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).

• Live demo: https://www.r-siddiq.tech/ML-Info-Frontend/
• Additonal context: https://www.rsiddiq.com/internet-programming.html
• Scope: Frontend-only, static content
• Tech: HTML5, CSS3

• 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.

• 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.

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

Double-click index.html in any modern browser.

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.

• scikit-learn: https://scikit-learn.org
• IBM ML overview: https://www.ibm.com/topics/machine-learning
• WEF on Quantum ML: https://www.weforum.org/agenda/2022/06/quantum-machine-learning-cybersecurity/
• “Attention is All You Need” (Transformer): https://arxiv.org/abs/1706.03762