en
Feedback
Machine Learning

Machine Learning

Open in Telegram

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

Show more

๐Ÿ“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 40 202 subscribers, ranking 3 365 in the Technologies & Applications category and 227 in the Syria region.

๐Ÿ“Š Audience metrics and dynamics

Since its creation on ะฝะตะฒั–ะดะพะผะพ, the project has demonstrated rapid growth, gathering an audience of 40 202 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.

๐Ÿ“ Description and content policy

The author describes the resource as a platform for expressing subjective opinions:
โ€œReal Machine Learning โ€” simple, practical, and built on experience. Learn step by step with clear explanations and working code. Admin: @HusseinSheikho || @Hussein_Sheikhoโ€

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.

40 202
Subscribers
+1024 hours
+837 days
+34330 days
Posts Archive
This GitHub repository is a real treasure trove of free programming books. Here you'll find hundreds of books on topics like #AI, #blockchain, app development, #game development, #Python #webdevelopment, #promptengineering, and many more โœ‹ GitHub: https://github.com/EbookFoundation/free-programming-books https://t.me/CodeProgrammer โญ

โ€œNobody believed you could grow small capitalโ€”until I saw this.โ€ $1,000 turned into real profit before my eyes. The secret? B
โ€œNobody believed you could grow small capitalโ€”until I saw this.โ€ $1,000 turned into real profit before my eyes. The secret? Bonus fuel & copytrading with Elite Gold. Want proof? See how itโ€™s actually done before the bonus ends. #ad InsideAds

Ever wonder how real traders grow $1,000 into proven profitsโ€”step by step, with full transparency? Elite Gold Trading opens t
Ever wonder how real traders grow $1,000 into proven profitsโ€”step by step, with full transparency? Elite Gold Trading opens the door to professional copytrading, verified results, and exclusive strategies you can follow today. New members get a 100% deposit bonusโ€”start with a real edge from day one. Ready to see how the pros do it? Join now & claim your bonus before this offer ends! #ad InsideAds

๐Ÿ“Œ The Evolution of Llama: From Llama 1 to Llama 3.1 ๐Ÿ—‚ Category: ARTIFICIAL INTELLIGENCE ๐Ÿ•’ Date: 2024-09-06 | โฑ๏ธ Read time:
๐Ÿ“Œ The Evolution of Llama: From Llama 1 to Llama 3.1 ๐Ÿ—‚ Category: ARTIFICIAL INTELLIGENCE ๐Ÿ•’ Date: 2024-09-06 | โฑ๏ธ Read time: 17 min read A Comprehensive Guide to the Advancements and Innovations in the Family of Llama Models fromโ€ฆ

๐Ÿ“Œ Scaling Numerical Data, Explained: A Visual Guide with Code Examples for Beginners ๐Ÿ—‚ Category: DATA SCIENCE ๐Ÿ•’ Date: 2024
๐Ÿ“Œ Scaling Numerical Data, Explained: A Visual Guide with Code Examples for Beginners ๐Ÿ—‚ Category: DATA SCIENCE ๐Ÿ•’ Date: 2024-09-06 | โฑ๏ธ Read time: 10 min read Transforming adult-sized data for child-like models

๐Ÿ“Œ An Intuitive Introduction to Reinforcement Learning, Part I ๐Ÿ—‚ Category: MACHINE LEARNING ๐Ÿ•’ Date: 2024-09-06 | โฑ๏ธ Read ti
๐Ÿ“Œ An Intuitive Introduction to Reinforcement Learning, Part I ๐Ÿ—‚ Category: MACHINE LEARNING ๐Ÿ•’ Date: 2024-09-06 | โฑ๏ธ Read time: 21 min read Exploring popular reinforcement learning environments, in a beginner-friendly way

๐Ÿ“Œ How to Implement Graph RAG Using Knowledge Graphs and Vector Databases ๐Ÿ—‚ Category: ๐Ÿ•’ Date: 2024-09-06 | โฑ๏ธ Read time: 49
๐Ÿ“Œ How to Implement Graph RAG Using Knowledge Graphs and Vector Databases ๐Ÿ—‚ Category: ๐Ÿ•’ Date: 2024-09-06 | โฑ๏ธ Read time: 49 min read The accompanying code for this tutorial is here. My last blog post was about how to implement knowledgeโ€ฆ

๐Ÿ“Œ Heatmap for Confusion Matrix in Python ๐Ÿ—‚ Category: DATA SCIENCE ๐Ÿ•’ Date: 2024-09-06 | โฑ๏ธ Read time: 5 min read One image
๐Ÿ“Œ Heatmap for Confusion Matrix in Python ๐Ÿ—‚ Category: DATA SCIENCE ๐Ÿ•’ Date: 2024-09-06 | โฑ๏ธ Read time: 5 min read One image can be worth of thousands words.

๐Ÿ“Œ Why Ratios Trump Raw Numbers in Business Health ๐Ÿ—‚ Category: DATA SCIENCE ๐Ÿ•’ Date: 2024-09-06 | โฑ๏ธ Read time: 5 min read U
๐Ÿ“Œ Why Ratios Trump Raw Numbers in Business Health ๐Ÿ—‚ Category: DATA SCIENCE ๐Ÿ•’ Date: 2024-09-06 | โฑ๏ธ Read time: 5 min read Understanding ratios is key to unlocking deeper insights into your businessโ€™s health and driving smarterโ€ฆ

