Learn the essentials fast — without losing rigor.

The AFAP series is built for engineers and researchers who want a clear, example-driven path: concepts → minimal theory → practical code → real evaluation. Book 1 is published, and the accompanying code will be available soon on Pythonholics.

Buy Book 1 on Amazon Get the code (GitHub)

TIP: Replace placeholder text like “AFAP — Book 1” and the cover image path (/assets/afap/book-1-cover.jpg) with your real data.

What makes AFAP different?

Speed-first structure
Only the theory you need, immediately followed by implementation and results.
Leakage-safe workflows
Train/validation/test discipline, proper CV, and reproducible evaluation.
Hands-on code + results
Code will be hosted on Pythonholics (with datasets, scripts, and logs). - Please Check GITHUB
Engineer-friendly explanations
No fluff: focus on what actually works and why.

Published books

Published Hardcover ASIN: B0GF2PGVGB

AFAP — Book 1 (replace with the official title)

Author: Axl Nickleson • Practical, example-driven learning

A fast and clear introduction with real, reproducible examples. Built for people who want results, not endless theory. Includes a planned code companion on Pythonholics.

View on Amazon Code & resources

Note: If you want the Amazon preview/description text here, paste it into this page (or add it as a short excerpt).

Code & resources (coming soon)

The companion code for the AFAP books will be hosted on Pythonholics: scripts, notebooks, datasets, and reproducible experiment logs.

End-to-end scripts
Train, validate, test, and export models with clean structure.
Reproducible evaluation
Proper CV, leakage-safe pipelines, and full metric reporting.
Minimal dependencies
Clean Python + scikit-learn style, easy to run.

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Recommended way to use the series

Each book is designed to be read fast, then used as a reference while you build.

Read once quickly to get the map of the topic.
Run the code (as soon as it’s published on Pythonholics) and reproduce results.
Modify experiments: change splits, models, and metrics to match your use-case.
Use as a field manual: short, practical sections you can revisit anytime.

If you want, add a “Resources” box here with: GitHub link, dataset list, and the exact folder structure you’ll publish.

Roadmap

Upcoming AFAP books will appear here as they move from planning → writing → published.

AFAP — Book 2
Status: In writing • Topic: (replace with title/topic)
AFAP — Book 3
Status: Planned • Topic: (replace with title/topic)
AFAP — Book 4
Status: Planned • Topic: (replace with title/topic)

Want a “Coming soon” banner per book? Add a badge like Planned or In writing.

FAQ

When will the code be available?

Soon on Pythonholics. This page will be updated with direct links to the repository/notebooks as soon as they are published.

Do I need to read all AFAP books in order?

Not necessarily. Each volume is designed to be useful as a standalone reference, but reading in order can help build a structured foundation.

What’s the “AFAP” philosophy?

Get to a working, correct solution quickly: the smallest amount of theory needed, followed by practical code and reproducible evaluation.

Where can I suggest topics or report issues?

Add a contact link (email or GitHub issues) in the footer when your code repository is live.

PYTHONHOLICS

As Fast As Possible Book Series