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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 40 208 subscribers, ranking 3 344 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 40 208 subscribers.

According to the latest data from 03 July, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 338 over the last 30 days and by 9 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 2.42% reactions from the total number of subscribers.
  • Post reach: On average, each post receives 822 views. Within the first day, a publication typically gains 973 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 04 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.

40 208
Subscribers
+924 hours
+727 days
+33830 days
Posts Archive
πŸ“Œ How to Handle Imbalanced Datasets in Machine Learning Projects πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-10-03 | ⏱️ Read tim
πŸ“Œ How to Handle Imbalanced Datasets in Machine Learning Projects πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-10-03 | ⏱️ Read time: 10 min read Techniques to handle imbalanced datasets, examples, and Python snippets

πŸ“Œ Efficient Testing of ETL Pipelines with Python πŸ—‚ Category: DATA ENGINEERING πŸ•’ Date: 2024-10-03 | ⏱️ Read time: 11 min re
πŸ“Œ Efficient Testing of ETL Pipelines with Python πŸ—‚ Category: DATA ENGINEERING πŸ•’ Date: 2024-10-03 | ⏱️ Read time: 11 min read How to Instantly Detect Data Quality Issues and Identify their Causes

πŸ“Œ Exploring How the New OpenAI Realtime API Simplifies Voice Agent Flows πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-
πŸ“Œ Exploring How the New OpenAI Realtime API Simplifies Voice Agent Flows πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-10-03 | ⏱️ Read time: 9 min read Setting up a Voice Agent using Twilio and the OpenAI Realtime API

πŸ“Œ How to succeed with AI: Combining Kafka and AI Guardrails πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-10-03 | ⏱️ Read time: 5
πŸ“Œ How to succeed with AI: Combining Kafka and AI Guardrails πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-10-03 | ⏱️ Read time: 5 min read Why real-time data and governance are non-negotiable for AI

πŸ“Œ Graph RAG, Automated Prompt Engineering, Agent Frameworks, and Other September Must-Reads πŸ—‚ Category: DATA SCIENCE πŸ•’ Dat
πŸ“Œ Graph RAG, Automated Prompt Engineering, Agent Frameworks, and Other September Must-Reads πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-10-03 | ⏱️ Read time: 4 min read The stories that resonated the most with our community in the past month

πŸ“Œ Who Really Owns the Airbnbs You’re Booking? – Marketing Perception vs Data Analytics Reality πŸ—‚ Category: DATA SCIENCE πŸ•’
πŸ“Œ Who Really Owns the Airbnbs You’re Booking? – Marketing Perception vs Data Analytics Reality πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-10-03 | ⏱️ Read time: 9 min read Over the last 20 years, Airbnb has spent billions on brand building as the authentic,…

πŸ“Œ What You Need to Know Before Switching to a Data Science Career in 2024 πŸ—‚ Category: CAREER ADVICE πŸ•’ Date: 2024-10-03 | ⏱
πŸ“Œ What You Need to Know Before Switching to a Data Science Career in 2024 πŸ—‚ Category: CAREER ADVICE πŸ•’ Date: 2024-10-03 | ⏱️ Read time: 13 min read How the market has changed (and the roadmap I’d follow if I started my journey…

πŸ“Œ Data Architecture: Lessons Learned πŸ—‚ Category: DATA ENGINEERING πŸ•’ Date: 2024-10-04 | ⏱️ Read time: 13 min read Three imp
πŸ“Œ Data Architecture: Lessons Learned πŸ—‚ Category: DATA ENGINEERING πŸ•’ Date: 2024-10-04 | ⏱️ Read time: 13 min read Three important lessons I have learned on my journey as data engineer and architect

πŸ“Œ Stop Guessing and Measure Your RAG System to Drive Real Improvements πŸ—‚ Category: πŸ•’ Date: 2024-10-04 | ⏱️ Read time: 30 m
πŸ“Œ Stop Guessing and Measure Your RAG System to Drive Real Improvements πŸ—‚ Category: πŸ•’ Date: 2024-10-04 | ⏱️ Read time: 30 min read Key metrics and techniques to elevate your retrieval-augmented generation performance

