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 40 205 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
🌍 Work Abroad for Skilled Construction Workers! Salary: $450–700 per month βœ… Free accommodation βœ… Free meals βœ… Official 1-ye
🌍 Work Abroad for Skilled Construction Workers! Salary: $450–700 per month βœ… Free accommodation βœ… Free meals βœ… Official 1-year work contract πŸ“Œ Open positions: β€’ Tilers β€’ Painters / Plasterers β€’ Bricklayers β€’ Facade Workers β€’ Plumbers β€’ Electricians πŸ’‘ Experience required! πŸ“² Apply now #ad InsideAds

β€œI deposited $1,000 and saw my trading capital DOUBLE before I even placed my first trade.” Want to know how this bonus trick
β€œI deposited $1,000 and saw my trading capital DOUBLE before I even placed my first trade.” Want to know how this bonus trick works and who else is secretly using it? Watch the real results from Elite Gold Trading πŸ‘‰ right here β€” hurry, this offer won’t wait. #ad InsideAds

I thought I’d read every secret manga out there… but last night I stumbled onto a title so wild it blew my mind. I can’t beli
I thought I’d read every secret manga out there… but last night I stumbled onto a title so wild it blew my mind. I can’t believe no one is talking about it. Want to know the name? Find it right here before it disappears. #ad InsideAds

πŸ“Œ Uncertainty in Markov Decisions Processes: a Robust Linear Programming approach πŸ—‚ Category: MATH πŸ•’ Date: 2024-09-18 | ⏱️
πŸ“Œ Uncertainty in Markov Decisions Processes: a Robust Linear Programming approach πŸ—‚ Category: MATH πŸ•’ Date: 2024-09-18 | ⏱️ Read time: 8 min read Theoretical derivation of the Robust Counterpart of Markov Decision Processes (MDPs) as a Linear Program…

πŸ“Œ Principal Component Analysis – Hands-On Tutorial πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-09-18 | ⏱️ Read time: 13 min read
πŸ“Œ Principal Component Analysis – Hands-On Tutorial πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-09-18 | ⏱️ Read time: 13 min read Dimensionality reduction through Principal Component Analysis (PCA).

β€œI never thought a $1,000 account could grow like thisβ€”until I saw how the Elite Gold Trading community does it every day!” M
β€œI never thought a $1,000 account could grow like thisβ€”until I saw how the Elite Gold Trading community does it every day!” Most traders lose by chasing quick wins. The real secret? Consistent, low-risk profits. Ready to see proof? Check this right now β€” don’t let others get ahead of you! #ad InsideAds

πŸ“Œ A Visual Exploration of Semantic Text Chunking πŸ—‚ Category: NATURAL LANGUAGE PROCESSING πŸ•’ Date: 2024-09-19 | ⏱️ Read time
πŸ“Œ A Visual Exploration of Semantic Text Chunking πŸ—‚ Category: NATURAL LANGUAGE PROCESSING πŸ•’ Date: 2024-09-19 | ⏱️ Read time: 22 min read Use embeddings and visualization tools to split text into meaningful chunks

πŸ“Œ Emerging Tech Is Nothing Without Methodology πŸ—‚ Category: ANALYTICS πŸ•’ Date: 2024-09-19 | ⏱️ Read time: 6 min read Or: a H
πŸ“Œ Emerging Tech Is Nothing Without Methodology πŸ—‚ Category: ANALYTICS πŸ•’ Date: 2024-09-19 | ⏱️ Read time: 6 min read Or: a Hundred Ways to Solve a Complex Problem

πŸ“Œ A Closer Look at Scipy’s Stats module – Part 1 πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-09-19 | ⏱️ Read time: 7 min read Le
πŸ“Œ A Closer Look at Scipy’s Stats module – Part 1 πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-09-19 | ⏱️ Read time: 7 min read Let’s learn the main methods from scipy.stats module in Python.

πŸ“Œ A Closer Look at Scipy’s Stats Module – Part 2 πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-09-19 | ⏱️ Read time: 6 min read Le
πŸ“Œ A Closer Look at Scipy’s Stats Module – Part 2 πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-09-19 | ⏱️ Read time: 6 min read Let’s learn the main methods from scipy.stats module in Python.

