Machine Learning

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Real Machine Learning — simple, practical, and built on experience. Learn step by step with clear explanations and working code. Admin: @HusseinSheikho || @Hussein_Sheikho

Network:Machine Learning with Python Syria232 Technologies & Applications3 402...

📈 Analytical overview of Telegram channel Machine Learning

Channel Machine Learning (@machinelearning9) in the English language segment is an active participant. Currently, the community unites 40 057 subscribers, ranking 3 402 in the Technologies & Applications category and 232 in the Syria region.

📊 Audience metrics and dynamics

Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 40 057 subscribers.

According to the latest data from 22 June, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 372 over the last 30 days and by 2 over the last 24 hours, overall reach remains high.

Verification status: Not verified
Engagement rate (ER): The average audience engagement rate is 1.94%. Within the first 24 hours after publication, content typically collects 1.16% reactions from the total number of subscribers.
Post reach: On average, each post receives 775 views. Within the first day, a publication typically gains 466 views.
Reactions and interaction: The audience actively supports content: the average number of reactions per post is 3.
Thematic interests: Content is focused on key topics such as distance, insidead, gpu, learning, degree.

📝 Description and content policy

The author describes the resource as a platform for expressing subjective opinions:
“Real Machine Learning — simple, practical, and built on experience. Learn step by step with clear explanations and working code. Admin: @HusseinSheikho || @Hussein_Sheikho”

Thanks to the high frequency of updates (latest data received on 23 June, 2026), the channel maintains relevance and a high level of publication reach. Analytics show that the audience actively interacts with content, making it an important point of influence in the Technologies & Applications category.

40 057

Subscribers

+224 hours

+237 days

+37230 days

775

Post views

~ 46624 hours

~ 62048 hours

1.94%

Engagement rate

~ 3

Posts per day

Ads index

beta

Posts Archive

40 070

📌 Building Robust Credit Scoring Models (Part 3) 🗂 Category: MACHINE LEARNING 🕒 Date: 2026-03-20 | ⏱️ Read time: 18 min read Handling outliers and missing values in borrower data using Python. #DataScience #AI #Python

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📌 The Basics of Vibe Engineering 🗂 Category: AGENTIC AI 🕒 Date: 2026-03-19 | ⏱️ Read time: 14 min read Building products without the coding part #DataScience #AI #Python

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📌 Vibe Coding with AI: Best Practices for Human-AI Collaboration in Software Development 🗂 Category: AGENTIC AI 🕒 Date: 2026-03-19 | ⏱️ Read time: 16 min read Accelerate coding with AI while staying in control and building reliable, production-ready software. #DataScience #AI #Python

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📌 Linear Regression Is Actually a Projection Problem, Part 1: The Geometric Intuition 🗂 Category: DATA SCIENCE 🕒 Date: 2026-03-19 | ⏱️ Read time: 14 min read A visual guide to vectors and projections #DataScience #AI #Python

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📌 Beyond Prompt Caching: 5 More Things You Should Cache in RAG Pipelines 🗂 Category: AGENTIC AI 🕒 Date: 2026-03-19 | ⏱️ Read time: 13 min read A practical guide to caching layers across the RAG pipeline, from query embeddings to full… #DataScience #AI #Python

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Repost from Machine Learning with Python

PhD Students - Do you need datasets for your research? Here are 30 datasets for research from NexData. Use discount code for 20% off: G5W924C3ZI 1. Korean Exam Question Dataset for AI Training https://lnkd.in/d_paSwt7 2. Multilingual Grammar Correction Dataset https://lnkd.in/dV43iqTp 3. High quality video caption dataset https://lnkd.in/dY9kxkhx 4. 3D models and scenes datasets for AI and simulation https://lnkd.in/dT-zscH4 5. Image editing datasets – object removal, addition & modification https://lnkd.in/dd8iCGMS 6. QA dataset – visual & text reasoning https://lnkd.in/dc3TNWFD 7. English instruction tuning dataset https://lnkd.in/dTeTgd2M 8. Large scale vision language dataset for AI training https://lnkd.in/dBJuxazN 9. News dataset https://lnkd.in/dYBJe5gd 10. Global building photos dataset https://lnkd.in/dVJsDXnC 11. Facial landmarks dataset https://lnkd.in/dz_KGCS4 12. 3D Human Pose & Landmarks dataset https://lnkd.in/dXE9ir8Z 13. 3D Hand Pose & Gesture Recognition dataset https://lnkd.in/d_QdGGb9 14. 14. Driver monitoring dataset – dangerous, fatigue https://lnkd.in/d6kF-9PW 15. Japanese handwriting OCR dataset https://lnkd.in/dHnriqrH 16. American English Male voice TTS dataset https://lnkd.in/dqyvg862 17. Riddles and brain teasers dataset https://lnkd.in/dKBHY3DE 18. Chinese test questions text https://lnkd.in/dQpUd8xC 19. Chinese medical question answering data https://lnkd.in/dsbWUCpz 20. Multi-round interpersonal dialogues text data https://lnkd.in/dQiUq_Jg 21. Human activity recognition dataset https://lnkd.in/dHM52MfV 22. Facial expression recognition dataset https://lnkd.in/dqQAfMau 23. Urban surveillance dataset https://lnkd.in/dc2RCnTk 24. Human body segmentation dataset https://lnkd.in/d6sSrDxS 25. Fashion segmentation – clothing & accessories https://lnkd.in/dptNUTz8 26. Fight video dataset – action recognition https://lnkd.in/dnY_m5hZ 27. Gesture recognition dataset https://lnkd.in/dFVPivYg 28. Facial skin defects dataset https://lnkd.in/dKCbUvU6 29. Smoke detection and behaviour recognition dataset https://lnkd.in/ddGg56R4 30. Weight loss transformation video dataset https://lnkd.in/dqqT4ed9 https://t.me/CodeProgrammer 👾

