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Machine Learning

Machine Learning

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

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πŸ“ˆ Analytical overview of Telegram channel Machine Learning

Channel Machine Learning (@machinelearning9) in the English language segment is an active participant. Currently, the community unites 40 191 subscribers, ranking 3 381 in the Technologies & Applications category and 228 in the Syria region.

πŸ“Š Audience metrics and dynamics

Since its creation on Π½Π΅Π²Ρ–Π΄ΠΎΠΌΠΎ, the project has demonstrated rapid growth, gathering an audience of 40 191 subscribers.

According to the latest data from 01 July, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 355 over the last 30 days and by 21 over the last 24 hours, overall reach remains high.

  • Verification status: Not verified
  • Engagement rate (ER): The average audience engagement rate is 2.04%. Within the first 24 hours after publication, content typically collects 2.12% reactions from the total number of subscribers.
  • Post reach: On average, each post receives 818 views. Within the first day, a publication typically gains 851 views.
  • Reactions and interaction: The audience actively supports content: the average number of reactions per post is 2.
  • Thematic interests: Content is focused on key topics such as distance, insidead, gpu, learning, degree.

πŸ“ Description and content policy

The author describes the resource as a platform for expressing subjective opinions:
β€œReal Machine Learning β€” 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 02 July, 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.

40 191
Subscribers
+2124 hours
+857 days
+35530 days
Posts Archive
πŸ“Œ Metrics to Evaluate a Classification Machine Learning Model πŸ—‚ Category: πŸ•’ Date: 2024-07-31 | ⏱️ Read time: 8 min read A
πŸ“Œ Metrics to Evaluate a Classification Machine Learning Model πŸ—‚ Category: πŸ•’ Date: 2024-07-31 | ⏱️ Read time: 8 min read A study case of credit card fraud

πŸ“Œ 8 Practical Prompt Engineering Tips for Better LLM Apps πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-08-01 | ⏱️ Read
πŸ“Œ 8 Practical Prompt Engineering Tips for Better LLM Apps πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-08-01 | ⏱️ Read time: 10 min read How to boost your LLM-native apps with 8 actionable prompt engineering tips for performance, production…

πŸ“Œ SQL Optimization, Data Science Portfolios, and Other July Must-Reads πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-08-01 | ⏱️ Re
πŸ“Œ SQL Optimization, Data Science Portfolios, and Other July Must-Reads πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-08-01 | ⏱️ Read time: 4 min read The stories that resonated the most with our community in the past month

πŸ“Œ Local LLM Fine-Tuning on Mac (M1 16GB) πŸ—‚ Category: LARGE LANGUAGE MODELS πŸ•’ Date: 2024-08-01 | ⏱️ Read time: 9 min read B
πŸ“Œ Local LLM Fine-Tuning on Mac (M1 16GB) πŸ—‚ Category: LARGE LANGUAGE MODELS πŸ•’ Date: 2024-08-01 | ⏱️ Read time: 9 min read Beginner-friendly Python code walkthrough (ft. MLX)

πŸ“Œ Introduction to Interpretable Clustering πŸ—‚ Category: πŸ•’ Date: 2024-08-01 | ⏱️ Read time: 13 min read What is interpretabl
πŸ“Œ Introduction to Interpretable Clustering πŸ—‚ Category: πŸ•’ Date: 2024-08-01 | ⏱️ Read time: 13 min read What is interpretable clustering and why is it important.

πŸ“Œ Economics of Generative AI πŸ—‚ Category: BUSINESS πŸ•’ Date: 2024-08-01 | ⏱️ Read time: 8 min read What’s the business model
πŸ“Œ Economics of Generative AI πŸ—‚ Category: BUSINESS πŸ•’ Date: 2024-08-01 | ⏱️ Read time: 8 min read What’s the business model for generative AI, given what we know today about the technology…

πŸ“Œ Building a RAG Pipeline with MongoDB: Vector Search for Personalized Movie Picks πŸ—‚ Category: LARGE LANGUAGE MODELS πŸ•’ Dat
πŸ“Œ Building a RAG Pipeline with MongoDB: Vector Search for Personalized Movie Picks πŸ—‚ Category: LARGE LANGUAGE MODELS πŸ•’ Date: 2024-08-01 | ⏱️ Read time: 9 min read Learn how to integrate LLMs and MongoDB for precise, context-aware suggestion systems

πŸ“Œ How I Built My First RAG Pipeline πŸ—‚ Category: πŸ•’ Date: 2024-08-02 | ⏱️ Read time: 6 min read A RAG pipeline to answer all
πŸ“Œ How I Built My First RAG Pipeline πŸ—‚ Category: πŸ•’ Date: 2024-08-02 | ⏱️ Read time: 6 min read A RAG pipeline to answer all of your recruiters’ questions for you!