πŸ“Œ How to Build Your Own Roadmap for a Successful Data Science Career πŸ—‚ Category: CAREER ADVICE πŸ•’ Date: 2024-09-19 | ⏱️ Rea
πŸ“Œ How to Build Your Own Roadmap for a Successful Data Science Career πŸ—‚ Category: CAREER ADVICE πŸ•’ Date: 2024-09-19 | ⏱️ Read time: 4 min read Our weekly selection of must-read Editors’ Picks and original features

πŸ“Œ The Evolution of Text to Video Models πŸ—‚ Category: DEEP LEARNING πŸ•’ Date: 2024-09-19 | ⏱️ Read time: 10 min read Simplifyi
πŸ“Œ The Evolution of Text to Video Models πŸ—‚ Category: DEEP LEARNING πŸ•’ Date: 2024-09-19 | ⏱️ Read time: 10 min read Simplifying the neural nets behind Generative Video Diffusion

πŸ“Œ AdEMAMix: A Deep Dive into a New Optimizer for Your Deep Neural Network πŸ—‚ Category: MACHINE LEARNING πŸ•’ Date: 2024-09-19
πŸ“Œ AdEMAMix: A Deep Dive into a New Optimizer for Your Deep Neural Network πŸ—‚ Category: MACHINE LEARNING πŸ•’ Date: 2024-09-19 | ⏱️ Read time: 15 min read A better and faster option than the ADAM optimizer, from Apple Research

No skills? No problem. Just copy-paste and GET PAID. ➑️ 22,000+ already started… YOU'RE NEXT! Click here @NPFXSignals #ad InsideAds

πŸ“Œ Shared Nearest Neighbors: A More Robust Distance Metric πŸ—‚ Category: πŸ•’ Date: 2024-09-19 | ⏱️ Read time: 36 min read A dis
πŸ“Œ Shared Nearest Neighbors: A More Robust Distance Metric πŸ—‚ Category: πŸ•’ Date: 2024-09-19 | ⏱️ Read time: 36 min read A distance metric that can improve prediction, clustering, and outlier detection in datasets with many…

πŸ“Œ Improving Code Quality with Array and DataFrame Type Hints πŸ—‚ Category: πŸ•’ Date: 2024-09-19 | ⏱️ Read time: 12 min read Ho
πŸ“Œ Improving Code Quality with Array and DataFrame Type Hints πŸ—‚ Category: πŸ•’ Date: 2024-09-19 | ⏱️ Read time: 12 min read How generic specification permits powerful static and runtime validation

πŸ“Œ Through the Uncanny Mirror: Do LLMs Remember Like the Human Mind? πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-09-19
πŸ“Œ Through the Uncanny Mirror: Do LLMs Remember Like the Human Mind? πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-09-19 | ⏱️ Read time: 10 min read Exploring the Eerie Parallels and Profound Differences Between AI and Human Memory

πŸ“Œ Mastering t-SNE: A Comprehensive Guide to Understanding and Implementation in Python πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 20
πŸ“Œ Mastering t-SNE: A Comprehensive Guide to Understanding and Implementation in Python πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-09-20 | ⏱️ Read time: 26 min read Unlock the power of t-SNE for visualizing high-dimensional data, with a step-by-step Python implementation and…

πŸ“Œ Choosing Between LLM Agent Frameworks πŸ—‚ Category: πŸ•’ Date: 2024-09-20 | ⏱️ Read time: 15 min read Thanks to John Gilhuly
πŸ“Œ Choosing Between LLM Agent Frameworks πŸ—‚ Category: πŸ•’ Date: 2024-09-20 | ⏱️ Read time: 15 min read Thanks to John Gilhuly for his contributions to this piece. Agents are having a moment.…

πŸ“Œ Paper Walkthrough: U-Net πŸ—‚ Category: DEEP LEARNING πŸ•’ Date: 2024-09-20 | ⏱️ Read time: 16 min read A PyTorch implementati
πŸ“Œ Paper Walkthrough: U-Net πŸ—‚ Category: DEEP LEARNING πŸ•’ Date: 2024-09-20 | ⏱️ Read time: 16 min read A PyTorch implementation on one of the most popular semantic segmentation models.