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KDnuggets
Articles on AI, Analytics, Big Data, Data Mining, Data Science, and Machine Learning by Gregory Piatetsky-Shapiro and Matthew Mayo at KDnuggets. KDnuggets is a leading site on Data Science, Machine Learning, AI and Analytics. Edited by Matthew Mayo. KDnuggets was founded by Gregory Piatetsky-Shapiro. KD stands for Knowledge DiscoveryMORE
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Showing 51-100 of 212 entries
Save money & take control in 2026. Discover 5 powerful open-source, self-hosted tools to replace costly subscriptions for data scientists.
13 Mar
Out of 816 wines flagged by at least one method, just 32 made the unanimous list. Those wines had something in common.
This article presents five useful and effective Python decorators to build and optimize high performance data pipelines.
New to data science? Cut through the noise with the 2026 starter kit. Which Python, SQL, and machine learning essentials are important, and what can be ignored?
Looking to build autonomous AI agent systems? Here are the frameworks that will help you orchestrate agents effectively.
In this guide, you learn how to install and run PersonaPlex locally step by step, so you can experience real time, interruptible speech to speech AI directly on your own machine.
Discover five AI tools that make exploring and understanding large codebases faster and easier.
Learn how to install bitnet.cpp, download the BitNet b1.58 model, and run a fully local AI chat and inference server on your machine.
Check out a practical benchmark of three popular SQL databases using real-world analytical problems.
Interested in becoming an LLM engineer? Here's a list of Python libraries you'll find essential for your work.
Learn how Google Stax tests AI models and prompts against your own criteria. Compare Gemini vs GPT with custom evaluators. Step-by-step guide for beginners
Analyzing a set of objective facts about language models role and evolution, with some thoughts on the following question: are they the new commodity of the decade we can no longer live without?
Learn how people are turning AI tools into real income by building practical systems, selling outcomes, and creating niche products that businesses are willing to pay for.
Learn these five Python decorators based on diverse libraries, that take particular significance when used in the context of LLM-based applications.
Need help choosing the right Python dataframe library? This article compares Pandas and Polars to help you decide.
Want to move beyond drawing boxes and arrows and actually understand how scalable systems are built? These GitHub repositories break down the concepts, patterns, and real-world trade-offs that make great system design possible.
This article introduces and explores Kedro's main features, guiding you through its core concepts for a better understanding before diving deeper into this framework for addressing real data science projects.
Spending hours cleaning, summarizing, and visualizing your data manually? Automate your exploratory data analysis workflow with these 5 ready-to-use Python scripts.
Skills are what make OpenClaw more than a local assistant, and these are the most popular ones worth installing today.
This article guide you through an example use case to turn a PRD into a functioning software prototype using Google Antigravity.
An AI agent combines a large language model for reasoning, access to tools or APIs for action, memory to retain context and a control loop to decide what happens next.
3 Mar
Artificial intelligence is currently occupying the same mental space that "the cloud" did fifteen years ago, or the internet itself did twenty-five years ago.
Build faster Python applications by mastering async programming and learning how to handle I/O bound workloads efficiently with real world examples.
Great LLMs need great data. Discover the pipelines, tools, and RAG architecture shaping the future of AI-ready data engineering
This article explores five infrastructure patterns that make Docker a powerful foundation for building robust, autonomous AI applications.
Redefining data storytelling through interactive narratives, immersive environments, and alternative sensory techniques
OpenClaw is incredibly powerful, but if you install it without understanding these five things, you could expose far more than you expect.
Bad data leads to bad decisions. These Python scripts will help you catch data quality issues before they cause problems.
Data Lake vs Data Warehouse vs Lakehouse vs Data Mesh explained simply. Learn the key differences and which architecture fits your data needs
Most people are only using 10% of OpenClaw. These integrations unlock what it is truly capable of.
For most small- and medium-sized business leaders, the question about AI has shifted. While it used to be “Should we use AI?”, it’s now “Where should we run it?”
Leverage NotebookLM's features to turn raw, sometimes chaotic information into a grounded PRD in a matter of minutes.
These five libraries approach validation from very different angles, which is exactly why they matter. Each one solves a specific class of problems that appear again and again in modern data and machine learning workflows.
A conversation with AI researcher Sebastian Wallkötter reveals insights on standardization, security challenges, and the fundamental question facing enterprise artificial intelligence adoption.
Build robust AI agents with design patterns for ReAct loops, multi-agent workflows, and state management essential for moving from prototype to reliable production.
This is the ultimate guide to uploading, downloading, and saving files in Colab.
7 Python tricks that may help make the most of the standalone XGBoost library, particularly in terms of seeking more accurate predictive models.
Learn how to build MCP servers and clients using FastMCP, which is comprehensive, complete with error handling, best practices, and deployment strategies, making it ideal for both beginners and intermediate developers.
ADK from Google addresses a critical gap in the agentic AI ecosystem by providing a framework that simplifies the construction and deployment of multi-agent systems. Learn more.
Want a smaller, faster, more secure agent stack than OpenClaw?
8 Python tricks to turn raw, messy data into clean, neatly preprocessed data with minimal effort.
Code reviews shouldn’t be a bottleneck. The best AI code review tools now catch bugs, anti-patterns, security flaws, and more in seconds before they ever hit production.
Before you commit, ask these 3 essential questions about vendor lock-in, hidden costs, and tool integration to protect your business future.
Check out this practical 2026 guide to Hugging Face. Explore transformers, datasets, sentiment analysis, APIs, fine-tuning, and deployment with Python.
This article covers the top seven Python libraries for implementing progress bars, with practical examples to help you quickly add progress tracking to data processing, machine learning, and automation workflows.
16 Feb
This article gently introduces feature stores, describing their origins, main characteristics, reasons for their current significance, and popular tools at present.
From February 16–22, DataCamp’s entire curriculum is 100% free.
Build your own private AI hub with Docker, Ollama, and n8n. A beginner's guide to self-hosted, local automation with no cloud fees.
Fast providers offering open source LLMs are breaking past previous speed limits, delivering low latency and strong performance that make them suitable for real time interaction, long running coding tasks, and production SaaS applications.