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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 40 193 subscribers, ranking 3 365 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 40 193 subscribers.

According to the latest data from 01 July, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 355 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 2.04%. Within the first 24 hours after publication, content typically collects 2.12% reactions from the total number of subscribers.
  • Post reach: On average, each post receives 818 views. Within the first day, a publication typically gains 851 views.
  • Reactions and interaction: The audience actively supports content: the average number of reactions per post is 2.
  • 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 02 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.

40 193
Subscribers
+2124 hours
+857 days
+35530 days
Posts Archive
πŸ“Œ Learning to Unlearn: Why Data Scientists and AI Practitioners Should Understand Machine Unlearning πŸ—‚ Category: MACHINE LE
πŸ“Œ Learning to Unlearn: Why Data Scientists and AI Practitioners Should Understand Machine Unlearning πŸ—‚ Category: MACHINE LEARNING πŸ•’ Date: 2024-08-22 | ⏱️ Read time: 24 min read Explore the intersections between privacy and AI with a guide to removing the impact of…

πŸ“Œ SQL User Defined Functions (UDFs) πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-08-22 | ⏱️ Read time: 11 min read A tutorial on
πŸ“Œ SQL User Defined Functions (UDFs) πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-08-22 | ⏱️ Read time: 11 min read A tutorial on mastering SQL UDFs: categories, use cases, and difference from stored procedures

πŸ“Œ Structured State Space Models Visually Explained πŸ—‚ Category: DEEP LEARNING πŸ•’ Date: 2024-08-22 | ⏱️ Read time: 21 min rea
πŸ“Œ Structured State Space Models Visually Explained πŸ—‚ Category: DEEP LEARNING πŸ•’ Date: 2024-08-22 | ⏱️ Read time: 21 min read Part 2 – Towards Mamba State Space Models for Images, Videos and Time Series

πŸ“Œ LLM Agents, Text Vectorization, Advanced SQL, and Other Must-Reads by Our Newest Authors πŸ—‚ Category: DATA SCIENCE πŸ•’ Date
πŸ“Œ LLM Agents, Text Vectorization, Advanced SQL, and Other Must-Reads by Our Newest Authors πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-08-22 | ⏱️ Read time: 4 min read Our weekly selection of must-read Editors’ Picks and original features

πŸ“Œ The Floyd-Warshall Algorithm From Graph Theory, Applied to Parsing Molecular Structures πŸ—‚ Category: CHEMISTRY πŸ•’ Date: 20
πŸ“Œ The Floyd-Warshall Algorithm From Graph Theory, Applied to Parsing Molecular Structures πŸ—‚ Category: CHEMISTRY πŸ•’ Date: 2024-08-22 | ⏱️ Read time: 10 min read Hands-on explanations assisted by simple JavaScript code

πŸ“Œ What It Takes To Build a Great Graph πŸ—‚ Category: πŸ•’ Date: 2024-08-22 | ⏱️ Read time: 8 min read Our world is composed of
πŸ“Œ What It Takes To Build a Great Graph πŸ—‚ Category: πŸ•’ Date: 2024-08-22 | ⏱️ Read time: 8 min read Our world is composed of relationships. Who we know, how we interact, how we transact…

Curious about mastering ethical hacking? Unlock the full CEH v13 course for freeβ€”no strings attached! Discover insider knowle
Curious about mastering ethical hacking? Unlock the full CEH v13 course for freeβ€”no strings attached! Discover insider knowledge, practical labs, and tools you won’t find anywhere else. Don’t miss this exclusive chance to propel your IT skills to the next level. Start learning todayβ€”access everything right here! #ad InsideAds

πŸ“Œ Graph RAG – A conceptual introduction πŸ—‚ Category: πŸ•’ Date: 2024-08-22 | ⏱️ Read time: 10 min read Graph RAG answers the b
πŸ“Œ Graph RAG – A conceptual introduction πŸ—‚ Category: πŸ•’ Date: 2024-08-22 | ⏱️ Read time: 10 min read Graph RAG answers the big questions where text embeddings won’t help you.

πŸ“Œ A Data Science Leader’s Guide to Ensuring Every Project Drives Business Value πŸ—‚ Category: CAREER ADVICE πŸ•’ Date: 2024-08-
πŸ“Œ A Data Science Leader’s Guide to Ensuring Every Project Drives Business Value πŸ—‚ Category: CAREER ADVICE πŸ•’ Date: 2024-08-22 | ⏱️ Read time: 9 min read Lessons from someone who manages a team of 8

πŸ“Œ Why Does Position-Based Chunking Lead to Poor Performance in RAGs? πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-08-2
πŸ“Œ Why Does Position-Based Chunking Lead to Poor Performance in RAGs? πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-08-22 | ⏱️ Read time: 12 min read How to implement semantic chunking and gain better results.