๐Ÿ“Œ Achieve Better Classification Results with ClassificationThresholdTuner ๐Ÿ—‚ Category: DATA SCIENCE ๐Ÿ•’ Date: 2024-09-07 | โฑ๏ธ
๐Ÿ“Œ Achieve Better Classification Results with ClassificationThresholdTuner ๐Ÿ—‚ Category: DATA SCIENCE ๐Ÿ•’ Date: 2024-09-07 | โฑ๏ธ Read time: 37 min read A python tool to tune and visualize the threshold choices for binary and multi-class classificationโ€ฆ

๐Ÿ“Œ Forever Learning: Why AI Struggles with Adapting to New Challenges ๐Ÿ—‚ Category: ARTIFICIAL INTELLIGENCE ๐Ÿ•’ Date: 2024-09-0
๐Ÿ“Œ Forever Learning: Why AI Struggles with Adapting to New Challenges ๐Ÿ—‚ Category: ARTIFICIAL INTELLIGENCE ๐Ÿ•’ Date: 2024-09-07 | โฑ๏ธ Read time: 16 min read Understanding the limits of deep learning and the quest for true continual adaptation

๐Ÿ“Œ From Theory to Practice with Particle Swarm Optimization, Using Python ๐Ÿ—‚ Category: ARTIFICIAL INTELLIGENCE ๐Ÿ•’ Date: 2024-
๐Ÿ“Œ From Theory to Practice with Particle Swarm Optimization, Using Python ๐Ÿ—‚ Category: ARTIFICIAL INTELLIGENCE ๐Ÿ•’ Date: 2024-09-07 | โฑ๏ธ Read time: 11 min read Hereโ€™s a tutorial on what PSO is and how to use it

๐Ÿ“Œ The Price of Gold: Is Olympic Success Reserved for the Wealthy? ๐Ÿ—‚ Category: DATA SCIENCE ๐Ÿ•’ Date: 2024-09-07 | โฑ๏ธ Read ti
๐Ÿ“Œ The Price of Gold: Is Olympic Success Reserved for the Wealthy? ๐Ÿ—‚ Category: DATA SCIENCE ๐Ÿ•’ Date: 2024-09-07 | โฑ๏ธ Read time: 15 min read Analyzing 30 years of Olympic Games medals distribution and national wealth indicators

๐Ÿ“Œ Calculating Contact ๐Ÿ—‚ Category: DATA SCIENCE ๐Ÿ•’ Date: 2024-09-07 | โฑ๏ธ Read time: 8 min read A Data-Driven Look at Alien C
๐Ÿ“Œ Calculating Contact ๐Ÿ—‚ Category: DATA SCIENCE ๐Ÿ•’ Date: 2024-09-07 | โฑ๏ธ Read time: 8 min read A Data-Driven Look at Alien Civilizations (Part 1 of the Drake Equation Series)

๐Ÿ“Œ From Stars to Life ๐Ÿ—‚ Category: ๐Ÿ•’ Date: 2024-09-07 | โฑ๏ธ Read time: 12 min read A Data-Driven Journey (Part 2 of the Drake
๐Ÿ“Œ From Stars to Life ๐Ÿ—‚ Category: ๐Ÿ•’ Date: 2024-09-07 | โฑ๏ธ Read time: 12 min read A Data-Driven Journey (Part 2 of the Drake Equation Series)

๐Ÿ“Œ How I Solved LinkedIn Queens Game Using Backtracking ๐Ÿ—‚ Category: ๐Ÿ•’ Date: 2024-09-07 | โฑ๏ธ Read time: 11 min read Using Op
๐Ÿ“Œ How I Solved LinkedIn Queens Game Using Backtracking ๐Ÿ—‚ Category: ๐Ÿ•’ Date: 2024-09-07 | โฑ๏ธ Read time: 11 min read Using OpenCV to auto-detect puzzle and redraw the final answer

๐Ÿ“Œ Understanding Einsteinโ€™s Notation and einsum Multiplication ๐Ÿ—‚ Category: ARTIFICIAL INTELLIGENCE ๐Ÿ•’ Date: 2024-09-08 | โฑ๏ธ
๐Ÿ“Œ Understanding Einsteinโ€™s Notation and einsum Multiplication ๐Ÿ—‚ Category: ARTIFICIAL INTELLIGENCE ๐Ÿ•’ Date: 2024-09-08 | โฑ๏ธ Read time: 5 min read Perform higher-order tensor operations with string notation

๐Ÿ“Œ Communicating with the Cosmos ๐Ÿ—‚ Category: ๐Ÿ•’ Date: 2024-09-08 | โฑ๏ธ Read time: 15 min read Estimating Alien Civilizations
๐Ÿ“Œ Communicating with the Cosmos ๐Ÿ—‚ Category: ๐Ÿ•’ Date: 2024-09-08 | โฑ๏ธ Read time: 15 min read Estimating Alien Civilizations (Part 3 of the Drake Equation Series)

๐Ÿ“Œ Intuitive Explanation of Async / Await in JavaScript ๐Ÿ—‚ Category: JAVASCRIPT ๐Ÿ•’ Date: 2024-09-08 | โฑ๏ธ Read time: 11 min re
๐Ÿ“Œ Intuitive Explanation of Async / Await in JavaScript ๐Ÿ—‚ Category: JAVASCRIPT ๐Ÿ•’ Date: 2024-09-08 | โฑ๏ธ Read time: 11 min read Designing asynchronous pipelines for efficient data processing

๐Ÿ“Œ Python QuickStart for People Learning AI ๐Ÿ—‚ Category: DATA SCIENCE ๐Ÿ•’ Date: 2024-09-08 | โฑ๏ธ Read time: 15 min read A begin
๐Ÿ“Œ Python QuickStart for People Learning AI ๐Ÿ—‚ Category: DATA SCIENCE ๐Ÿ•’ Date: 2024-09-08 | โฑ๏ธ Read time: 15 min read A beginner-friendly guide