Resource Hub
3505 hand-picked resources for students, job seekers, career changers, founders, and AI learners — 100% free to browse with no account required.
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58 resources
A DeepLearning.AI course built with Prior Labs, about 1.5 hours at the intermediate level, on using tabular foundation models such as TabPFN for prediction on spreadsheet-style data and connecting them to data science agents. Enrollment requires a free account.
RudderStack's free library of explainer articles on data engineering, data infrastructure and machine learning, covering topics such as generalization, deep learning and supplying customer data to LLM applications. RudderStack sells a customer data platform.
An interactive report showing small transformer networks whose weights were written directly by an AI agent, with no training, to solve toy tasks such as bit-string parity. Every weight matrix is displayed, and code, run logs and saved programs are published on GitHub.
A practice platform for implementing machine learning algorithms from scratch in Python, NumPy, PyTorch, CUDA and Triton, with math explanations, visualizations and research-paper implementations. A free tier covers core problems and the ML math library; premium plans add study roadmaps and more.
Archive of Break Through Tech's AI Studio challenge projects, where university students work in teams on real machine learning problems set by partner companies.
A free job board focused on AI, machine learning, data science, and data engineering roles, with company listings and a career insights blog. Job seekers can browse and search openings at no cost; employers pay to post roles.
Personal site of A. Feder Cooper, a researcher working on the intersection of machine learning and law, including work on copyright and generative AI, data privacy and ML systems. Lists publications, talks and writing.
Hands-on tutorial (Sept. 2026) showing how to spot when the text data a live AI system sees starts to shift, using a domain classifier and centroid distance.
Why I recommend it: Free tutorial. Assumes you know Python and basic machine learning.
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.
The free research library and blog of Snorkel AI, the company spun out of Stanford's Snorkel project on programmatic labelling. The papers and posts explain how training data for AI models is actually built — labelling, evaluation sets, and the 'environments' used to train agents. Useful if you want to understand the unglamorous data work behind model quality, which is where a lot of the real jobs are.
Why I recommend it: Research and blog posts are free to read with no signup. Read it for the how, not the verdict: Snorkel sells data services to frontier AI labs, so posts arguing that better data beats bigger models are also a sales case. Everything else on the site is a paid enterprise product — 'request dataset samples' means a sales call.
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.
Free programs for undergraduate students pursuing tech careers, with a current focus on AI, data science and machine learning pathways. Includes fellowships and work-based learning run with university and industry partners.
Why I recommend it: No cost to students, but programs are application-based and tied to specific universities and cohort dates. Check the programs page for who is eligible before you plan around it.
A platform for data science competitions, public datasets, and collaborative notebooks to practice machine learning.
Why I recommend it: Competitions, public datasets, and notebooks are free; heavy compute or private datasets need a paid plan.
A breakdown of ten new AI job titles — from agent orchestration to evaluations specialists — with what each role actually does and what hiring managers screen for.
From the site: Discover the 10 fastest-growing AI roles in 2026. Learn who to hire for ethics, UX, prompt engineering, and more. Stay ahead with AI talent.
Why I recommend it: Written to help companies hire, which makes it a clear read on the titles and skills being asked for right now if you are aiming at AI work.
Personal site of Andrew Ng, co-founder of Coursera and DeepLearning.AI and a founding lead of Google Brain, collecting his courses, writing and current projects.
From the site: Andrew Ng has helped millions of people learn AI. Founder of DeepLearning.AI, AI Fund, and LandingAI. Co-Founder of Coursera. Board Director at Amazon.
Why I recommend it: His teaching is the cheapest serious on-ramp into machine learning that exists. Start from the courses list rather than the news.
The 2016 paper that framed AI safety as a set of specific engineering problems — side effects, reward hacking, unsafe exploration — rather than a philosophical worry. Free on arXiv.
From the site: Rapid progress in machine learning and artificial intelligence (AI) has brought increasing attention to the potential impacts of AI technologies on society. In this paper we discuss one such potential impact: the problem of accidents in machine learning systems, defined as unintended and harmful behavior that may emer…
Why I recommend it: Start here if the safety conversation sounds abstract. It is plain about what can go wrong and why, and almost everything since cites it.
Yann LeCun's position paper arguing that today's language models are the wrong architecture, and sketching what he thinks should replace them. Free to read.
Why I recommend it: The serious technical case against scaling language models further. Dense, but it is the argument itself rather than a summary of it.
Xiaomi's MiMo family of open language and reasoning models, with weights and technical reports published free for anyone to download and run.
Why I recommend it: Free open weights — worth knowing about if you want to run a capable model yourself.
A free arXiv preprint on recursive self-improvement in AI agents trained inside evolving simulated worlds.
Why I recommend it: Technical, and central to the safety debate about systems that improve themselves.
A free explorer for new arXiv research with plain-language paper summaries, topic pages and video overviews, so you can follow AI research without reading raw papers.
From the site: Your first stop to discover and learn about new arXiv research. Detailed paper summaries, video overviews, and more — no prompting required.
Why I recommend it: The fastest way I know to keep up with AI research when you are not a researcher. Free to browse.
A plain-language explainer on RLCD, a way of aligning language models by learning from contrasting outputs rather than human ratings alone.
From the site: RLCD is a method developed to adjust language models to human preferences without using human feedback data. This approach aims to address…
Why I recommend it: Good background reading if you want to understand how the models you use are actually steered.
A free ebook walking through reinforcement learning from the basics to RLHF, written for practitioners rather than researchers.
From the site: Reinforcement learning (RL) is transforming how reliable AI agents are trained and deployed. Discover real-world use cases, efficiency techniques like LoRA, and practical patterns you can apply today.
Why I recommend it: Free download in exchange for an email address. Solid grounding if you keep seeing "RLHF" and nodding along.
The standard free Python distribution for data and AI work — package management, notebooks and thousands of libraries in one install.
From the site: Anaconda is the trusted foundation for AI-native development. Secure, orchestrate, and accelerate data and AI at scale, from first experiment to production.
Why I recommend it: Free for individual use. If you are learning Python for data work, this saves you a week of setup pain.
A community blog where researchers publish and debate technical AI alignment work, free and open to read.
From the site: A community blog devoted to technical AI alignment research
Why I recommend it: Dense reading, but this is where a lot of safety research is argued out in public before it reaches papers.
