en
Feedback
Machine Learning

Machine Learning

Open in Telegram

Real Machine Learning β€” simple, practical, and built on experience. Learn step by step with clear explanations and working code. Admin: @HusseinSheikho || @Hussein_Sheikho

Show more

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 40 149 subscribers, ranking 3 375 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 40 149 subscribers.

According to the latest data from 28 June, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 378 over the last 30 days and by 7 over the last 24 hours, overall reach remains high.

  • Verification status: Not verified
  • Engagement rate (ER): The average audience engagement rate is 2.09%. Within the first 24 hours after publication, content typically collects 1.91% reactions from the total number of subscribers.
  • Post reach: On average, each post receives 841 views. Within the first day, a publication typically gains 766 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 29 June, 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.

40 149
Subscribers
+724 hours
+1147 days
+37830 days
Posts Archive
πŸ“Œ System Design: Load Balancer πŸ—‚ Category: πŸ•’ Date: 2024-06-28 | ⏱️ Read time: 9 min read Orchestrating strategies for opti
πŸ“Œ System Design: Load Balancer πŸ—‚ Category: πŸ•’ Date: 2024-06-28 | ⏱️ Read time: 9 min read Orchestrating strategies for optimal workload distribution in microservice applications

πŸ“Œ Demonstrating Prioritization Effectiveness in Sales πŸ—‚ Category: πŸ•’ Date: 2024-06-28 | ⏱️ Read time: 6 min read The power
πŸ“Œ Demonstrating Prioritization Effectiveness in Sales πŸ—‚ Category: πŸ•’ Date: 2024-06-28 | ⏱️ Read time: 6 min read The power of machine learning for contact priorization

πŸ“Œ 5 Habits That Made Me A Data Scientist πŸ—‚ Category: CODING πŸ•’ Date: 2024-06-28 | ⏱️ Read time: 7 min read Advice and tips
πŸ“Œ 5 Habits That Made Me A Data Scientist πŸ—‚ Category: CODING πŸ•’ Date: 2024-06-28 | ⏱️ Read time: 7 min read Advice and tips on becoming a data scientist

πŸ“Œ Different Ways of Image Generation with Stable Diffusion 3 πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-06-28 | ⏱️ Read time: 8
πŸ“Œ Different Ways of Image Generation with Stable Diffusion 3 πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-06-28 | ⏱️ Read time: 8 min read Running SD3 in Google Colab and on a Local PC

πŸ“Œ Modeling the Extinction of the Catalan Language πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-06-28 | ⏱️ Read time: 10 min read
πŸ“Œ Modeling the Extinction of the Catalan Language πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-06-28 | ⏱️ Read time: 10 min read Applying existing literature to a practical case

πŸ“Œ Using OpenAI and PandasAI for Series Operations πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-06-29 | ⏱️ Read time: 6
πŸ“Œ Using OpenAI and PandasAI for Series Operations πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-06-29 | ⏱️ Read time: 6 min read Incorporate natural language queries and operations into your Python data cleaning workflow.

πŸ“Œ Explainability, Interpretability and Observability in Machine Learning πŸ—‚ Category: MACHINE LEARNING πŸ•’ Date: 2024-06-30 |
πŸ“Œ Explainability, Interpretability and Observability in Machine Learning πŸ—‚ Category: MACHINE LEARNING πŸ•’ Date: 2024-06-30 | ⏱️ Read time: 7 min read These are terms commonly used to describe the transparency of a model, but what do…

Over $20 BILLION wiped out, 1M+ traders erased in hours. Why did no one warn you? I switched from daily trading to arbitrage
Over $20 BILLION wiped out, 1M+ traders erased in hours. Why did no one warn you? I switched from daily trading to arbitrage right before the chaosβ€”here’s the signal everyone’s missing. Are you ready to stop gambling? Find out how #ad InsideAds

πŸ“Œ 3 Essential Questions to Address When Building an API-Involved Incremental Data Loading Script πŸ—‚ Category: DATA ENGINEERI
πŸ“Œ 3 Essential Questions to Address When Building an API-Involved Incremental Data Loading Script πŸ—‚ Category: DATA ENGINEERING πŸ•’ Date: 2024-06-30 | ⏱️ Read time: 9 min read This article explains both the conceptual framework and practical code implementation for syncing data from…

