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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 40 346 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 40 346 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.

40 346
Subscribers
+1724 hours
+1237 days
+39330 days
Posts Archive
πŸ“Œ Applications of Density Estimation to Legal Theory πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2025-06-10 | ⏱️ Read time: 15 min re
πŸ“Œ Applications of Density Estimation to Legal Theory πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2025-06-10 | ⏱️ Read time: 15 min read A brief analysis using density estimation to compare the two-verdict and three-verdict systems.

πŸ“Œ 10,000x Faster Bayesian Inference: Multi-GPU SVI vs. Traditional MCMC πŸ—‚ Category: MACHINE LEARNING πŸ•’ Date: 2025-06-10 |
πŸ“Œ 10,000x Faster Bayesian Inference: Multi-GPU SVI vs. Traditional MCMC πŸ—‚ Category: MACHINE LEARNING πŸ•’ Date: 2025-06-10 | ⏱️ Read time: 18 min read Using GPU acceleration to speed up Bayesian Inference from months to minutes…

πŸ“Œ Automate Models Training: An MLOps Pipeline with Tekton and Buildpacks πŸ—‚ Category: MACHINE LEARNING πŸ•’ Date: 2025-06-10 |
πŸ“Œ Automate Models Training: An MLOps Pipeline with Tekton and Buildpacks πŸ—‚ Category: MACHINE LEARNING πŸ•’ Date: 2025-06-10 | ⏱️ Read time: 11 min read A step-by-step guide to containerizing and orchestrating an ML training workflow without the Dockerfile headache,…

πŸ“Œ Audio Spectrogram Transformers Beyond the Lab πŸ—‚ Category: MACHINE LEARNING πŸ•’ Date: 2025-06-10 | ⏱️ Read time: 7 min read
πŸ“Œ Audio Spectrogram Transformers Beyond the Lab πŸ—‚ Category: MACHINE LEARNING πŸ•’ Date: 2025-06-10 | ⏱️ Read time: 7 min read A recipe for building a portable soundscape monitoring app with AudioMoth, Raspberry Pi, and a…

πŸ“Œ Mobile App Development with Python πŸ—‚ Category: PROGRAMMING πŸ•’ Date: 2025-06-11 | ⏱️ Read time: 8 min read Build iOS & And
πŸ“Œ Mobile App Development with Python πŸ—‚ Category: PROGRAMMING πŸ•’ Date: 2025-06-11 | ⏱️ Read time: 8 min read Build iOS & Android Apps with Kivy

πŸ“Œ Can AI Truly Develop a Memory That Adapts Like Ours? πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2025-06-12 | ⏱️ Read ti
πŸ“Œ Can AI Truly Develop a Memory That Adapts Like Ours? πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2025-06-12 | ⏱️ Read time: 17 min read Exploring Titans: A new architecture equipping LLMs with human-inspired memory that learns and updates itself…

πŸ“Œ Exploring the Proportional Odds Model for Ordinal Logistic Regression πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2025-06-12 | ⏱️ R
πŸ“Œ Exploring the Proportional Odds Model for Ordinal Logistic Regression πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2025-06-12 | ⏱️ Read time: 21 min read Understanding and Implementing Brant’s Tests in Ordinal Logistic Regression with Python

πŸ“Œ User Authorisation in Streamlit With OIDC and Google πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2025-06-12 | ⏱️ Read time: 10 min
πŸ“Œ User Authorisation in Streamlit With OIDC and Google πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2025-06-12 | ⏱️ Read time: 10 min read Log in to a Streamlit app with a Google email account

πŸ“Œ Design Smarter Prompts and Boost Your LLM Output: Real Tricks from an AI Engineer’s Toolbox πŸ—‚ Category: LARGE LANGUAGE MO
πŸ“Œ Design Smarter Prompts and Boost Your LLM Output: Real Tricks from an AI Engineer’s Toolbox πŸ—‚ Category: LARGE LANGUAGE MODELS πŸ•’ Date: 2025-06-12 | ⏱️ Read time: 9 min read Not just what you ask, but how you ask it. Practical techniques for prompt engineering…

πŸ“Œ Agentic AI 103: Building Multi-Agent Teams πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2025-06-12 | ⏱️ Read time: 9 min
πŸ“Œ Agentic AI 103: Building Multi-Agent Teams πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2025-06-12 | ⏱️ Read time: 9 min read Build multi-agent teams that can automate tasks and enhance productivity.

