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 Modeling Urban Walking Risk Using Spatial-Temporal Machine Learning

πŸ—‚ Category: MACHINE LEARNING

πŸ•’ Date: 2026-01-28 | ⏱️ Read time: 12 min read

Estimating neighborhood-level pedestrian risk from real-world incident data

#DataScience #AI #Python
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πŸ“Œ Federated Learning, Part 2: Implementation with the Flower Framework

πŸ—‚ Category: FEDERATED LEARNING

πŸ•’ Date: 2026-01-28 | ⏱️ Read time: 11 min read

Implementing cross-silo federated learning step by step

#DataScience #AI #Python
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πŸ“Œ Machine Learning in Production? What This Really Means

πŸ—‚ Category: MACHINE LEARNING

πŸ•’ Date: 2026-01-28 | ⏱️ Read time: 10 min read

From notebooks to real-world systems

#DataScience #AI #Python
πŸ“Œ Optimizing Vector Search: Why You Should Flatten Structured Data

πŸ—‚ Category: MACHINE LEARNING

πŸ•’ Date: 2026-01-29 | ⏱️ Read time: 7 min read

An analysis of how flattening structured data can boost precision and recall by up to 20%

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πŸ“Œ RoPE, Clearly Explained

πŸ—‚ Category: LARGE LANGUAGE MODELS

πŸ•’ Date: 2026-01-29 | ⏱️ Read time: 8 min read

Going beyond the math to build intuition

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πŸ“Œ The Unbearable Lightness of Coding

πŸ—‚ Category: LLM APPLICATIONS

πŸ•’ Date: 2026-01-29 | ⏱️ Read time: 9 min read

Confessions of a vibe coder

#DataScience #AI #Python
πŸ“Œ Randomization Works in Experiments, Even Without Balance

πŸ—‚ Category: DATA SCIENCE

πŸ•’ Date: 2026-01-29 | ⏱️ Read time: 10 min read

Randomization usually balances confounders in experiments, but what happens when it doesn’t?

#DataScience #AI #Python
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πŸ“Œ Creating an Etch A Sketch App Using Python and Turtle

πŸ—‚ Category: PROGRAMMING

πŸ•’ Date: 2026-01-30 | ⏱️ Read time: 7 min read

A beginner-friendly Python tutorial

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πŸ“Œ Why Your Multi-Agent System is Failing: Escaping the 17x Error Trap of the β€œBag of Agents”

πŸ—‚ Category: AGENTIC AI

πŸ•’ Date: 2026-01-30 | ⏱️ Read time: 27 min read

Hard-won lessons on how to scale agentic systems without scaling the chaos, including a taxonomy…

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πŸ“Œ On the Possibility of Small Networks for Physics-Informed Learning

πŸ—‚ Category: MACHINE LEARNING

πŸ•’ Date: 2026-01-30 | ⏱️ Read time: 20 min read

A new kind of hyperparameter study

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πŸ“Œ Multi-Attribute Decision Matrices, Done Right

πŸ—‚ Category: DATA SCIENCE

πŸ•’ Date: 2026-01-30 | ⏱️ Read time: 7 min read

How to structure decisions, identify efficient options, and avoid misleading value metrics

#DataScience #AI #Python
πŸ“Œ How to Run Claude Code for Free with Local and Cloud Models from Ollama

πŸ—‚ Category: PROGRAMMING

πŸ•’ Date: 2026-01-31 | ⏱️ Read time: 16 min read

Ollama now offers Anthropic API compatibility

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