πŸ“Œ Learn Transformer Fine-Tuning and Segment Anything πŸ—‚ Category: MACHINE LEARNING πŸ•’ Date: 2024-06-30 | ⏱️ Read time: 13 mi
πŸ“Œ Learn Transformer Fine-Tuning and Segment Anything πŸ—‚ Category: MACHINE LEARNING πŸ•’ Date: 2024-06-30 | ⏱️ Read time: 13 min read Train Meta’s SAM to segment high fidelity masks for any domain

πŸ“Œ Analyzing the Pros and Cons of Electric Vehicle Purchases: Insights from Newspaper News πŸ—‚ Category: DATA SCIENCE πŸ•’ Date:
πŸ“Œ Analyzing the Pros and Cons of Electric Vehicle Purchases: Insights from Newspaper News πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-07-01 | ⏱️ Read time: 10 min read In today’s market, buying electric cars represents an important challenge and a purchase decision that…

πŸ“Œ Mastering SQL Optimization: From Functional to Efficient Queries πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-07-01 | ⏱️ Read t
πŸ“Œ Mastering SQL Optimization: From Functional to Efficient Queries πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-07-01 | ⏱️ Read time: 11 min read Six Simple Yet Effective SQL Tips That Helped Me Reduce 50 Hours of Snowflake Query…

πŸ“Œ Chart Wars: Pie Chart vs. Sorted Radial Bar Chart πŸ—‚ Category: DATA VISUALIZATION πŸ•’ Date: 2024-07-01 | ⏱️ Read time: 8 mi
πŸ“Œ Chart Wars: Pie Chart vs. Sorted Radial Bar Chart πŸ—‚ Category: DATA VISUALIZATION πŸ•’ Date: 2024-07-01 | ⏱️ Read time: 8 min read Have your pie and eat it too!

πŸ“Œ You Don’t Know Jacc(ard) πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-07-01 | ⏱️ Read time: 11 min read When the Jaccard simila
πŸ“Œ You Don’t Know Jacc(ard) πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-07-01 | ⏱️ Read time: 11 min read When the Jaccard similarity index isn’t the right tool for the job, and what to…

πŸ“Œ Framework for Success Metrics Questions | Facebook Groups Success Metrics πŸ—‚ Category: META πŸ•’ Date: 2024-07-01 | ⏱️ Read
πŸ“Œ Framework for Success Metrics Questions | Facebook Groups Success Metrics πŸ—‚ Category: META πŸ•’ Date: 2024-07-01 | ⏱️ Read time: 6 min read The framework that will help you ace upcoming interviews

πŸ“Œ Using a JSON Agent with LangChain, LangSmith and OpenAI’s GPT-4o πŸ—‚ Category: πŸ•’ Date: 2024-07-01 | ⏱️ Read time: 9 min re
πŸ“Œ Using a JSON Agent with LangChain, LangSmith and OpenAI’s GPT-4o πŸ—‚ Category: πŸ•’ Date: 2024-07-01 | ⏱️ Read time: 9 min read Developing a chatbot to answer questions about a JSON dataset

πŸ“Œ How to Turn Your AI Idea Into a Scalable Product: A Technical Guide πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-07-
πŸ“Œ How to Turn Your AI Idea Into a Scalable Product: A Technical Guide πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-07-02 | ⏱️ Read time: 9 min read Time to leave localhost behind and start acquiring users

πŸ“Œ Introduction to Linear Programming – Part II πŸ—‚ Category: CODING πŸ•’ Date: 2024-07-02 | ⏱️ Read time: 16 min read Productio
πŸ“Œ Introduction to Linear Programming – Part II πŸ—‚ Category: CODING πŸ•’ Date: 2024-07-02 | ⏱️ Read time: 16 min read Production optimisation with R

πŸ“Œ RFM Segmentation: Unleashing Customer Insights πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-07-02 | ⏱️ Read time: 9 min read Tr
πŸ“Œ RFM Segmentation: Unleashing Customer Insights πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-07-02 | ⏱️ Read time: 9 min read Transforming customer data into actionable insights with RFM segmentation

πŸ“Œ Continual Learning – A Deep Dive Into Elastic Weight Consolidation Loss πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-07-02 | ⏱️
πŸ“Œ Continual Learning – A Deep Dive Into Elastic Weight Consolidation Loss πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-07-02 | ⏱️ Read time: 10 min read With PyTorch Implementation