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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 151-200 of 212 entries
Large open-source language models are now widely accessible, and this article compares leading AI API providers on performance, pricing, latency, and real-world reliability to help you choose the right option.
Google Antigravity marks the beginning of the "agent-first" era, It isn't just a Copilot, it’s a platform where you stop being the typist and start being the architect.
This list focuses on tools that streamline real workflows across data, operations, and content, not flashy demos or brittle bots. Each one earns its place by reducing manual effort while keeping humans in the loop where it actually matters.
Avoiding Overfitting, Class Imbalance, & Feature Scaling Issues: The Machine Learning Practitioner’s Notebook
14 Jan
Machine learning practitioners encounter three persistent challenges that can undermine model performance: overfitting, class imbalance, and feature scaling issues.
A quick guide to the best code sandboxes for AI agents, so your LLM can build, test, and debug safely without touching your production infrastructure.
Delegating without chaos is the difference between a business that plateaus and one that scales.
Stop using print statements and start logging like a pro. This guide shows Python developers how to log smarter and debug faster.
Same data, different formats, very different performance.
Feature engineering doesn’t have to be complex. These 5 Python scripts help you create meaningful features that improve model performance.
We tested five imputation methods with proper cross-validation and statistical testing. Mean imputation won for prediction but destroyed feature relationships.
This tutorial will guide you through the complete process of self-hosting n8n on Docker in just 5 simple steps, with detailed explanations and code samples, regardless of your technical background.
My go-to tech stack that helps me code faster, stay organized, and ship with confidence.
Tired of repetitive data cleaning tasks? This article covers five Python scripts that handle common data cleaning tasks efficiently and reliably.
The ultimate goal is to run these automations on a single workstation or small server, replacing fragile scripts and expensive API-based systems.
The most popular GitHub repositories to help you learn AI, from fundamentals and math to LLMs, agents, computer vision, and real-world production systems.
This is an "expectations vs reality" approach to demystify, based on research of real success and failure stories, what are the capabilities and limits of vibe coding.
Although data science and AI engineering share tools and terminology, they are not interchangeable careers. This article explains how the work, goals, and impact of each role differ so you can choose the career path that fits you.
A list of ready to use n8n workflow templates that help data scientists quickly analyze data, extract and transform it, and build reliable knowledge bases.
Choosing a cloud services provider can feel a lot like dating: every vendor promises reliability, security, and support, but only a few truly live up to it. The wrong choice can lead to costly downtime, security headaches, or performance bottlenecks that …
In this article, we retroactively analyze what I would consider the ten most consequential, broadly impactful AI storylines of 2025, and gain insight into where the field is going in 2026.
Build ML web apps in minutes with Gradio's Python framework. Create interactive demos for models with text, image, or audio inputs with no frontend skills needed. Deploy and share instantly.
Building data pipelines? These Python ETL tools will make your life easier.
Which of these state-of-the-art models writes the best code?
Long-running LLM applications degrade when context is unmanaged. Context engineering turns the context window into a deliberate, optimized resource. Learn more in this article.
Read these 6 tricks for treating your Docker container like a reproducible artifact, not a disposable wrapper.
Want to level up your data science toolkit? Here are some Python libraries that'll make your work easier.
Make extra money between classes with beginner-friendly freelance platforms that fit your lifestyle.
A quick look at the top 7 agentic AI browsers that can search the web for you, fill forms automatically, handle research, draft content, and streamline your entire workflow.
Learn Docker by doing with five beginner-friendly projects covering hosting, multi-container apps, CI, and monitoring.
24 Dec
Best OCR and vision language models you can run locally that transform documents, tables, and diagrams into flawless markdown copies with benchmark-crushing accuracy.
How can we reason with uncertainty and make smarter decisions from data? This article explains the key probability ideas in data science.
Looking ahead to 2026, the most impactful trends are not flashy frameworks but structural changes in how data pipelines are designed, owned, and operated.
This article explains how Gistr transforms the way data professionals interact with their most valuable asset: their accumulated knowledge.
This is a list of top LLM and VLMs that are fast, smart, and small enough to run locally on devices as small as a Raspberry Pi or even a smart fridge.
Spending too much time on repetitive tasks? These Python scripts will help you automate the mundane stuff that drains your productivity.
Prompt engineering is not just about asking models the right questions — it is about structuring those questions to think like a data auditor. When used correctly, it can make quality assurance faster, smarter, and far more adaptable than traditiona…
Learn how to run your own language model for free using lightweight models and Hugging Face Spaces.
Take a tour of 5 of the most popular AI-powered app builders out there to leverage automation in the process of building software.
The overarching goal is to maximize the return on analytical talent, shifting their focus entirely from data preparation to predictive model development, which is a necessary move if the business intends to compete in an AI-driven economy.
Build a lightweight Python DSL to define and check data quality rules in a clear, expressive way. Turn complex validation logic into simple, reusable configurations that anyone on your data team can understand.
It’s not about clever wording anymore. It’s about designing environments where AI can think with depth, consistency, and purpose.
In this article, we overview five cutting-edge MLOps trends that will shape 2026.
Set up, build, and test agentic apps with Claude Code, powered by your locally installed Claude CLI and Claude Code subscription.
These five configurations can turn your Docker setup from a slow chore into a finely tuned machine.
The point is this: those who learn to collaborate with AI rather than fear it will hold the keys to tomorrow’s job market.
These are the essentials that help me code faster, analyze data smarter, and automate more of my workflow.
Understanding the underlying technology helps explain why AI browsers exhibit such uneven performance.
This article transforms the unwelcome experiences into five comprehensive frameworks that will elevate your Excel-based machine learning work.
25 Nov
Don’t Miss DataCamp’s Black Friday Deal: Nov 12-Dec 4
In today’s data-saturated world, simply being “data-driven” isn’t enough. The most successful organizations are those that translate data, analytics, and AI into measurable business outcomes—creating real value for customers and shareholders alike.