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Data Science Tutorials

Learn data science and AI with step-by-step tutorials on the DataCamp blog. Master Python, SQL, machine learning, and build your own AI agents.
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GPT-Live-1 API Tutorial: Build a Full-Duplex Voice Assistant

Follow this GPT-Live-1 API tutorial to build a full-duplex voice learning assistant with browser WebRTC, backend delegation, web search, and confirmed actions.
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Khalid Abdelaty

September 15, 2026

What Are AI World Models? How They Work and 2026 Trends

Discover what AI world models are, how they differ from large language models, and how they help AI predict the future. Explore the latest 2026 industry trends.
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Vaibhav Mehra

September 14, 2026

Causal Machine Learning: From Prediction to Cause and Effect

Causal machine learning combines ML with causal inference to move beyond prediction and estimate what happens when you intervene. This article covers the core concepts, methods, Python tools, and common mistakes.
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Dario Radečić

September 14, 2026

State Space Models: How They Work and Where They're Used

Learn how state space models represent dynamic systems using hidden states and observations, including the state and observation equations, Kalman filtering, and time-series applications.
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Vinod Chugani

September 14, 2026

Code Review With Claude Code: Catch Bugs Before They Reach Production

A practical guide to reviewing Python data science pull requests with Claude Code, GitHub, and ultrareview.
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Tim Lu

September 14, 2026

LangChain4j Tool Memory Tutorial: Build a Java Agent With Oracle AI Database

Learn how to build a simple LangChain4j agent that can remember important notes across conversations.
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Anders Swanson

September 13, 2026

LMCache Tutorial: Build a Scalable KV Cache Layer for LLM Inference

Learn how LMCache stores and reuses KV states across LLM requests, integrates with vLLM, and reduces repeated prefill computation for long-context inference.
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Abid Ali Awan

September 13, 2026

Residual Plots Explained: How to Check Regression Model Assumptions

Learn what a residual plot is, how to interpret common patterns, and how residual plots help identify nonlinearity, heteroscedasticity, outliers, and other regression problems.
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Vinod Chugani

September 10, 2026

Saddle Points in Optimization: What They Are and Why They Matter

A practical, math-first look at saddle points - what makes them different from local minima and maxima, how to spot one using gradients and the Hessian, and why they show up so often in high-dimensional optimization and neural network training.
Dario Radečić's photo

Dario Radečić

September 10, 2026

Augmented Matrix Explained: How to Solve Systems of Equations

Learn what an augmented matrix is, how it represents systems of equations, and how to use row operations to solve them.
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Iheb Gafsi

September 10, 2026

How to Reduce Token Usage in AI Coding Agents: 4 Tools That Can Help

Reduce token usage by cutting context bloat, terminal noise, verbose responses, and over-engineered code with lightweight tools that optimize coding-agent workflows automatically.
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Abid Ali Awan

September 10, 2026