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Fly to a Data Science Career : Machine Learning Tips | Fly to data science — 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
nafisatibrahim.com ↗	73 match 1 shared topics	Nafisat Ibrahim \| Data Scientist & Machine Learning Engineer	Canada~ en	artificial-intelligence	robotsllmsaihumans	emailphone	none
alpsanalytics.com ↗	72 match 1 shared topics	Leverage AI, machine learning, data science for better profitability!	en	artificial-intelligence WordPress	robotsllmsaihumans	emailphone	none
dch-it.com ↗	72 match 1 shared topics	dch IT - Data Science & Machine Learning	en	artificial-intelligence WordPress	robotsllmsaihumans	emailphone	none
bridgingai.com ↗	70 match 1 shared topics	Bridging Academia and Industry – Thoughts on Big Data and Machine Learning	en	artificial-intelligence WordPress	robotsllmsaihumans	emailphone	none
iagonunes.com.br ↗	70 match 1 shared topics	Iago Nunes - Machine Learning Engineer \| Data Scientist	Brazil en	artificial-intelligence	robotsllmsaihumans	emailphone	none
simplemachinelearning.com ↗	70 match 1 shared topics	Simple Machine Learning	en	artificial-intelligence	robotsllmsaihumans	emailphone	none
decodeaiml.com ↗	70 match 1 shared topics	Decode-AiML \| Your ultimate guide to landing roles like Applied Scientist, Data Scientist, and Machine Learning Engineer at MAANG and other top tech companies.	en	artificial-intelligence	robotsllmsaihumans	emailphone	none
deepdojo.com ↗	70 match 1 shared topics	The Deep Dojo Machine Learning Blog	en	artificial-intelligence	robotsllmsaihumans	emailphone	none
deepvisiondata.com ↗	70 match 1 shared topics	Synthetic Training Data for Machine Learning Systems \| Deep Vision Data	en	artificial-intelligence WordPress	robotsllmsaihumans	emailphone	none
deepvisiondata.ai ↗	70 match 1 shared topics	Synthetic Training Data for Machine Learning Systems \| Deep Vision Data	en	artificial-intelligence WordPress	robotsllmsaihumans	emailphone	none
bryanbosire.com ↗	70 match 1 shared topics	Bryan Bosire \| Machine Learning Engineer	en	artificial-intelligence	robotsllmsaihumans	email phone	none
ledarrow.dev ↗	69 match 1 shared topics	Lawson E. Darrow \| Machine Learning Engineer	en	artificial-intelligence	robotsllmsaihumans	emailphone	none
amaletix.com ↗	69 match 1 shared topics	Amaletix \| Data Science \| Analytics \| Artificial Intelligence \| Machine Learning \|	en	artificial-intelligence Wix	robots llmsaihumans	emailphone	none
crystsolovc.org ↗	69 match 1 shared topics	crystsolovc — AI, Machine Learning & DevOps Solutions	en	artificial-intelligence	robotsllmsaihumans	emailphone	none
aayushkumar.me ↗	69 match 1 shared topics	Aayush Kumar \| Machine Learning Engineer	en	artificial-intelligence	robotsllmsaihumans	emailphone	none
abhishekyadav.tech ↗	69 match 1 shared topics	Abhishek Paleru - Data Science Portfolio	en	artificial-intelligence	robotsllmsaihumans	emailphone	none
ddkweb.com ↗	69 match 1 shared topics	DDKweb \| DDKanalytics \| data science \| analytics \| simulation \| virtual \| machine learning \| Boston \| Newton MA	en	artificial-intelligence	robotsllmsaihumans	emailphone	none
bifold.berlin ↗	69 match 1 shared topics	BIFOLD Berlin - Big Data Management and Machine Learning	en	artificial-intelligence TYPO3	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.