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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 Embracing Simplicity and Composability in Data Engineering πŸ—‚ Category: DATA ENGINEERING πŸ•’ Date: 2024-08-03 | ⏱️ Read tim
πŸ“Œ Embracing Simplicity and Composability in Data Engineering πŸ—‚ Category: DATA ENGINEERING πŸ•’ Date: 2024-08-03 | ⏱️ Read time: 11 min read Lessons from 30+ years in data engineering: The overlooked value of keeping it simple

πŸ“Œ Predicting metadata for Humanitarian datasets with LLMs part 2 – An alternative to fine-tuning πŸ—‚ Category: DATA SCIENCE οΏ½
πŸ“Œ Predicting metadata for Humanitarian datasets with LLMs part 2 – An alternative to fine-tuning πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-08-03 | ⏱️ Read time: 34 min read Using LLM prompting for tagging metadata on humanitarian datasets

πŸ“Œ Productionizing a RAG App with Prefect, Weave, and RAGAS πŸ—‚ Category: πŸ•’ Date: 2024-08-03 | ⏱️ Read time: 14 min read Addi
πŸ“Œ Productionizing a RAG App with Prefect, Weave, and RAGAS πŸ—‚ Category: πŸ•’ Date: 2024-08-03 | ⏱️ Read time: 14 min read Adding evaluation, automated data pulling, and other improvements.

πŸ“Œ Easy Object Detection with Yolo-NAS πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-08-03 | ⏱️ Read time: 7 min read Le
πŸ“Œ Easy Object Detection with Yolo-NAS πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-08-03 | ⏱️ Read time: 7 min read Learn how to do object detection with Python using yolo-NAS

πŸ“Œ An Introduction to Quantile Loss, a.k.a. the Pinball Loss πŸ—‚ Category: DEEP LEARNING πŸ•’ Date: 2024-08-04 | ⏱️ Read time: 7
πŸ“Œ An Introduction to Quantile Loss, a.k.a. the Pinball Loss πŸ—‚ Category: DEEP LEARNING πŸ•’ Date: 2024-08-04 | ⏱️ Read time: 7 min read Learn the intuition behind the metric used to evaluate probabilistic forecasts

πŸ“Œ Data Value Lineage, meaning at last? πŸ—‚ Category: πŸ•’ Date: 2024-08-04 | ⏱️ Read time: 10 min read Maximise the business va
πŸ“Œ Data Value Lineage, meaning at last? πŸ—‚ Category: πŸ•’ Date: 2024-08-04 | ⏱️ Read time: 10 min read Maximise the business value of your data

πŸ“Œ The Secret Network of Owls πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-08-04 | ⏱️ Read time: 6 min read A data-based tribute t
πŸ“Œ The Secret Network of Owls πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-08-04 | ⏱️ Read time: 6 min read A data-based tribute to the International Owl Awareness Day

πŸ“Œ How To Learn AI (Roadmap) πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-08-05 | ⏱️ Read time: 11 min read A full brea
πŸ“Œ How To Learn AI (Roadmap) πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-08-05 | ⏱️ Read time: 11 min read A full breakdown of how you can learn AI this year effectively

πŸ“Œ ChatGPT vs. Claude vs. Gemini for Data Analysis (Part 1) πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-08-05 | ⏱️ Rea
πŸ“Œ ChatGPT vs. Claude vs. Gemini for Data Analysis (Part 1) πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-08-05 | ⏱️ Read time: 21 min read Ten Questions to test which AI assistant writes the best SQL

πŸ“Œ How I Built BeatBuddy: A Web App that Analyzes Your Spotify Data πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-08-05 | ⏱️ Read t
πŸ“Œ How I Built BeatBuddy: A Web App that Analyzes Your Spotify Data πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-08-05 | ⏱️ Read time: 11 min read The story behind BeatBuddy, a web app that analyzes what you’re listening to on Spotify

πŸ“Œ How to Dynamically Restrict Data Import in Power Query πŸ—‚ Category: πŸ•’ Date: 2024-08-06 | ⏱️ Read time: 9 min read When we
πŸ“Œ How to Dynamically Restrict Data Import in Power Query πŸ—‚ Category: πŸ•’ Date: 2024-08-06 | ⏱️ Read time: 9 min read When we have a large amount of data, we might ask: Do I really need…

