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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 40 205 subscribers, ranking 3 352 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 40 205 subscribers.

According to the latest data from 02 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 10 over the last 24 hours, overall reach remains high.

  • Verification status: Not verified
  • Engagement rate (ER): The average audience engagement rate is 1.99%. Within the first 24 hours after publication, content typically collects 2.28% reactions from the total number of subscribers.
  • Post reach: On average, each post receives 800 views. Within the first day, a publication typically gains 915 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 03 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.

40 205
Subscribers
+1024 hours
+837 days
+34330 days
Posts Archive
πŸ“Œ Graph RAG into Production – step-by-step πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-09-23 | ⏱️ Read time: 17 min r
πŸ“Œ Graph RAG into Production – step-by-step πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-09-23 | ⏱️ Read time: 17 min read A GCP native, fully serverless implementation that you will replicate in minutes

πŸ“Œ Semantic Layer for the People and by the People πŸ—‚ Category: πŸ•’ Date: 2024-09-23 | ⏱️ Read time: 14 min read My 3 +1 joker
πŸ“Œ Semantic Layer for the People and by the People πŸ—‚ Category: πŸ•’ Date: 2024-09-23 | ⏱️ Read time: 14 min read My 3 +1 jokers with templates for building a powerful analytical semantic layer

πŸ“Œ Zero-Shot Localization with CLIP-Style Encoders πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-09-24 | ⏱️ Read time: 1
πŸ“Œ Zero-Shot Localization with CLIP-Style Encoders πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-09-24 | ⏱️ Read time: 11 min read How can we see what a vision encoder sees?

πŸ“Œ A Deep Dive into Odds Ratio πŸ—‚ Category: STATISTICS πŸ•’ Date: 2024-09-24 | ⏱️ Read time: 20 min read Understanding, calcula
πŸ“Œ A Deep Dive into Odds Ratio πŸ—‚ Category: STATISTICS πŸ•’ Date: 2024-09-24 | ⏱️ Read time: 20 min read Understanding, calculating, visualizing, and interpreting odds ratios and their confidence intervals with practical examples in…

πŸ“Œ Building an Interactive UI for Llamaindex Workflows πŸ—‚ Category: πŸ•’ Date: 2024-09-24 | ⏱️ Read time: 11 min read A guide t
πŸ“Œ Building an Interactive UI for Llamaindex Workflows πŸ—‚ Category: πŸ•’ Date: 2024-09-24 | ⏱️ Read time: 11 min read A guide to integrating human-in-the-loop interactions using Llamaindex, FastAPI, and Streamlit

πŸ“Œ Feature Engineering Techniques for Numerical Variables in Python πŸ—‚ Category: MACHINE LEARNING πŸ•’ Date: 2024-09-24 | ⏱️ Re
πŸ“Œ Feature Engineering Techniques for Numerical Variables in Python πŸ—‚ Category: MACHINE LEARNING πŸ•’ Date: 2024-09-24 | ⏱️ Read time: 21 min read Learn the most useful feature engineering techniques to convert numerical values ​​into useful information for…

πŸ“Œ I’ve hired 3 cohorts of data science interns – here’s my advice on getting an offer πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 202
πŸ“Œ I’ve hired 3 cohorts of data science interns – here’s my advice on getting an offer πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-09-24 | ⏱️ Read time: 16 min read Resume and interview tips for landing a data science internship

πŸ“Œ Doctors Leverage Multimodal Data; Medical AI Should Too πŸ—‚ Category: πŸ•’ Date: 2024-09-25 | ⏱️ Read time: 11 min read Integ
πŸ“Œ Doctors Leverage Multimodal Data; Medical AI Should Too πŸ—‚ Category: πŸ•’ Date: 2024-09-25 | ⏱️ Read time: 11 min read Integrating multimodal data enables a new generation of medical AI systems to better capture doctor’s…

πŸ“Œ Water Cooler Small Talk: The Birthday Paradox πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-09-25 | ⏱️ Read time: 9 min read A l
πŸ“Œ Water Cooler Small Talk: The Birthday Paradox πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-09-25 | ⏱️ Read time: 9 min read A look at the counterintuitive mathematics of shared birthdays

πŸ“Œ Convenient Time Series Forecasting with sktime πŸ—‚ Category: MACHINE LEARNING πŸ•’ Date: 2024-09-25 | ⏱️ Read time: 8 min rea
πŸ“Œ Convenient Time Series Forecasting with sktime πŸ—‚ Category: MACHINE LEARNING πŸ•’ Date: 2024-09-25 | ⏱️ Read time: 8 min read How to make forecasting as easy as a walk in the park

