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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 40 265 subscribers, ranking 3 343 in the Technologies & Applications category and 227 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 40 265 subscribers.

According to the latest data from 06 July, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 336 over the last 30 days and by -4 over the last 24 hours, overall reach remains high.

  • Verification status: Not verified
  • Engagement rate (ER): The average audience engagement rate is 2.25%. Within the first 24 hours after publication, content typically collects 1.88% reactions from the total number of subscribers.
  • Post reach: On average, each post receives 906 views. Within the first day, a publication typically gains 758 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.

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 07 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.

40 265
Subscribers
-424 hours
+917 days
+33630 days
Posts Archive
πŸ“Œ Meet GPT, The Decoder-Only Transformer πŸ—‚ Category: DEEP LEARNING πŸ•’ Date: 2025-01-06 | ⏱️ Read time: 31 min read Understa
πŸ“Œ Meet GPT, The Decoder-Only Transformer πŸ—‚ Category: DEEP LEARNING πŸ•’ Date: 2025-01-06 | ⏱️ Read time: 31 min read Understanding and implementing the GPT-1, GPT-2 and GPT-3 architectures

πŸ“Œ Understanding the Evolution of ChatGPT: Part 1-An In-Depth Look at GPT-1 and What Inspired It πŸ—‚ Category: DEEP LEARNING οΏ½
πŸ“Œ Understanding the Evolution of ChatGPT: Part 1-An In-Depth Look at GPT-1 and What Inspired It πŸ—‚ Category: DEEP LEARNING πŸ•’ Date: 2025-01-06 | ⏱️ Read time: 11 min read Tracing the roots of ChatGPT: GPT-1, the foundation of OpenAI’s LLMs

πŸ“Œ The State of Quantum Computing: Where Are We Today? πŸ—‚ Category: SCIENCE AND TECHNOLOGY πŸ•’ Date: 2025-01-06 | ⏱️ Read time
πŸ“Œ The State of Quantum Computing: Where Are We Today? πŸ—‚ Category: SCIENCE AND TECHNOLOGY πŸ•’ Date: 2025-01-06 | ⏱️ Read time: 8 min read And what we need to overcome

πŸ“Œ AI Agents Hype, Explained – What You Really Need to Know to Get Started πŸ—‚ Category: LARGE LANGUAGE MODELS πŸ•’ Date: 2025-0
πŸ“Œ AI Agents Hype, Explained – What You Really Need to Know to Get Started πŸ—‚ Category: LARGE LANGUAGE MODELS πŸ•’ Date: 2025-01-06 | ⏱️ Read time: 6 min read I’ll set the record straight – AI Agents are not new but advanced. Learn how…

πŸ“Œ In Defense of Statistical Significance πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2025-01-06 | ⏱️ Read time: 6 min read We have to
πŸ“Œ In Defense of Statistical Significance πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2025-01-06 | ⏱️ Read time: 6 min read We have to draw the line somewhere

πŸ“Œ Encapsulation: A Software Engineering Concept Data Scientists Must Know To Succeed πŸ—‚ Category: CODING πŸ•’ Date: 2025-01-06
πŸ“Œ Encapsulation: A Software Engineering Concept Data Scientists Must Know To Succeed πŸ—‚ Category: CODING πŸ•’ Date: 2025-01-06 | ⏱️ Read time: 13 min read Simple concepts that differentiate a professional from amateurs

πŸ“Œ An Overview of Feature Selection πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2025-01-07 | ⏱️ Read time: 28 min read With a presenta
πŸ“Œ An Overview of Feature Selection πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2025-01-07 | ⏱️ Read time: 28 min read With a presentation of a rarely used, highly effective method for establishing the predictive power…

πŸ“Œ The Build vs. Buy Dilemma for GenAI Applications πŸ—‚ Category: STRATEGY πŸ•’ Date: 2025-01-07 | ⏱️ Read time: 6 min read A st
πŸ“Œ The Build vs. Buy Dilemma for GenAI Applications πŸ—‚ Category: STRATEGY πŸ•’ Date: 2025-01-07 | ⏱️ Read time: 6 min read A strategic guide to build vs. buy for GenAI

πŸ“Œ Easy Map Boundary Extraction with GeoPandas πŸ—‚ Category: DATA VISUALIZATION πŸ•’ Date: 2025-01-07 | ⏱️ Read time: 8 min read
πŸ“Œ Easy Map Boundary Extraction with GeoPandas πŸ—‚ Category: DATA VISUALIZATION πŸ•’ Date: 2025-01-07 | ⏱️ Read time: 8 min read How to visualize country borders with Python