πŸ“Œ Connecting the Dots for Better Movie Recommendations πŸ—‚ Category: LARGE LANGUAGE MODELS πŸ•’ Date: 2025-06-12 | ⏱️ Read time
πŸ“Œ Connecting the Dots for Better Movie Recommendations πŸ—‚ Category: LARGE LANGUAGE MODELS πŸ•’ Date: 2025-06-12 | ⏱️ Read time: 11 min read Connecting the Dots for Better Movie Recommendations: Lightweight graph RAG on Rotten Tomatoes movie reviews

πŸ“Œ How AI Agents β€œTalk” to Each Other πŸ—‚ Category: THE VARIABLE πŸ•’ Date: 2025-06-13 | ⏱️ Read time: 3 min read Minimize chaos
πŸ“Œ How AI Agents β€œTalk” to Each Other πŸ—‚ Category: THE VARIABLE πŸ•’ Date: 2025-06-13 | ⏱️ Read time: 3 min read Minimize chaos and maintain inter-agent harmony in your projects

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πŸ“Œ AI Is Not a Black Box (Relatively Speaking) πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2025-06-13 | ⏱️ Read time: 7 min
πŸ“Œ AI Is Not a Black Box (Relatively Speaking) πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2025-06-13 | ⏱️ Read time: 7 min read Compared to the opacity around human intelligence, AI is more transparent in some very tangible…

πŸ“Œ What If I had AI in 2018: Rent the Runway Fulfillment Center Optimization πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 20
πŸ“Œ What If I had AI in 2018: Rent the Runway Fulfillment Center Optimization πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2025-06-13 | ⏱️ Read time: 7 min read An LLM in 2018 would not have trivialized a complex project, although it could have…

πŸ“Œ Stop Building AI Platforms πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2025-06-13 | ⏱️ Read time: 7 min read When small
πŸ“Œ Stop Building AI Platforms πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2025-06-13 | ⏱️ Read time: 7 min read When small and medium companies achieve success in building Data and ML platforms, building AI…

πŸ“Œ Agents, APIs, and the Next Layer of the Internet πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2025-06-16 | ⏱️ Read time:
πŸ“Œ Agents, APIs, and the Next Layer of the Internet πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2025-06-16 | ⏱️ Read time: 13 min read Part I: Shipping Containers for Thought Every so often a simple idea rewires everything. The…

πŸ“Œ I Won $10,000 in a Machine Learning Competition β€” Here’s My Complete Strategy πŸ—‚ Category: MACHINE LEARNING πŸ•’ Date: 2025-
πŸ“Œ I Won $10,000 in a Machine Learning Competition β€” Here’s My Complete Strategy πŸ—‚ Category: MACHINE LEARNING πŸ•’ Date: 2025-06-16 | ⏱️ Read time: 7 min read Complete guide to feature selection, threshold optimization, and neural network architecture for ML competitions

πŸ“Œ Build an AI Agent to Explore Your Data Catalog with Natural Language πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2025-06
πŸ“Œ Build an AI Agent to Explore Your Data Catalog with Natural Language πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2025-06-16 | ⏱️ Read time: 7 min read Leverage LLMs to query your Databricks Data Catalog

πŸ“Œ Regularisation: A Deep Dive into Theory, Implementation, and Practical Insights πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2025-06
πŸ“Œ Regularisation: A Deep Dive into Theory, Implementation, and Practical Insights πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2025-06-16 | ⏱️ Read time: 70 min read A detailed guide on controlling overfitting and increasing the stability of your models.