A large open community where software developers publish tutorials, post-mortems and career posts. Good for practical write-ups on tools and frameworks, and for publishing your own work in public where hiring managers can find it.
Why I recommend it: Free to read and free to post, no subscription. Quality varies a lot because anyone can publish, so check dates and test code before relying on it.
Jason Brownlee's long-running tutorial site: hundreds of free, step-by-step machine learning walkthroughs in Python, organised into 'start here' guides by topic — getting set up, understanding algorithms, your first complete project, your first neural network, time series forecasting. Each tutorial is written to get you to a working result rather than a theory exam.
Why I recommend it: The tutorials and the 'start here' guides are free to read with no account. The site's business is paid ebooks, and the free ebook offer costs you an email address and an ongoing email course, so expect the marketing. A fair criticism to know going in: the tutorials are recipe-shaped, which gets you running code fast but can leave the why thin — pair them with something that explains the ideas.
Long-running free publication on data science, machine learning and AI, mixing daily tutorials with roundups of tools and techniques. Free to read, supported by ads and affiliate links.
Why I recommend it: Strongest on hands-on tutorials, weakest on product roundups — several posts carry affiliate links to courses, so treat recommendations as suggestions rather than independent verdicts.
Well-explained computer science and programming tutorials, quizzes, and interview practice problems.
From the site: Your All-in-One Learning Portal. It contains well written, well thought and well explained computer science and programming articles, quizzes and practice/competitive programming/company interview Questions.
Why I recommend it: Ad-supported; most articles and practice problems are free to read.
Free, well-written documentation and tutorials for building and hosting sites, APIs and AI workers — one of the better free places to learn modern web infrastructure.
From the site: Connect, protect, and build everywhere.
Why I recommend it: Free docs with working examples. A good self-teaching path if you want infrastructure skills on your resume.
A large free archive of practitioner-written tutorials and explainers on machine learning, statistics, data engineering and AI, from beginner to advanced.
Why I recommend it: Quality varies by author, but the beginner explainers are among the easiest free routes into data work.
Free, MIT-licensed Python library and documentation for applying AI to satellite and geospatial data, with tutorials, notebooks, a QGIS plugin and video walkthroughs.
From the site: A Python package for using Artificial Intelligence (AI) with geospatial data
Why I recommend it: A free, well-documented open-source project — a good portfolio path if you want to work in mapping, climate or remote sensing.
Jeff Su's site collects his practical AI and productivity tips for working professionals — templates, workflows and short guides drawn from his popular videos.
Beginner-friendly electronics tutorials from engineer and YouTube educator AfroTechMods, covering transistors, op-amps, soldering, and circuit debugging in plain language.
Why I recommend it: Great first stop if formal engineering courses lost you. Build one circuit, then go back to the theory.
Five-step walkthrough from Innovating with AI on creating a reusable Claude Skill so an assistant writes and works in your voice.
Why I recommend it: Details: build one skill around a task you repeat weekly — cover letters, client recaps, outreach — and you will feel the payoff immediately.
Free, hands-on AI tutorials and automation walkthroughs for people who want to actually build with the tools.
In plain terms: Free AI tutorials and automation guides written for practitioners rather than theorists. Good for learning tools you can use in your job this week.
From the site: Free AI tutorials, prompts, and automation walkthroughs.
Why I recommend it: Her tutorials are step-by-step, not hype. Pick one workflow you repeat weekly and automate it.