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📌 Why You Should Stop Worrying About AI Taking Data Science Jobs 🗂 Category: DATA SCIENCE 🕒 Date: 2026-03-18 | ⏱️ Read time: 8 min read It’s all just fearmongering #DataScience #AI #Python

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📌 The New Experience of Coding with AI 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2026-03-18 | ⏱️ Read time: 12 min read The seduction of AI code assistants #DataScience #AI #Python

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Repost from Machine Learning with Python

Follow the Machine Learning with Python channel on WhatsApp: https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A

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📌 Two-Stage Hurdle Models: Predicting Zero-Inflated Outcomes 🗂 Category: MACHINE LEARNING 🕒 Date: 2026-03-18 | ⏱️ Read time: 20 min read Why one model can’t do two jobs #DataScience #AI #Python

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Listen, here’s the crazy part: while everyone’s scared BTC might crash, insiders just dropped $350 MILLION buying the dip. Unreal, right? The market’s playing a game where the biggest shorts might implode first-like a loaded gun cocked and ready. This isn’t hype, it’s cold, hard data from 14 years of trading wisdom. Wanna see how the pros move and actually win? Check this out 👉 Scalping Kings No fluff, just profits. #ad InsideAds

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CNN vs Vision Transformer — The Battle for Computer Vision 👁⚡️ Two architectures. One goal: identify the cat. But they see things differently: 🧠 CNN (Convolutional Neural Network) · Scans the image with filters · Detects local patterns first (edges → textures → shapes) · Builds understanding layer by layer 🔄 Vision Transformer (ViT) · Splits image into patches (like words in a sentence) · Detects global patterns from the start · Sees the whole picture using attention mechanisms Same input. Same output. Different journey. CNNs think locally and build up. Transformers think globally from the get-go. Which one wins? Depends on the task — but both are shaping the future of how machines see. https://t.me/CodeProgrammer

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📌 Self-Hosting Your First LLM 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2026-03-17 | ⏱️ Read time: 20 min read Privacy. Cost. Customization. Everything you need to know—step by step. #DataScience #AI #Python

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📌 How to Effectively Review Claude Code Output 🗂 Category: AGENTIC AI 🕒 Date: 2026-03-17 | ⏱️ Read time: 7 min read Get more out of your coding agents by making reviewing more efficient #DataScience #AI #Python

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Time Complexity of 10 Most Popular ML Algorithms Know What You're Waiting For ⏳🧠

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Repost from Machine Learning with Python

🚀 𝐓𝐎𝐏 𝐑𝐀𝐆 𝐈𝐍𝐓𝐄𝐑𝐕𝐈𝐄𝐖 𝐐𝐔𝐄𝐒𝐓𝐈𝐎𝐍𝐒 𝐀𝐍𝐃 𝐀𝐍𝐒𝐖𝐄𝐑𝐒 ⁣⁣ 🔹 Advanced #RAG engineering concepts⁣⁣ • Multi-stage retrieval pipelines⁣⁣ • Agentic RAG vs classical RAG⁣⁣ • Latency optimization⁣⁣ • Security risks in enterprise RAG systems⁣⁣ • Monitoring and debugging production RAG systems⁣⁣ ⁣⁣ 📄 𝐓𝐡𝐞 𝐏𝐃𝐅 𝐜𝐨𝐧𝐭𝐚𝐢𝐧𝐬 𝟒𝟎 𝐬𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞𝐝 𝐪𝐮𝐞𝐬𝐭𝐢𝐨𝐧𝐬 𝐰𝐢𝐭𝐡 𝐜𝐥𝐞𝐚𝐫 𝐞𝐱𝐩𝐥𝐚𝐧𝐚𝐭𝐢𝐨𝐧𝐬 𝐭𝐨 𝐡𝐞𝐥𝐩 𝐲𝐨𝐮 𝐮𝐧𝐝𝐞𝐫𝐬𝐭𝐚𝐧𝐝 𝐛𝐨𝐭𝐡 𝐜𝐨𝐧𝐜𝐞𝐩𝐭𝐬 𝐚𝐧𝐝 𝐬𝐲𝐬𝐭𝐞𝐦 𝐝𝐞𝐬𝐢𝐠𝐧 𝐭𝐡𝐢𝐧𝐤𝐢𝐧𝐠.⁣⁣ ⁣⁣ https://t.me/CodeProgrammer

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📌 How a Neural Network Learned Its Own Fraud Rules: A Neuro-Symbolic AI Experiment 🗂 Category: DEEP LEARNING 🕒 Date: 2026-03-17 | ⏱️ Read time: 18 min read Most neuro-symbolic systems inject rules written by humans. But what if a neural network could… #DataScience #AI #Python

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📌 Introducing Gemini Embeddings 2 Preview 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2026-03-17 | ⏱️ Read time: 10 min read One embedding model to rule them all #DataScience #AI #Python

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📌 How to Build a Production-Ready Claude Code Skill 🗂 Category: AGENTIC AI 🕒 Date: 2026-03-16 | ⏱️ Read time: 11 min read What I learned building and distributing my first Skill from scratch #DataScience #AI #Python

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📌 Follow the AI Footpaths 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2026-03-16 | ⏱️ Read time: 6 min read Shadow AI and the desire paths of modern work #DataScience #AI #Python