πŸ“Œ The Top 10 Data Lifecycle Problems that Data Engineering Solves πŸ—‚ Category: ANALYTICS πŸ•’ Date: 2024-08-02 | ⏱️ Read time:
πŸ“Œ The Top 10 Data Lifecycle Problems that Data Engineering Solves πŸ—‚ Category: ANALYTICS πŸ•’ Date: 2024-08-02 | ⏱️ Read time: 16 min read Clear strategies for addressing key pain points

πŸ“Œ 3 Surprising Use-cases for Branching in Airflow you’ve not seen before πŸ—‚ Category: DATA ENGINEERING πŸ•’ Date: 2024-08-02 |
πŸ“Œ 3 Surprising Use-cases for Branching in Airflow you’ve not seen before πŸ—‚ Category: DATA ENGINEERING πŸ•’ Date: 2024-08-02 | ⏱️ Read time: 4 min read Introduction How often is it that you’re writing a Data Pipeline and then you wish…

πŸ“Œ Python Code Playground in MkDocs πŸ—‚ Category: PRODUCTIVITY πŸ•’ Date: 2024-08-02 | ⏱️ Read time: 6 min read Making documenta
πŸ“Œ Python Code Playground in MkDocs πŸ—‚ Category: PRODUCTIVITY πŸ•’ Date: 2024-08-02 | ⏱️ Read time: 6 min read Making documentation come to life

πŸ“Œ TDS Newsletter: September Must-Reads on ML Career Roadmaps, Python Essentials, AI Agents, and More πŸ—‚ Category: THE VARIAB
πŸ“Œ TDS Newsletter: September Must-Reads on ML Career Roadmaps, Python Essentials, AI Agents, and More πŸ—‚ Category: THE VARIABLE πŸ•’ Date: 2025-10-02 | ⏱️ Read time: 3 min read Don’t miss our most-read and -shared articles of the past month

Ever wondered how much smarter your workflow could be with AI? Meet Padma AI β€” your personal Telegram bot that makes work fas
Ever wondered how much smarter your workflow could be with AI? Meet Padma AI β€” your personal Telegram bot that makes work faster, easier, smarter. Try out the AI assistant everyone’s talking about now β€” and see how much more you can do in a day. Don’t miss your edge β€” join Padma AI and upgrade your routine! #ad InsideAds

Missed the latest Kaiju battle? Season 2 of β€œKaiju No.8” ⛩️ is almost hereβ€”now in Hindi dub! Be first to watch exclusive epis
Missed the latest Kaiju battle? Season 2 of β€œKaiju No.8” ⛩️ is almost hereβ€”now in Hindi dub! Be first to watch exclusive episodes, insider updates, and surprise drops right here. Don’t waitβ€”join now to catch every epic moment before anyone else. Ready for the ultimate anime experience? Subscribe now! #ad InsideAds

Missed the last big airdrop? Don’t repeat it. Padma turns grinding into a clear loop: finish daily quests, unlock upgrades an
Missed the last big airdrop? Don’t repeat it. Padma turns grinding into a clear loop: finish daily quests, unlock upgrades and artifacts drops, and convert progress into PAD tokens. Start early this season to grab higher multipliers and leaderboard rewards. Start now! #ad InsideAds

πŸ“Œ AI Hallucinations: Can Memory Hold the Answer? πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-08-02 | ⏱️ Read time: 7
πŸ“Œ AI Hallucinations: Can Memory Hold the Answer? πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-08-02 | ⏱️ Read time: 7 min read Exploring How Memory Mechanisms Can Mitigate Hallucinations in Large Language Models

πŸ“Œ 5 PCA Visualizations You Must Try On Your Next Data Science Project πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-08-02 | ⏱️ Rea
πŸ“Œ 5 PCA Visualizations You Must Try On Your Next Data Science Project πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-08-02 | ⏱️ Read time: 8 min read Which features carry the most weight? How do original features contribute to principal components? These…

πŸ“Œ Train/Fine-Tune Segment Anything 2 (SAM 2) in 60 Lines of Code πŸ—‚ Category: MACHINE LEARNING πŸ•’ Date: 2024-08-03 | ⏱️ Read
πŸ“Œ Train/Fine-Tune Segment Anything 2 (SAM 2) in 60 Lines of Code πŸ—‚ Category: MACHINE LEARNING πŸ•’ Date: 2024-08-03 | ⏱️ Read time: 15 min read A step-by-step tutorial for fine-tuning SAM2 for custom segmentation tasks.

πŸ“Œ Text Vectorization Demystified: Transforming Language into Data πŸ—‚ Category: πŸ•’ Date: 2024-08-03 | ⏱️ Read time: 13 min re
πŸ“Œ Text Vectorization Demystified: Transforming Language into Data πŸ—‚ Category: πŸ•’ Date: 2024-08-03 | ⏱️ Read time: 13 min read An intuitive guide to text vectorization

πŸ“Œ PySpark Explained: Delta Tables πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-08-03 | ⏱️ Read time: 15 min read Learn how to use
πŸ“Œ PySpark Explained: Delta Tables πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-08-03 | ⏱️ Read time: 15 min read Learn how to use the building blocks of Delta Lakes.