AI Tools & Frameworks Directory

Explore five free Microsoft GitHub courses covering data science, machine learning, artificial intelligence, generative AI, LLMs, RAG, fine-tuning, and AI agents.

In this article, you will learn how to treat prompt templates as tunable hyperparameters for a language model, using scikit-learn's grid search to find the...

A year ago, terminal AI mostly meant asking a model to explain errors, generate commands, or edit small functions. In 2026, leading coding CLIs have become full agent runtimes that can inspect repositories, plan work, modify files, run tests, use external tools, and verify results.  In this article,...

Before reaching for an LLM API on every classification problem, it's worth knowing what a decades-old baseline can already do with the labeled data you have — and exactly how much more data buys you. The post How Many Labeled Examples Does a Text Classifier Actually Need? I Measured It. appeared fir...

First in a series on probabilistic forecasting for physical signals. Next: what happens when you roll the forecast forward more than one step. The post Your Model’s MSE Is Lying to You appeared first on Towards Data Science.

When Codex is the right shape for the problem, when Claude Code is, and how I split 5 specialist agents between them on dense AI capacity work. The post When to Use One Model and When to Use a Team of Agents appeared first on Towards Data Science.

In this article, you will learn what model distillation is, how it has evolved for large language models, and why it has become one of...

A viral debate over loops versus graphs points to a bigger shift in how we build AI systems. Here’s what graph engineering actually means, how it differs from prompt, context, and loop engineering, and why it matters. The post Graph Engineering for AI Agents: From Prompts and Loops to Workflows appe...

Why the skill-inflation panic is aimed at the wrong thing, and what it costs to make agent knowledge a build artifact instead of a file. The post From Static to Dynamic Skills: A Different Model for Agent Knowledge appeared first on Towards Data Science.

Building a FastAPI endpoint for churn prediction, and everything that broke between "it runs" and "it's live The post Your Model Isn't Done Until Someone Else Can Call It appeared first on Towards Data Science.

A practitioner's guide to estimating what an opt-in AI feature actually did, when nobody randomized it. The post Your AI Adoption Lift Is a Selection Effect appeared first on Towards Data Science.

A real Weave project that regression-tests three OpenAI models against the exact reply format your app depends on. The post One Capital Letter Was Silently Breaking My AI Support Bot, and It Wasn't in the New Model appeared first on Towards Data Science.

OpenAI has released ChatGPT Images 2.5, its latest image-generation model, with a greater emphasis on controlled editing than simply producing prettier images. The update promises sharper details, more natural lighting and textures, stronger reference-image preservation, more reliable multi-turn edi...

I built a system that automatically discovers, verifies, and applies relevant requirements from earlier interactions without asking the user where they came from. The post Coding Agents Don't Need Longer History — They Need Intent Continuity appeared first on Towards Data Science.

This article explains 5 Python techniques for efficient resource orchestration and sticks to what's stable today, 3.11 and later for the core techniques, with one 3.14-specific tool called out explicitly as requiring that version

Once feature engineering lives inside a Pipeline, each step is fitted on training data only, and the model is scored what it actually earned. And that is the idea behind this new cheat sheet.

Why autonomous agents expose a new explainability problem in fraud detection The post What SHAP Can't Explain About Agentic AI Fraud appeared first on Towards Data Science.

Moving from static model assignment to intelligent, task-level LLM selection. The post Optimizing LLM Inference Costs in Multi-Agent Systems with Adaptive Model Routing appeared first on Towards Data Science.

Automate common CSV tasks with these 5 Python scripts for cleaning, validating, transforming, and processing CSV files using the standard library.

A model is only as reliable as the assumptions behind it The post Who Questions What Works: When Should We Retest Our Assumptions? appeared first on Towards Data Science.

A Hindi lesson can mix English terms (loan words), scanned tables and handwritten equations. Making that content searchable, translating it and reading it aloud requires several kinds of AI. Bodhan AI and AI4Bharat’s four new models target those jobs across Indian languages.  Released in September 2...

A practical guide to building, testing, and documenting SQL transformations The post Getting started with dbt appeared first on Towards Data Science.

A six-stage pipeline that checks its numbers before calling anything an answer.

Understanding permutation symmetry in deep learning, and what it means for weight averaging and model merging The post The Symmetry That Breaks Neural Network Averaging appeared first on Towards Data Science.