created for an Internet Programming course; content is educational.

Project Structure

ML-Info-Frontend/
├─ index.html, algorithms.html, evaluation.html, future.html
├─ css/
│  └─ styles.css
└─ img/
   └─ confusion_matrix.jpg, future_ml.jpg, headshot.jpg, kfold_cv.jpg, reinforcement_learning.jpg, supervised_learning.jpg, unsupervised_learning.jpg

Getting Started

Option A: Open directly

Double-click index.html in any modern browser.

Option B (recommended): Serve statically

Some browsers throttle local assets. Use a simple local server:

  • Python 3

    python -m http.server

    Visit http://localhost:8000/

  • Node (npx)

    npx http-server .

    Follow the printed URL.

References & Acknowledgments