Machine Learning – Telegram
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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📌 A Generalizable MARL-LP Approach for Scheduling in Logistics

🗂 Category: MACHINE LEARNING

🕒 Date: 2026-02-26 | ⏱️ Read time: 17 min read

Part 1. Hybrid Solution for Dynamic Vehicle Routing — Context and Architecture

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📌 Detecting and Editing Visual Objects with Gemini

🗂 Category: LLM APPLICATIONS

🕒 Date: 2026-02-26 | ⏱️ Read time: 34 min read

A practical guide to identifying, restoring, and transforming elements within your images

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📌 Designing Data and AI Systems That Hold Up in Production

🗂 Category: AUTHOR SPOTLIGHTS

🕒 Date: 2026-02-26 | ⏱️ Read time: 6 min read

A system-level perspective on architecture, agents, and responsible scale

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📌 Take a Deep Dive into Filtering in DAX

🗂 Category: DATA ANALYSIS

🕒 Date: 2026-02-26 | ⏱️ Read time: 13 min read

Have you ever wondered what happens when you apply a filter in a DAX expression?…

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📌 Coding the Pong Game from Scratch in Python

🗂 Category: PROGRAMMING

🕒 Date: 2026-02-27 | ⏱️ Read time: 18 min read

Implementing the classic Pong game in Python using OOP and Turtle

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📌 Stop Asking if a Model Is Interpretable

🗂 Category: ARTIFICIAL INTELLIGENCE

🕒 Date: 2026-02-27 | ⏱️ Read time: 6 min read

Start asking what question the explanation should answer.

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📌 Generative AI, Discriminative Human

🗂 Category: ARTIFICIAL INTELLIGENCE

🕒 Date: 2026-02-27 | ⏱️ Read time: 14 min read

How to think critically about AI in an ocean of hype

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📌 The Gap Between Junior and Senior Data Scientists Isn’t Code

🗂 Category: DATA SCIENCE

🕒 Date: 2026-02-27 | ⏱️ Read time: 6 min read

Why my obsession with complex algorithms was actually holding my career back.

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📌 Claude Skills and Subagents: Escaping the Prompt Engineering Hamster Wheel

🗂 Category: AGENTIC AI

🕒 Date: 2026-02-28 | ⏱️ Read time: 17 min read

How reusable, lazy-loaded instructions solve the context bloat problem in AI-assisted development.

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📌 Scaling ML Inference on Databricks: Liquid or Partitioned? Salted or Not?

🗂 Category: DATA ENGINEERING

🕒 Date: 2026-02-28 | ⏱️ Read time: 11 min read

A case study on techniques to maximize your clusters

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