Welcome to the Machine Learning Hub, your one-stop destination for all things related to machine learning!
Get ready to embark on an exciting journey into the realm of AI and discover how machines can learn and make intelligent decisions. Our blog articles are crafted with simplicity and clarity in mind, making complex machine learning concepts easy to understand for everyone. Whether you’re a beginner or an experienced practitioner, we’ve got you covered with informative and insightful content. Explore the fascinating world of algorithms, models, and data as we delve into supervised and unsupervised learning, reinforcement learning, and more. Discover practical applications in various domains like healthcare, finance, and autonomous vehicles. From introductory guides to advanced techniques, we’re here to help you demystify machine learning and unlock its potential. Join us on this journey as we unravel the secrets of machine learning and empower you to build intelligent systems that can analyze data, make predictions, and drive innovation.
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The European Commission’s latest draft guidelines provide much-needed clarity on how organizations should classify high-risk AI systems under Article 6 of the EU AI Act. However, they also raise an important question for enterprises: could your existing AI systems already be considered high-risk without you realizing it? The answer may depend on more …
Could Your AI Systems Already Be High-Risk Under the EU AI Act? Read More »
, I will give my first impressions of the newest OpenAI model, GPT-5.6. The model was released a few days ago, and I’ve gotten the chance to test it extensively since the release and compare it against other models such as GPT-5.5, Opus 4.8, and Fable 5. I’ll give my judgment on the model, share …
# Introduction Everyone is building agents. Far fewer people can explain, precisely, why their agent loops forever, ignores a tool it was given, or confidently reports success on a task it never finished. The gap between shipping an agent and understanding one is where these five resources live, and every one of them is completely …
a marketing mix model to a senior director. The slide showed two beta coefficients side by side: Linear TV at +2.4, Digital TV at +1.8. He nodded, satisfied. Then he asked the question I was dreading. “Let’s assume we run this with last week’s refreshed data. Same channels, same model, same everything, but one extra …
Why Your Betas Explode: The Hidden Geometry of Multicollinearity Read More »
# Introduction An agent that calls a cloud API for every decision is renting its intelligence. It needs a key, it racks up a bill per token, and every request leaves your machine before you get an answer back. An agent built to run locally skips all of that. No API key, no per-call cost …
7 Python Frameworks for Orchestrating Local AI Agents Read More »
In my latest post on structured outputs, the three main approaches for getting machine-readable responses from an LLM. Those are JSON Mode, Function Calling, and OpenAI’s Structured Outputs. If you haven’t read that post yet, it’s worth a quick read before this one, since we’ll be building directly on top of it. So, today, we’ll …
Pydantic + OpenAI: The Cleanest Way to Get Structured Outputs from LLMs Read More »
# Introduction SQL is still one of the most important skills for data analysts, data scientists, business intelligence analysts, and analytics engineers. But learning SQL syntax is only the first step. To stand out, you need to show that you can use SQL to solve real business problems. That is where portfolio projects help. A …
5 Real-World SQL Projects to Build Your Data Portfolio Read More »
I’ve worked on, conversation state tends to grow quickly over time. It’s common to resend large portions of the history on each turn—including older tool outputs, repeated RAG retrievals, and context that’s no longer relevant. As this accumulates, prompts can become significantly larger, which may increase inference cost and latency, and in some cases affect …
# Inroduction This article is part of my noob series where we write about the questions people Google most but may not understand well because of complex math and everything. So, if you are here, you might have heard fine-tuning somewhere in the context of large language models (LLMs) especially. This concept already existed in …
Fine-Tuning Explained for Noobs (How Pretrained Models Learn New Skills) Read More »
latest wave of articles, opinion pieces, and forecasts from consultants and AI vendors, all confidently predicting the imminent takeover of human work by agentic AI, I recently found myself thinking about my first year at Imperial College London, back in 2009. Our lecturer for the introductory programming course would often tell us to sketch out …