πŸ“Œ All The SQL a Data Scientist Needs to Know πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2025-01-07 | ⏱️ Read time: 12 min read What
πŸ“Œ All The SQL a Data Scientist Needs to Know πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2025-01-07 | ⏱️ Read time: 12 min read What you need to know, best practices, and where you can practice your skills

πŸ“Œ Airflow Data Intervals: A Deep Dive πŸ—‚ Category: DATA ENGINEERING πŸ•’ Date: 2025-01-07 | ⏱️ Read time: 10 min read Building
πŸ“Œ Airflow Data Intervals: A Deep Dive πŸ—‚ Category: DATA ENGINEERING πŸ•’ Date: 2025-01-07 | ⏱️ Read time: 10 min read Building idempotent and re-playable data pipelines

πŸ“Œ How to Create a Customized GenAI Video in 3 Simple Steps πŸ—‚ Category: πŸ•’ Date: 2025-01-07 | ⏱️ Read time: 15 min read Put
πŸ“Œ How to Create a Customized GenAI Video in 3 Simple Steps πŸ—‚ Category: πŸ•’ Date: 2025-01-07 | ⏱️ Read time: 15 min read Put a real-world object into fully AI-generated 4D scenes with minimal effort, so that it…

πŸ“Œ Analyzing Health Surveys Made Easy with Functions in R πŸ—‚ Category: πŸ•’ Date: 2025-01-07 | ⏱️ Read time: 9 min read Solving
πŸ“Œ Analyzing Health Surveys Made Easy with Functions in R πŸ—‚ Category: πŸ•’ Date: 2025-01-07 | ⏱️ Read time: 9 min read Solving the issue of having missing data in the variables for sampling design

πŸ“Œ How to Securely Connect Microsoft Fabric to Azure Databricks SQL API πŸ—‚ Category: DATA SECURITY πŸ•’ Date: 2025-01-07 | ⏱️ R
πŸ“Œ How to Securely Connect Microsoft Fabric to Azure Databricks SQL API πŸ—‚ Category: DATA SECURITY πŸ•’ Date: 2025-01-07 | ⏱️ Read time: 10 min read Integration architecture focusing on security and access control

πŸ“Œ Customizing Your Fine-tuning Code Using HuggingFace’s Transformers Library πŸ—‚ Category: πŸ•’ Date: 2025-01-08 | ⏱️ Read time
πŸ“Œ Customizing Your Fine-tuning Code Using HuggingFace’s Transformers Library πŸ—‚ Category: πŸ•’ Date: 2025-01-08 | ⏱️ Read time: 8 min read Examples of custom callbacks and custom fine-tuning code from different libraries

πŸ“Œ Building Effective Metrics to Describe Users πŸ—‚ Category: ANALYTICS πŸ•’ Date: 2025-01-08 | ⏱️ Read time: 4 min read How can
πŸ“Œ Building Effective Metrics to Describe Users πŸ—‚ Category: ANALYTICS πŸ•’ Date: 2025-01-08 | ⏱️ Read time: 4 min read How can numerical user metrics be transformed into a personalized assessment of whether this behavior…

πŸ“Œ Linear Programming: Auxiliary Variables πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2025-01-08 | ⏱️ Read time: 15 min read Part 5:
πŸ“Œ Linear Programming: Auxiliary Variables πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2025-01-08 | ⏱️ Read time: 15 min read Part 5: Increasing LP flexibility to handle tricky logic

πŸ“Œ Statistical Learnability of Strategic Linear Classifiers: A Proof Walkthrough πŸ—‚ Category: MACHINE LEARNING πŸ•’ Date: 2025-
πŸ“Œ Statistical Learnability of Strategic Linear Classifiers: A Proof Walkthrough πŸ—‚ Category: MACHINE LEARNING πŸ•’ Date: 2025-01-08 | ⏱️ Read time: 19 min read With the help of an intricate geometric construction, we can prove that instance-wise cost functions…

πŸ“Œ RAG Isn’t Immune to LLM Hallucination πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2025-01-20 | ⏱️ Read time: 11 min read
πŸ“Œ RAG Isn’t Immune to LLM Hallucination πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2025-01-20 | ⏱️ Read time: 11 min read How to measure how much of your RAG’s output is correct

πŸ“Œ How Most Organizations Get Data Strategy Wrong – and How to Fix It πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2025-01-2
πŸ“Œ How Most Organizations Get Data Strategy Wrong – and How to Fix It πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2025-01-20 | ⏱️ Read time: 79 min read Redefining Data Strategy to Drive Competitive Advantage with Data, Analytics and AI