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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 40 273 subscribers, ranking 3 347 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 40 273 subscribers.

According to the latest data from 07 July, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 352 over the last 30 days and by 21 over the last 24 hours, overall reach remains high.

  • Verification status: Not verified
  • Engagement rate (ER): The average audience engagement rate is 2.23%. 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 896 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 08 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.

40 273
Subscribers
+2124 hours
+957 days
+35230 days
Posts Archive
πŸ“Œ Introducing n-Step Temporal-Difference Methods πŸ—‚ Category: MACHINE LEARNING πŸ•’ Date: 2024-12-29 | ⏱️ Read time: 10 min re
πŸ“Œ Introducing n-Step Temporal-Difference Methods πŸ—‚ Category: MACHINE LEARNING πŸ•’ Date: 2024-12-29 | ⏱️ Read time: 10 min read Dissecting β€œReinforcement Learning” by Richard S. Sutton with custom Python implementations, Episode V

πŸ“Œ I Combined the Blockchain and AI to Generate Art. Here’s What Happened Next. πŸ—‚ Category: BLOCKCHAIN πŸ•’ Date: 2024-12-30 |
πŸ“Œ I Combined the Blockchain and AI to Generate Art. Here’s What Happened Next. πŸ—‚ Category: BLOCKCHAIN πŸ•’ Date: 2024-12-30 | ⏱️ Read time: 8 min read Using LLMs to create artistic representations of data

πŸ“Œ How to Build a Graph RAG App πŸ—‚ Category: πŸ•’ Date: 2024-12-30 | ⏱️ Read time: 30 min read The accompanying code for the ap
πŸ“Œ How to Build a Graph RAG App πŸ—‚ Category: πŸ•’ Date: 2024-12-30 | ⏱️ Read time: 30 min read The accompanying code for the app and notebook are here. Knowledge graphs (KGs) and Large Language…

πŸ“Œ How to Build a Resume Optimizer with AI πŸ—‚ Category: πŸ•’ Date: 2024-12-30 | ⏱️ Read time: 7 min read Step-by-step guide wit
πŸ“Œ How to Build a Resume Optimizer with AI πŸ—‚ Category: πŸ•’ Date: 2024-12-30 | ⏱️ Read time: 7 min read Step-by-step guide with example Python code

πŸ“Œ Mastering Model Uncertainty: Thresholding Techniques in Deep Learning πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-12-30 | ⏱️ R
πŸ“Œ Mastering Model Uncertainty: Thresholding Techniques in Deep Learning πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-12-30 | ⏱️ Read time: 7 min read A few words on thresholding, the softmax activation function, introducing an extra label, and considerations…

πŸ“Œ From Default Python Line Chart to Journal-Quality Infographics πŸ—‚ Category: ANALYTICS πŸ•’ Date: 2024-12-30 | ⏱️ Read time:
πŸ“Œ From Default Python Line Chart to Journal-Quality Infographics πŸ—‚ Category: ANALYTICS πŸ•’ Date: 2024-12-30 | ⏱️ Read time: 3 min read Transform boring default Matplotlib line charts into stunning, customized visualizations

πŸ“Œ The Key to Smarter Models: Tracking Feature Histories πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-12-31 | ⏱️ Read t
πŸ“Œ The Key to Smarter Models: Tracking Feature Histories πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-12-31 | ⏱️ Read time: 10 min read Capture context and improve predictions with historical data

πŸ“Œ The Math Behind In-Context Learning πŸ—‚ Category: LARGE LANGUAGE MODELS πŸ•’ Date: 2024-12-31 | ⏱️ Read time: 6 min read From
πŸ“Œ The Math Behind In-Context Learning πŸ—‚ Category: LARGE LANGUAGE MODELS πŸ•’ Date: 2024-12-31 | ⏱️ Read time: 6 min read From attention to gradient descent: unraveling how transformers learn from examples

πŸ“Œ Creating SMOTE Oversampling from Scratch πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-12-31 | ⏱️ Read time: 8 min read A Python
πŸ“Œ Creating SMOTE Oversampling from Scratch πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-12-31 | ⏱️ Read time: 8 min read A Python tutorial on how to implement oversampling and how to make custom variations

