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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📌 Implementing the Snake Game in Python

🗂 Category: PROGRAMMING

🕒 Date: 2026-02-10 | ⏱️ Read time: 17 min read

An easy step-by-step guide to building the snake game from scratch

#DataScience #AI #Python
📌 How to Personalize Claude Code

🗂 Category: LLM APPLICATIONS

🕒 Date: 2026-02-10 | ⏱️ Read time: 8 min read

Learn how to get more out of Claude code by giving it access to more…

#DataScience #AI #Python
🐱 5 of the Best GitHub Repos
🔃 for Data Scientists

👨🏻‍💻 When I was just starting out and trying to get into the "data" field, I had no one to guide me, nor did I know what exactly I should study. To be honest, I was confused for months and felt lost.

▶️ But doing projects was like water on fire and helped me a lot to build my skills.

Repo Awesome Data Analysis

🏷 A complete treasure trove of everything you need to start: SQL, Python, AI, data analysis, and more... In short, if you want to start from zero and strengthen your foundation, start here first.

                  


Repo Data Scientist Handbook

🏷 A concise handbook that tells you what you need to learn and what you can ignore for now.

                  


Repo Cookiecutter Data Science

🏷 A standard project template used by professionals. With this template, you can structure your data analysis and AI projects like a pro.

                  


Repo Data Science Cookie Cutter

🏷 This is also a very clean project template that teaches you how to build a data project that won’t fall apart tomorrow and can be easily updated. Meaning your projects will be useful in the real world from the start.

                  


Repo ML From Scratch

🏷 Here, the main AI algorithms are implemented from scratch in simple language. It’s great for understanding how models really work and for explaining them well in your interviews.

🌐 #Data_Science #DataScience
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📌 How to Model The Expected Value of Marketing Campaigns

🗂 Category: DATA SCIENCE

🕒 Date: 2026-02-10 | ⏱️ Read time: 9 min read

The approach that takes companies to the next level of data maturity

#DataScience #AI #Python
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📌 Not All RecSys Problems Are Created Equal

🗂 Category: MACHINE LEARNING

🕒 Date: 2026-02-11 | ⏱️ Read time: 9 min read

How baseline strength, churn, and subjectivity determine complexity

#DataScience #AI #Python
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📌 Building an AI Agent to Detect and Handle Anomalies in Time-Series Data

🗂 Category: AGENTIC AI

🕒 Date: 2026-02-11 | ⏱️ Read time: 13 min read

Combining statistical detection with agentic decision-making

#DataScience #AI #Python
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📌 AI in Multiple GPUs: Understanding the Host and Device Paradigm

🗂 Category: ARTIFICIAL INTELLIGENCE

🕒 Date: 2026-02-12 | ⏱️ Read time: 7 min read

Learn how CPU and GPUs interact in the host-device paradigm

#DataScience #AI #Python
📌 How to Leverage Explainable AI for Better Business Decisions

🗂 Category: ARTIFICIAL INTELLIGENCE

🕒 Date: 2026-02-12 | ⏱️ Read time: 10 min read

Moving beyond the black box to turn complex model outputs into actionable organizational strategies.

#DataScience #AI #Python
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📌 The Evolving Role of the ML Engineer

🗂 Category: AUTHOR SPOTLIGHTS

🕒 Date: 2026-02-13 | ⏱️ Read time: 5 min read

Stephanie Kirmer on the $200 billion investment bubble, how AI companies can rebuild trust, and…

#DataScience #AI #Python
📌 AI in Multiple GPUs: Point-to-Point and Collective Operations

🗂 Category: ARTIFICIAL INTELLIGENCE

🕒 Date: 2026-02-13 | ⏱️ Read time: 10 min read

Learn PyTorch distributed operations for multi GPU AI workloads

#DataScience #AI #Python
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