πŸ“Œ Exposing Jailbreak Vulnerabilities in LLM Applications with ARTKIT πŸ—‚ Category: MACHINE LEARNING πŸ•’ Date: 2024-09-25 | ⏱️
πŸ“Œ Exposing Jailbreak Vulnerabilities in LLM Applications with ARTKIT πŸ—‚ Category: MACHINE LEARNING πŸ•’ Date: 2024-09-25 | ⏱️ Read time: 10 min read Automated prompt-based testing to extract hidden passwords in the popular Gandalf challenge

πŸ“Œ How Cohort Analysis Can Transform Your Customer Insights πŸ—‚ Category: πŸ•’ Date: 2024-09-25 | ⏱️ Read time: 6 min read Disco
πŸ“Œ How Cohort Analysis Can Transform Your Customer Insights πŸ—‚ Category: πŸ•’ Date: 2024-09-25 | ⏱️ Read time: 6 min read Discover how tracking customer behavior over time with cohort analysis can improve engagement and retention…

πŸ“Œ I Spent My Money on Benchmarking LLMs on Dutch Exams So You Don’t Have To πŸ—‚ Category: LARGE LANGUAGE MODELS πŸ•’ Date: 2024
πŸ“Œ I Spent My Money on Benchmarking LLMs on Dutch Exams So You Don’t Have To πŸ—‚ Category: LARGE LANGUAGE MODELS πŸ•’ Date: 2024-09-25 | ⏱️ Read time: 12 min read OpenAI’s new o1-preview is way too expensive for how it performs on the results

πŸ“Œ VisionTS: Building Superior Forecasting Models from Images πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-09-26 | ⏱️ R
πŸ“Œ VisionTS: Building Superior Forecasting Models from Images πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-09-26 | ⏱️ Read time: 9 min read Leveraging the power of images for time-series forecasting

πŸ“Œ Simulate the Challenges of a Circular Economy for Fashion Retail πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-09-26 | ⏱️ Read t
πŸ“Œ Simulate the Challenges of a Circular Economy for Fashion Retail πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-09-26 | ⏱️ Read time: 17 min read Use data analytics to simulate a circular rental model for fashion retail and understand store…

πŸ“Œ MCP in Practice πŸ—‚ Category: AGENTIC AI πŸ•’ Date: 2025-09-29 | ⏱️ Read time: 14 min read Mapping power, concentration, and
πŸ“Œ MCP in Practice πŸ—‚ Category: AGENTIC AI πŸ•’ Date: 2025-09-29 | ⏱️ Read time: 14 min read Mapping power, concentration, and usage in the emerging AI developer ecosystem

πŸ“Œ I Made My AI Model 84% Smaller and It Got Better, Not Worse πŸ—‚ Category: LARGE LANGUAGE MODELS πŸ•’ Date: 2025-09-29 | ⏱️ Re
πŸ“Œ I Made My AI Model 84% Smaller and It Got Better, Not Worse πŸ—‚ Category: LARGE LANGUAGE MODELS πŸ•’ Date: 2025-09-29 | ⏱️ Read time: 20 min read The counterintuitive approach to AI optimization that’s changing how we deploy models

πŸ“Œ Preparing Video Data for Deep Learning: Introducing Vid Prepper πŸ—‚ Category: MACHINE LEARNING πŸ•’ Date: 2025-09-29 | ⏱️ Rea
πŸ“Œ Preparing Video Data for Deep Learning: Introducing Vid Prepper πŸ—‚ Category: MACHINE LEARNING πŸ•’ Date: 2025-09-29 | ⏱️ Read time: 13 min read A guide to fast video data preprocessing for machine learning

πŸ“Œ Dummy Regressor, Explained: A Visual Guide with Code Examples for Beginners πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-09-26
πŸ“Œ Dummy Regressor, Explained: A Visual Guide with Code Examples for Beginners πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-09-26 | ⏱️ Read time: 7 min read Naively choosing the best number for all of your prediction

πŸ“Œ Working with Embeddings: Closed versus Open Source πŸ—‚ Category: MACHINE LEARNING πŸ•’ Date: 2024-09-26 | ⏱️ Read time: 20 mi
πŸ“Œ Working with Embeddings: Closed versus Open Source πŸ—‚ Category: MACHINE LEARNING πŸ•’ Date: 2024-09-26 | ⏱️ Read time: 20 min read Using techniques to improve semantic search