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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 40 365 subscribers, ranking 3 329 in the Technologies & Applications category and 225 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 40 365 subscribers.

According to the latest data from 11 July, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 393 over the last 30 days and by 17 over the last 24 hours, overall reach remains high.

  • Verification status: Not verified
  • Engagement rate (ER): The average audience engagement rate is 2.29%. Within the first 24 hours after publication, content typically collects 1.74% reactions from the total number of subscribers.
  • Post reach: On average, each post receives 924 views. Within the first day, a publication typically gains 702 views.
  • Reactions and interaction: The audience actively supports content: the average number of reactions per post is 4.
  • 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 12 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.

40 365
Subscribers
+1724 hours
+1237 days
+39330 days
Posts Archive
πŸ“Œ There and Back Again: An AI Career Journey πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2025-07-14 | ⏱️ Read time: 7 min
πŸ“Œ There and Back Again: An AI Career Journey πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2025-07-14 | ⏱️ Read time: 7 min read A full circle moment 30 years in the making

πŸ“Œ Topic Model Labelling with LLMs πŸ—‚ Category: LARGE LANGUAGE MODELS πŸ•’ Date: 2025-07-14 | ⏱️ Read time: 6 min read Python t
πŸ“Œ Topic Model Labelling with LLMs πŸ—‚ Category: LARGE LANGUAGE MODELS πŸ•’ Date: 2025-07-14 | ⏱️ Read time: 6 min read Python tutorial for reproducible labeling of cutting-edge topic models with GPT4-o-mini.

πŸ“Œ Accuracy Is Dead: Calibration, Discrimination, and Other Metrics You Actually Need πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2025
πŸ“Œ Accuracy Is Dead: Calibration, Discrimination, and Other Metrics You Actually Need πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2025-07-14 | ⏱️ Read time: 7 min read A deep dive into advanced evaluation for data scientists

πŸ“Œ The Future of AI Agent Communication with ACP πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2025-07-15 | ⏱️ Read time: 17
πŸ“Œ The Future of AI Agent Communication with ACP πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2025-07-15 | ⏱️ Read time: 17 min read A practical guide to connecting and coordinating multiple AI agents.

πŸ“Œ Automating Deep Learning: A Gentle Introduction to AutoKeras and Keras Tuner πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2025-07-15
πŸ“Œ Automating Deep Learning: A Gentle Introduction to AutoKeras and Keras Tuner πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2025-07-15 | ⏱️ Read time: 4 min read How to save time and boost your models with these two approachable AutoML libraries.

πŸ“Œ From Equal Weights to Smart Weights: OTPO’s Approach to Better LLM Alignment πŸ—‚ Category: LARGE LANGUAGE MODELS πŸ•’ Date: 2
πŸ“Œ From Equal Weights to Smart Weights: OTPO’s Approach to Better LLM Alignment πŸ—‚ Category: LARGE LANGUAGE MODELS πŸ•’ Date: 2025-07-15 | ⏱️ Read time: 7 min read Using optimal transport to weight what matters most In LLM-generated responses

πŸ“Œ Deploy a Streamlit App to AWS πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2025-07-15 | ⏱️ Read time: 16 min read Using the Elastic
πŸ“Œ Deploy a Streamlit App to AWS πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2025-07-15 | ⏱️ Read time: 16 min read Using the Elastic Beanstalk service

πŸ“Œ How to Ensure Reliability in LLM Applications πŸ—‚ Category: LARGE LANGUAGE MODELS πŸ•’ Date: 2025-07-15 | ⏱️ Read time: 7 min
πŸ“Œ How to Ensure Reliability in LLM Applications πŸ—‚ Category: LARGE LANGUAGE MODELS πŸ•’ Date: 2025-07-15 | ⏱️ Read time: 7 min read Learn how to make your LLM applications more robust

πŸ“Œ How Metrics (and LLMs) Can Trick You: A Field Guide to Paradoxes πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2025-07-15 | ⏱️ Read t
πŸ“Œ How Metrics (and LLMs) Can Trick You: A Field Guide to Paradoxes πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2025-07-15 | ⏱️ Read time: 8 min read When numbers lie β€” and your metrics mislead you

πŸ“Œ Do You Really Need a Foundation Model? πŸ—‚ Category: MACHINE LEARNING πŸ•’ Date: 2025-07-16 | ⏱️ Read time: 10 min read LLM o
πŸ“Œ Do You Really Need a Foundation Model? πŸ—‚ Category: MACHINE LEARNING πŸ•’ Date: 2025-07-16 | ⏱️ Read time: 10 min read LLM or custom model: how should you choose the right solution?

