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ML Compass Guide: Learn Machine Learning Algorithms — 18 websites ranked by shared content topics, category and on-page relevance.

Each result shows its full tech stack, contacts and AI-policy — not just a name · Browse all sites in Artificial Intelligence →

Domain	Match	Title	Country/Lang	Category	AI files	Contact	AI-protection
aayushgautam.tech ↗	71 match 2 shared topics	Aayush Gautam - AI & Machine Learning Developer	en	artificial-intelligence	robotsllmsaihumans	emailphone	none
debuggercafe.com ↗	70 match 2 shared topics	DebuggerCafe - Deep Learning, Machine Learning, Artificial Intelligence	en	artificial-intelligence WordPress WooCommerce	robotsllmsaihumans	emailphone	none
deepanshumody.com ↗	70 match 2 shared topics	Deepanshu Mody \| Machine Learning Research Engineer	en	artificial-intelligence	robotsllmsaihumans	emailphone	none
amaboh.com ↗	70 match 2 shared topics	Amaboh Achu - Machine Learning and data engineering portfolio	en	artificial-intelligence	robotsllmsaihumans	emailphone	none
pro-datascience.com ↗	70 match 2 shared topics	Zero Code AI Enabled Machine Learning Model Development tools	en	technology-and-computing WordPress WooCommerce	robots llmsaihumans	emailphone	none
aiart.com.au ↗	69 match 2 shared topics	Dr.Jason - Machine Learning Engineer	Australia en	artificial-intelligence	robotsllmsaihumans	emailphone	none
programmathically.com ↗	69 match 2 shared topics	Programmathically - A Blog on Building Machine Learning Solutions	en	artificial-intelligence WordPress	robotsllmsaihumans	emailphone	none
deep-learning-mastery.com ↗	69 match 2 shared topics	Tobias Klein Machine Learning Engineer Portfolio	en	artificial-intelligence	robotsllmsaihumans	emailphone	none
deepinfra.ai ↗	69 match 2 shared topics	Machine Learning Models and Infrastructure \| DeepInfra	en	artificial-intelligence	robotsllmsaihumans	emailphone	none
deepinfra.com ↗	69 match 2 shared topics	Machine Learning Models and Infrastructure \| DeepInfra	en	artificial-intelligence	robotsllmsaihumans	emailphone	none
aadityachapagain.com ↗	69 match 2 shared topics	Aaditya Chapagain \| Machine Learning Engineer & Full Stack Developer	en	artificial-intelligence	robotsllmsaihumans	emailphone	none
fkleineboesing.com ↗	68 match 2 shared topics	Felix Kleine Bösing, Machine Learning Engineer	en	artificial-intelligence	robotsllmsaihumans	emailphone	none
danmazur.ca ↗	68 match 2 shared topics	Dan Mazur, PhD \| Personal website of Dan Mazur, PhD. Dan is a machine learning engineer in Vancouver, BC.	Canada en	artificial-intelligence	robotsllmsaihumans	emailphone	none
1chipml.org ↗	68 match 2 shared topics	GitHub - 1chipML/1chipML: a library for numerical crunching and machine learning on microcontrollers · GitHub	en	artificial-intelligence	robotsllmsaihumans	emailphone	none
minfx.ai ↗	67 match 2 shared topics	Minfx: Get to your machine learning goals faster - Minfx	en	artificial-intelligence	robotsllmsaihumans	emailphone	none
focustime.ai ↗	67 match 2 shared topics	Pulse joins the Mozilla family to help develop a new approach to machine learning	en	technology-and-computing Weebly	robotsllmsaihumans	emailphone	none
kevinchow.org ↗	67 match 2 shared topics	Kevin Chow's Blog – My journey of machine learning and data science.	en	artificial-intelligence WordPress	robotsllmsaihumans	emailphone	none
learning-from-data.com ↗	67 match 2 shared topics	Kernel Based Algorithms for Mining Huge Data Sets: Supervised, Semi-supervised, and Unsupervised Learning	en	artificial-intelligence	robotsllmsaihumans	emailphone	none

How the match score works

Each match is a 0–100 similarity score — the higher it is, the more two sites resemble one another. It’s computed automatically from our own crawl data (never from what a site says about itself) by combining several independent signals, so a high score means several of them point the same way:

shared topics how many of the same fine‑grained content topics (IAB sub‑categories) both sites cover — the main signal.
same category whether they sit in the same business / industry category.
content & title how closely their titles and page descriptions line up in wording and meaning.

No single signal decides the result — they’re blended together. Treat the score as a way to rank candidates rather than an absolute percentage; the chips on each result show which signals contributed.