πŸ“Œ Reinforcement Learning, Part 7: Introduction to Value-Function Approximation πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date:
πŸ“Œ Reinforcement Learning, Part 7: Introduction to Value-Function Approximation πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-08-22 | ⏱️ Read time: 13 min read Scaling reinforcement learning from tabular methods to large spaces

πŸ“Œ Building an Image Similarity Search Engine with FAISS and CLIP πŸ—‚ Category: DEEP LEARNING πŸ•’ Date: 2024-08-23 | ⏱️ Read ti
πŸ“Œ Building an Image Similarity Search Engine with FAISS and CLIP πŸ—‚ Category: DEEP LEARNING πŸ•’ Date: 2024-08-23 | ⏱️ Read time: 6 min read A guided tutorial explaining how to search your image dataset with text or photo queries,…

πŸ“Œ Building an Agentic Retrieval-Augmented Generation (RAG) System with IBM Watsonx and Langchain πŸ—‚ Category: πŸ•’ Date: 2024-
πŸ“Œ Building an Agentic Retrieval-Augmented Generation (RAG) System with IBM Watsonx and Langchain πŸ—‚ Category: πŸ•’ Date: 2024-08-23 | ⏱️ Read time: 6 min read A quick-start tutorial

πŸ“Œ BERT – Intuitively and Exhaustively Explained πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-08-23 | ⏱️ Read time: 58 min read Ba
πŸ“Œ BERT – Intuitively and Exhaustively Explained πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-08-23 | ⏱️ Read time: 58 min read Baking General Understanding into Language Models

πŸ“Œ The Tournament of Reinforcement Learning: DDPG, SAC, PPO, I2A, Decision Transformer πŸ—‚ Category: ARTIFICIAL INTELLIGENCE οΏ½
πŸ“Œ The Tournament of Reinforcement Learning: DDPG, SAC, PPO, I2A, Decision Transformer πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-08-23 | ⏱️ Read time: 15 min read Training simulated humanoid robots to fight using five new Reinforcement Learning papers

πŸ“Œ Art Guard: Protecting Your Online Images From Generative AI πŸ—‚ Category: MACHINE LEARNING πŸ•’ Date: 2024-08-23 | ⏱️ Read ti
πŸ“Œ Art Guard: Protecting Your Online Images From Generative AI πŸ—‚ Category: MACHINE LEARNING πŸ•’ Date: 2024-08-23 | ⏱️ Read time: 21 min read Steps you can take to prevent bots from scraping and using your art to train…

πŸ“Œ An Introduction to Quantum Computers and Quantum Coding πŸ—‚ Category: πŸ•’ Date: 2024-08-23 | ⏱️ Read time: 18 min read Demys
πŸ“Œ An Introduction to Quantum Computers and Quantum Coding πŸ—‚ Category: πŸ•’ Date: 2024-08-23 | ⏱️ Read time: 18 min read Demystifying the novel world of quantum computing, quantum programming, and quantum algorithms.

πŸ“Œ DBSCAN, Explained in 5 Minutes πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-08-23 | ⏱️ Read time: 5 min read Fastest implementa
πŸ“Œ DBSCAN, Explained in 5 Minutes πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-08-23 | ⏱️ Read time: 5 min read Fastest implementation in python

πŸ“Œ Interpreting Weight Regularization In Machine Learning πŸ—‚ Category: DEEP LEARNING πŸ•’ Date: 2024-08-23 | ⏱️ Read time: 9 mi
πŸ“Œ Interpreting Weight Regularization In Machine Learning πŸ—‚ Category: DEEP LEARNING πŸ•’ Date: 2024-08-23 | ⏱️ Read time: 9 min read Why do L1 and L2 regularization result in model sparsity and weight shrinkage? What about…

πŸ“Œ Bernoulli Naive Bayes, Explained: A Visual Guide with Code Examples for Beginners πŸ—‚ Category: MACHINE LEARNING πŸ•’ Date: 2
πŸ“Œ Bernoulli Naive Bayes, Explained: A Visual Guide with Code Examples for Beginners πŸ—‚ Category: MACHINE LEARNING πŸ•’ Date: 2024-08-24 | ⏱️ Read time: 9 min read Unlocking predictive power through Yes/No probability