πŸ“Œ Top 12 Skills Data Scientists Need to Succeed in 2025 πŸ—‚ Category: CAREER ADVICE πŸ•’ Date: 2024-12-31 | ⏱️ Read time: 27 mi
πŸ“Œ Top 12 Skills Data Scientists Need to Succeed in 2025 πŸ—‚ Category: CAREER ADVICE πŸ•’ Date: 2024-12-31 | ⏱️ Read time: 27 min read It’s (not) all about LLMs and AI tools

πŸ“Œ Multi-Agentic RAG with Hugging Face Code Agents πŸ—‚ Category: πŸ•’ Date: 2024-12-31 | ⏱️ Read time: 80 min read Using Qwen2.5
πŸ“Œ Multi-Agentic RAG with Hugging Face Code Agents πŸ—‚ Category: πŸ•’ Date: 2024-12-31 | ⏱️ Read time: 80 min read Using Qwen2.5-7B-Instruct powered code agents to create a local, open source, multi-agentic RAG system

πŸ“Œ Chi-Squared Test: Comparing Variations Through Soccer πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2025-01-01 | ⏱️ Read time: 13 min
πŸ“Œ Chi-Squared Test: Comparing Variations Through Soccer πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2025-01-01 | ⏱️ Read time: 13 min read Understanding Different Types of Chi-Squared Tests: A/B Testing for Data Science Series (8)

πŸ“Œ Transforming Data into Solutions: Building a Smart App with Python and AI πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 20
πŸ“Œ Transforming Data into Solutions: Building a Smart App with Python and AI πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2025-01-01 | ⏱️ Read time: 13 min read Some financial analysts worry that artificial intelligence may not justify the massive investments being made…

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πŸ“Œ Mastering Sensor Fusion: Color Image Obstacle Detection with KITTI Data – Part 2 πŸ—‚ Category: DEEP LEARNING πŸ•’ Date: 2025-
πŸ“Œ Mastering Sensor Fusion: Color Image Obstacle Detection with KITTI Data – Part 2 πŸ—‚ Category: DEEP LEARNING πŸ•’ Date: 2025-01-01 | ⏱️ Read time: 26 min read How to use Color Image data for object detection in the context of obstacle detection

πŸ“Œ Scaling Statistics: Incremental Standard Deviation in SQL with dbt πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2025-01-01 | ⏱️ Read
πŸ“Œ Scaling Statistics: Incremental Standard Deviation in SQL with dbt πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2025-01-01 | ⏱️ Read time: 7 min read Why scan yesterday’s data when you can increment today’s?

πŸ“Œ AI-Powered Information Extraction and Matchmaking πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2025-01-01 | ⏱️ Read time:
πŸ“Œ AI-Powered Information Extraction and Matchmaking πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2025-01-01 | ⏱️ Read time: 34 min read Developing an application for extracting key profile information from CVs and recommending jobs aligned with…

πŸ“Œ Mastering the Basics: How Linear Regression Unlocks the Secrets of Complex Models πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’
πŸ“Œ Mastering the Basics: How Linear Regression Unlocks the Secrets of Complex Models πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2025-01-01 | ⏱️ Read time: 12 min read Full explanation on Linear Regression and how it learns

πŸ“Œ 5 Simple Projects to Start Today: A Learning Roadmap for Data Engineering πŸ—‚ Category: DATA ENGINEERING πŸ•’ Date: 2025-01-0
πŸ“Œ 5 Simple Projects to Start Today: A Learning Roadmap for Data Engineering πŸ—‚ Category: DATA ENGINEERING πŸ•’ Date: 2025-01-02 | ⏱️ Read time: 11 min read Start with 5 practical projects to lay the foundation for your data engineering roadmap.

πŸ“Œ How to Process 10k Images in Seconds πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2025-01-02 | ⏱️ Read time: 7 min read Efficient im
πŸ“Œ How to Process 10k Images in Seconds πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2025-01-02 | ⏱️ Read time: 7 min read Efficient image operations with multiprocessing in Python