πŸ“Œ The Power of Building from Scratch πŸ—‚ Category: AUTHOR SPOTLIGHTS πŸ•’ Date: 2025-07-16 | ⏱️ Read time: 5 min read Mauro Di
πŸ“Œ The Power of Building from Scratch πŸ—‚ Category: AUTHOR SPOTLIGHTS πŸ•’ Date: 2025-07-16 | ⏱️ Read time: 5 min read Mauro Di Pietro discusses building AI agents with open-source tools, bridging theory and practice, and…

πŸ“Œ 3 Steps to Context Engineering a Crystal-Clear Project πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2025-07-16 | ⏱️ Read
πŸ“Œ 3 Steps to Context Engineering a Crystal-Clear Project πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2025-07-16 | ⏱️ Read time: 7 min read Learn three easy steps for gaining an intelligent picture for any project by using the…

πŸ“Œ How to Overlay a Heatmap on a Real Map with Python πŸ—‚ Category: DATA VISUALIZATION πŸ•’ Date: 2025-07-16 | ⏱️ Read time: 9 m
πŸ“Œ How to Overlay a Heatmap on a Real Map with Python πŸ—‚ Category: DATA VISUALIZATION πŸ•’ Date: 2025-07-16 | ⏱️ Read time: 9 min read Visualizing historical tornado trends

πŸ“Œ Exploring Prompt Learning: Using English Feedback to Optimize LLM Systems πŸ—‚ Category: LARGE LANGUAGE MODELS πŸ•’ Date: 2025
πŸ“Œ Exploring Prompt Learning: Using English Feedback to Optimize LLM Systems πŸ—‚ Category: LARGE LANGUAGE MODELS πŸ•’ Date: 2025-07-16 | ⏱️ Read time: 11 min read Prompt learning presents a compelling approach for continuous improvement of AI applications

πŸ“Œ Midyear 2025 AI Reflection πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2025-07-16 | ⏱️ Read time: 7 min read Impressions
πŸ“Œ Midyear 2025 AI Reflection πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2025-07-16 | ⏱️ Read time: 7 min read Impressions on agentic AI progress and the AI-2027 Jobocalypse scenario

πŸ“Œ Your 1M+ Context Window LLM Is Less Powerful Than You Think πŸ—‚ Category: LARGE LANGUAGE MODELS πŸ•’ Date: 2025-07-17 | ⏱️ Re
πŸ“Œ Your 1M+ Context Window LLM Is Less Powerful Than You Think πŸ—‚ Category: LARGE LANGUAGE MODELS πŸ•’ Date: 2025-07-17 | ⏱️ Read time: 9 min read Why working memory is a more important bottleneck than raw context window size

πŸ“Œ Summer Must-Reads: The Data Science Edition πŸ—‚ Category: THE VARIABLE πŸ•’ Date: 2025-07-17 | ⏱️ Read time: 4 min read Cool
πŸ“Œ Summer Must-Reads: The Data Science Edition πŸ—‚ Category: THE VARIABLE πŸ•’ Date: 2025-07-17 | ⏱️ Read time: 4 min read Cool off with some engaging, enlightening reads.

πŸ“Œ Don’t Waste Your Labeled Anomalies: 3 Practical Strategies to Boost Anomaly Detection Performance πŸ—‚ Category: MACHINE LEA
πŸ“Œ Don’t Waste Your Labeled Anomalies: 3 Practical Strategies to Boost Anomaly Detection Performance πŸ—‚ Category: MACHINE LEARNING πŸ•’ Date: 2025-07-17 | ⏱️ Read time: 15 min read A few labels go a long way in anomaly detection

πŸ“Œ Estimating Disease Rates Without Diagnosis πŸ—‚ Category: STATISTICS πŸ•’ Date: 2025-07-17 | ⏱️ Read time: 7 min read Immune g
πŸ“Œ Estimating Disease Rates Without Diagnosis πŸ—‚ Category: STATISTICS πŸ•’ Date: 2025-07-17 | ⏱️ Read time: 7 min read Immune genes as predictors of disease

πŸ“Œ The Age of Self-Evolving AI Is Here πŸ—‚ Category: LARGE LANGUAGE MODELS πŸ•’ Date: 2025-07-17 | ⏱️ Read time: 17 min read How
πŸ“Œ The Age of Self-Evolving AI Is Here πŸ—‚ Category: LARGE LANGUAGE MODELS πŸ•’ Date: 2025-07-17 | ⏱️ Read time: 17 min read How Meta’s latest breakthrough lets models learn, adapt, and improve β€” all on their own