πŸ“Œ Segment Anything 2: What Is the Secret Sauce? (A Deep Learner’s Guide) πŸ—‚ Category: DEEP LEARNING πŸ•’ Date: 2024-08-06 | ⏱️
πŸ“Œ Segment Anything 2: What Is the Secret Sauce? (A Deep Learner’s Guide) πŸ—‚ Category: DEEP LEARNING πŸ•’ Date: 2024-08-06 | ⏱️ Read time: 12 min read Foundation + Promptable + Interactive + Video. How?

πŸ“Œ Gemma vs. Llama vs. Mistral: Exploring Smaller AI Models πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-08-06 | ⏱️ Rea
πŸ“Œ Gemma vs. Llama vs. Mistral: Exploring Smaller AI Models πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-08-06 | ⏱️ Read time: 16 min read A Comparative Study of Small-Scale Language Models: Evaluating Gemma, Llama 3, and Mistral in Reading…

πŸ“Œ A Simple Strategy to Improve LLM Query Generation πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-08-06 | ⏱️ Read time:
πŸ“Œ A Simple Strategy to Improve LLM Query Generation πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-08-06 | ⏱️ Read time: 27 min read Dynamic Few-Shot Prompting

πŸ“Œ Visualizing Stochastic Regularization for Entity Embeddings πŸ—‚ Category: MACHINE LEARNING πŸ•’ Date: 2024-08-06 | ⏱️ Read ti
πŸ“Œ Visualizing Stochastic Regularization for Entity Embeddings πŸ—‚ Category: MACHINE LEARNING πŸ•’ Date: 2024-08-06 | ⏱️ Read time: 15 min read A glimpse into how neural networks perceive categoricals and their hierarchies

πŸ“Œ Seven Key Features You Should Know for Creating Professional Visualizations with Plotly πŸ—‚ Category: DATA VISUALIZATION πŸ•’
πŸ“Œ Seven Key Features You Should Know for Creating Professional Visualizations with Plotly πŸ—‚ Category: DATA VISUALIZATION πŸ•’ Date: 2024-08-06 | ⏱️ Read time: 5 min read Create Visualizations at the Level of Leading Newspapers

πŸ“Œ How I Became a Data Scientist at Meta Without A β€œPerfect” Degree πŸ—‚ Category: CAREER ADVICE πŸ•’ Date: 2024-08-06 | ⏱️ Read
πŸ“Œ How I Became a Data Scientist at Meta Without A β€œPerfect” Degree πŸ—‚ Category: CAREER ADVICE πŸ•’ Date: 2024-08-06 | ⏱️ Read time: 15 min read 4 jobs and 2 career pivots

πŸ“Œ Data Visualization Explained (Part 3): The Role of Color πŸ—‚ Category: DATA VISUALIZATION πŸ•’ Date: 2025-10-08 | ⏱️ Read tim
πŸ“Œ Data Visualization Explained (Part 3): The Role of Color πŸ—‚ Category: DATA VISUALIZATION πŸ•’ Date: 2025-10-08 | ⏱️ Read time: 7 min read A simple and powerful guide to using color for more impactful data stories.

πŸ“Œ Scale Your Productivity: Leveraging AWS Gen AI to Summarize Meeting Notes in Seconds πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 20
πŸ“Œ Scale Your Productivity: Leveraging AWS Gen AI to Summarize Meeting Notes in Seconds πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-08-06 | ⏱️ Read time: 18 min read A comprehensive walkthrough on how one can utilize AWS Lambda, Bedrock, and S3 to create…

πŸ“Œ Visualising Strava Race Analysis πŸ—‚ Category: πŸ•’ Date: 2024-08-06 | ⏱️ Read time: 17 min read Two New Graphs That Compare
πŸ“Œ Visualising Strava Race Analysis πŸ—‚ Category: πŸ•’ Date: 2024-08-06 | ⏱️ Read time: 17 min read Two New Graphs That Compare Runners on the Same Event