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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 40 229 subscribers, ranking 3 336 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 40 229 subscribers.

According to the latest data from 04 July, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 343 over the last 30 days and by 16 over the last 24 hours, overall reach remains high.

  • Verification status: Not verified
  • Engagement rate (ER): The average audience engagement rate is 1.92%. Within the first 24 hours after publication, content typically collects 1.89% reactions from the total number of subscribers.
  • Post reach: On average, each post receives 771 views. Within the first day, a publication typically gains 761 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 05 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.

40 229
Subscribers
+1624 hours
+837 days
+34330 days
Posts Archive
πŸ“Œ Building a PubMed Dataset πŸ—‚ Category: DATA ENGINEERING πŸ•’ Date: 2024-10-31 | ⏱️ Read time: 6 min read Step-by-Step Instru
πŸ“Œ Building a PubMed Dataset πŸ—‚ Category: DATA ENGINEERING πŸ•’ Date: 2024-10-31 | ⏱️ Read time: 6 min read Step-by-Step Instructions for Constructing a Dataset of PubMed-Listed Publications on Cardiovascular Disease Research

πŸ“Œ Minimum Viable MLE πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-10-31 | ⏱️ Read time: 8 min read Building a minimal production-
πŸ“Œ Minimum Viable MLE πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-10-31 | ⏱️ Read time: 8 min read Building a minimal production-ready sentiment analysis model

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Join today and get 150% bonus! We will turn Β£100->Β£250 #ad InsideAds

πŸ“Œ Les Miserables Social Network Analysis Using Marimo Notebooks and the NetworkX Python library πŸ—‚ Category: DATA SCIENCE πŸ•’
πŸ“Œ Les Miserables Social Network Analysis Using Marimo Notebooks and the NetworkX Python library πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-10-31 | ⏱️ Read time: 16 min read Build a Marimo notebook using NetworkX Python library, uncovering the hidden structures in Victor Hugo’s…

πŸ“Œ How To Specialize In Data Science / Machine Learning πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-10-31 | ⏱️ Read ti
πŸ“Œ How To Specialize In Data Science / Machine Learning πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-10-31 | ⏱️ Read time: 8 min read Is it better to be a generalist or specialist?

πŸ“Œ LLM Evaluation, AI Side Projects, User-Friendly Data Tables, and Other October Must-Reads πŸ—‚ Category: DATA SCIENCE πŸ•’ Dat
πŸ“Œ LLM Evaluation, AI Side Projects, User-Friendly Data Tables, and Other October Must-Reads πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-10-31 | ⏱️ Read time: 4 min read The stories that resonated the most with our community in the past month

πŸ“Œ How I Improved My Productivity as a Data Scientist with Two Small Habits πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-10-31 | ⏱
πŸ“Œ How I Improved My Productivity as a Data Scientist with Two Small Habits πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-10-31 | ⏱️ Read time: 8 min read How to incorporate these habits into your daily routine

πŸ“Œ The Savant Syndrome: Is Pattern Recognition Equivalent to Intelligence? πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024
πŸ“Œ The Savant Syndrome: Is Pattern Recognition Equivalent to Intelligence? πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-10-31 | ⏱️ Read time: 15 min read Exploring the limits of artificial intelligence: why mastering patterns may not equal genuine reasoning

πŸ“Œ Game Theory, Part 2 – Nice Guys Finished First πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-10-31 | ⏱️ Read time: 8 min read It
πŸ“Œ Game Theory, Part 2 – Nice Guys Finished First πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-10-31 | ⏱️ Read time: 8 min read Iterated Prisoner’s Dilemma Shows Nice Guys Can Finish First

πŸ“Œ TIME-MOE: Billion-Scale Time Series Foundation Model with Mixture-of-Experts πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date:
πŸ“Œ TIME-MOE: Billion-Scale Time Series Foundation Model with Mixture-of-Experts πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-10-31 | ⏱️ Read time: 9 min read And open-source as well!

πŸ“Œ Awesome Plotly with Code Series (Part 3): Highlighting Bars in the Long Tails πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-11-0
πŸ“Œ Awesome Plotly with Code Series (Part 3): Highlighting Bars in the Long Tails πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-11-01 | ⏱️ Read time: 7 min read Who said that the longer tails are not important? Let’s give them a proper way…

πŸ“Œ On the Programmability of AWS Trainium and Inferentia πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-11-01 | ⏱️ Read t
πŸ“Œ On the Programmability of AWS Trainium and Inferentia πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-11-01 | ⏱️ Read time: 14 min read Accelerating AI/ML Model Training with Custom Operators – Part 4

πŸ“Œ Build and Deploy a Multi-File RAG App to the Web πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-11-01 | ⏱️ Read time: 10 min read
πŸ“Œ Build and Deploy a Multi-File RAG App to the Web πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-11-01 | ⏱️ Read time: 10 min read Part 2 – Deploying to the web using Hugging Face Spaces

πŸ“Œ Multimodal AI Search for Business Applications πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-11-01 | ⏱️ Read time: 19
πŸ“Œ Multimodal AI Search for Business Applications πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-11-01 | ⏱️ Read time: 19 min read Enabling businesses to extract real value from their data

πŸ“Œ Choosing and Implementing Hugging Face Models πŸ—‚ Category: MACHINE LEARNING πŸ•’ Date: 2024-11-01 | ⏱️ Read time: 10 min rea
πŸ“Œ Choosing and Implementing Hugging Face Models πŸ—‚ Category: MACHINE LEARNING πŸ•’ Date: 2024-11-01 | ⏱️ Read time: 10 min read Pulling pre-trained models out of the box for your use case

πŸ“Œ Unpopular Opinion: It’s Harder Than Ever to Be a Good Data Scientist πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-11-01 | ⏱️ Re
πŸ“Œ Unpopular Opinion: It’s Harder Than Ever to Be a Good Data Scientist πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-11-01 | ⏱️ Read time: 32 min read What working as a data scientist at various companies and industries over the past 6+…

πŸ“Œ Unsupervised LLM Evaluations πŸ—‚ Category: MACHINE LEARNING πŸ•’ Date: 2024-11-02 | ⏱️ Read time: 14 min read Practitioners g
πŸ“Œ Unsupervised LLM Evaluations πŸ—‚ Category: MACHINE LEARNING πŸ•’ Date: 2024-11-02 | ⏱️ Read time: 14 min read Practitioners guide to judging outputs of large language models

πŸ“Œ MOIRAI-MOE: Upgrading MOIRAI with Mixture-of-Experts for Enhanced Forecasting πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date
πŸ“Œ MOIRAI-MOE: Upgrading MOIRAI with Mixture-of-Experts for Enhanced Forecasting πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-11-02 | ⏱️ Read time: 10 min read The popular foundation time-series model just got an update!

πŸ“Œ A Simple Example Using PCA for Outlier Detection πŸ—‚ Category: πŸ•’ Date: 2024-11-02 | ⏱️ Read time: 24 min read Improve accu
πŸ“Œ A Simple Example Using PCA for Outlier Detection πŸ—‚ Category: πŸ•’ Date: 2024-11-02 | ⏱️ Read time: 24 min read Improve accuracy, speed, and memory usage by performing PCA transformation before outlier detection

πŸ“Œ Should you learn how to code in the next decade? πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-11-02 | ⏱️ Read time:
πŸ“Œ Should you learn how to code in the next decade? πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-11-02 | ⏱️ Read time: 7 min read Or will AI eat up all the software in the world?