πŸ“Œ Advanced Techniques in Lying using Data Visualizations πŸ—‚ Category: DATA VISUALIZATION πŸ•’ Date: 2024-10-04 | ⏱️ Read time:
πŸ“Œ Advanced Techniques in Lying using Data Visualizations πŸ—‚ Category: DATA VISUALIZATION πŸ•’ Date: 2024-10-04 | ⏱️ Read time: 11 min read Discover the power of chart design to manipulate an audience, whatever the narrative

πŸ“Œ Dynamic GitHub Pages – Panel (pyodide-worker) πŸ—‚ Category: PROGRAMMING πŸ•’ Date: 2024-10-04 | ⏱️ Read time: 12 min read How
πŸ“Œ Dynamic GitHub Pages – Panel (pyodide-worker) πŸ—‚ Category: PROGRAMMING πŸ•’ Date: 2024-10-04 | ⏱️ Read time: 12 min read How do you create a dynamic and client-side GitHub Pages ? The first stone in an…

πŸ“Œ Four Takeaways from a 5-Year Journey πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-10-04 | ⏱️ Read time: 6 min read N
πŸ“Œ Four Takeaways from a 5-Year Journey πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-10-04 | ⏱️ Read time: 6 min read Navigating the post-graduation learnings

πŸ“Œ Running visibility analysis in QGIS πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-10-04 | ⏱️ Read time: 10 min read One of my al
πŸ“Œ Running visibility analysis in QGIS πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-10-04 | ⏱️ Read time: 10 min read One of my all-time favourite types of spatial analysis is visibility analysis. It’s a really…

πŸ“Œ Prompt Caching in LLMs: Intuition πŸ—‚ Category: DEEP LEARNING πŸ•’ Date: 2024-10-04 | ⏱️ Read time: 4 min read A brief tour o
πŸ“Œ Prompt Caching in LLMs: Intuition πŸ—‚ Category: DEEP LEARNING πŸ•’ Date: 2024-10-04 | ⏱️ Read time: 4 min read A brief tour of how caching works in attention-based models

πŸ“Œ Making Text Data AI-Ready πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-10-04 | ⏱️ Read time: 8 min read An introduct
πŸ“Œ Making Text Data AI-Ready πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-10-04 | ⏱️ Read time: 8 min read An introduction using no-code solutions

πŸ“Œ AI-Powered Corrosion Detection for Industrial Equipment: A Scalable Approach with AWS πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2
πŸ“Œ AI-Powered Corrosion Detection for Industrial Equipment: A Scalable Approach with AWS πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-10-05 | ⏱️ Read time: 6 min read A Complete AWS ML Solution with SageMaker, Lambda, and API Gateway

πŸ“Œ 5 Amazing Plugins for an Eye-Catching Visual Studio Code UI πŸ—‚ Category: PROGRAMMING πŸ•’ Date: 2024-10-05 | ⏱️ Read time: 7
πŸ“Œ 5 Amazing Plugins for an Eye-Catching Visual Studio Code UI πŸ—‚ Category: PROGRAMMING πŸ•’ Date: 2024-10-05 | ⏱️ Read time: 7 min read Make your code shine by transforming the IDE into a stunning coding environment

πŸ“Œ What Clients Really Ask for in AI Projects πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2025-09-27 | ⏱️ Read time: 7 min
πŸ“Œ What Clients Really Ask for in AI Projects πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2025-09-27 | ⏱️ Read time: 7 min read Managing AI projects is no walk in the park, but you have the power to…

πŸ“Œ Learning Triton One Kernel At a Time: Vector Addition πŸ—‚ Category: DEEP LEARNING πŸ•’ Date: 2025-09-27 | ⏱️ Read time: 9 min
πŸ“Œ Learning Triton One Kernel At a Time: Vector Addition πŸ—‚ Category: DEEP LEARNING πŸ•’ Date: 2025-09-27 | ⏱️ Read time: 9 min read The basics of GPU programming, optimisation, and your first Triton kernel

πŸ“Œ RAG 101: Chunking Strategies πŸ—‚ Category: MACHINE LEARNING πŸ•’ Date: 2024-10-05 | ⏱️ Read time: 13 min read Why, When, and
πŸ“Œ RAG 101: Chunking Strategies πŸ—‚ Category: MACHINE LEARNING πŸ•’ Date: 2024-10-05 | ⏱️ Read time: 13 min read Why, When, and How to chunk for enhanced RAG. Build intuition to develop chunking strategies.