Machine Learning
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Machine learning insights, practical tutorials, and clear explanations for beginners and aspiring data scientists. Follow the channel for models, algorithms, coding guides, and real-world ML applications.

Admin: @HusseinSheikho || @Hussein_Sheikho
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πŸ“Œ How to Filter for Dates, Including or Excluding Future Dates, in Semantic Models

πŸ—‚ Category: DATA ANALYSIS

πŸ•’ Date: 2026-01-04 | ⏱️ Read time: 5 min read

It is common to have either planning data or the previous year’s data displayed beyond…

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πŸ“Œ Stop Blaming the Data: A Better Way to Handle Covariance Shift

πŸ—‚ Category: DATA SCIENCE

πŸ•’ Date: 2026-01-05 | ⏱️ Read time: 9 min read

Instead of using shift as an excuse for poor performance, use Inverse Probability Weighting to…

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πŸ“Œ YOLOv1 Loss Function Walkthrough: Regression for All

πŸ—‚ Category: ARTIFICIAL INTELLIGENCE

πŸ•’ Date: 2026-01-05 | ⏱️ Read time: 26 min read

An explanation of how YOLOv1 measures the correctness of its object detection and classification predictions

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πŸ“Œ How to Optimize Your AI Coding Agent Context

πŸ—‚ Category: PROGRAMMING

πŸ•’ Date: 2026-01-06 | ⏱️ Read time: 7 min read

Make your coding agents more efficient

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πŸ“Œ GliNER2: Extracting Structured Information from Text

πŸ—‚ Category: NATURAL LANGUAGE PROCESSING

πŸ•’ Date: 2026-01-06 | ⏱️ Read time: 11 min read

From unstructured text to structured Knowledge Graphs

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πŸ“Œ Feature Detection, Part 3: Harris Corner Detection

πŸ—‚ Category: MACHINE LEARNING

πŸ•’ Date: 2026-01-05 | ⏱️ Read time: 7 min read

Finding the most informative points in images

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πŸ“Œ Measuring What Matters with NeMo Agent Toolkit

πŸ—‚ Category: LLM APPLICATIONS

πŸ•’ Date: 2026-01-06 | ⏱️ Read time: 13 min read

A practical guide to observability, evaluations, and model comparisons

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πŸ“Œ The Best Data Scientists Are Always Learning

πŸ—‚ Category: DATA SCIENCE

πŸ•’ Date: 2026-01-06 | ⏱️ Read time: 10 min read

Part 2: Avoiding burnout, learning strategies and the superpower of solitude

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