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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 LightGBM: The Fastest Option of Gradient Boosting πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2025-01-12 | ⏱️ Read time: 7 min read
πŸ“Œ LightGBM: The Fastest Option of Gradient Boosting πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2025-01-12 | ⏱️ Read time: 7 min read Learn how to implement a fast and effective Gradient Boosting model using Python

πŸ“Œ What Would a Stoic Do? – An AI-Based Decision-Making Model πŸ—‚ Category: DEEP LEARNING πŸ•’ Date: 2025-01-12 | ⏱️ Read time:
πŸ“Œ What Would a Stoic Do? – An AI-Based Decision-Making Model πŸ—‚ Category: DEEP LEARNING πŸ•’ Date: 2025-01-12 | ⏱️ Read time: 14 min read Using AI to build Marcus Aurelius’ reincarnation

πŸ“Œ What is MicroPython? Do I Need to Know it as a Data Scientist? πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2025-01-12 | ⏱️ Read tim
πŸ“Œ What is MicroPython? Do I Need to Know it as a Data Scientist? πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2025-01-12 | ⏱️ Read time: 6 min read In this year’s edition of the Stack Overflow survey, MicroPython is with 1.6% in the…

πŸ“Œ Using Constraint Programming to Solve Math Theorems πŸ—‚ Category: MATHEMATICS πŸ•’ Date: 2025-01-12 | ⏱️ Read time: 6 min rea
πŸ“Œ Using Constraint Programming to Solve Math Theorems πŸ—‚ Category: MATHEMATICS πŸ•’ Date: 2025-01-12 | ⏱️ Read time: 6 min read Case study: the quasigroups existence problem

πŸ“Œ Speed up Pandas code with Numpy πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2025-01-13 | ⏱️ Read time: 13 min read But I can’t vect
πŸ“Œ Speed up Pandas code with Numpy πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2025-01-13 | ⏱️ Read time: 13 min read But I can’t vectorise this, can I?  β€¦. yes, you probably can!

πŸ“Œ How to Build a Knowledge Graph in Minutes (And Make It Enterprise-Ready) πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 202
πŸ“Œ How to Build a Knowledge Graph in Minutes (And Make It Enterprise-Ready) πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2025-01-13 | ⏱️ Read time: 10 min read I tried and failed creating one-but it was when LLMs were not a thing!

πŸ“Œ Understanding the Evolution of ChatGPT: Part 2 – GPT-2 and GPT-3 πŸ—‚ Category: DEEP LEARNING πŸ•’ Date: 2025-01-13 | ⏱️ Read
πŸ“Œ Understanding the Evolution of ChatGPT: Part 2 – GPT-2 and GPT-3 πŸ—‚ Category: DEEP LEARNING πŸ•’ Date: 2025-01-13 | ⏱️ Read time: 10 min read Scaling from 117M to 175B: Insights into GPT-2 and GPT-3.

πŸ“Œ Going Beyond Bias-Variance Tradeoff Into Double Descent Phenomenon πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2025-01-1
πŸ“Œ Going Beyond Bias-Variance Tradeoff Into Double Descent Phenomenon πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2025-01-13 | ⏱️ Read time: 22 min read It’s not how many times you get knocked down that count, it’s how many times…

πŸ“Œ A Multimodal AI Assistant: Combining Local and Cloud Models πŸ—‚ Category: πŸ•’ Date: 2025-01-13 | ⏱️ Read time: 22 min read U
πŸ“Œ A Multimodal AI Assistant: Combining Local and Cloud Models πŸ—‚ Category: πŸ•’ Date: 2025-01-13 | ⏱️ Read time: 22 min read Use LangGraph, mlx and Florence2 to build an agent that answers complex image questions, with…

πŸ“Œ Contextual Topic Modelling in Chinese Corpora with KeyNMF πŸ—‚ Category: NATURAL LANGUAGE PROCESSING πŸ•’ Date: 2025-01-13 | ⏱
πŸ“Œ Contextual Topic Modelling in Chinese Corpora with KeyNMF πŸ—‚ Category: NATURAL LANGUAGE PROCESSING πŸ•’ Date: 2025-01-13 | ⏱️ Read time: 8 min read A comprehensive guide on getting the most out of your Chinese topic models, from preprocessing…

πŸ“Œ The AI (R)Evolution, Looking From 2024 Into the Immediate Future πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2025-01-13
πŸ“Œ The AI (R)Evolution, Looking From 2024 Into the Immediate Future πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2025-01-13 | ⏱️ Read time: 17 min read Witnessing rapid innovation, fierce competition, and transformative tools for life, work, and human development

πŸ“Œ Understanding Flash Attention: Writing the Algorithm from Scratch in Triton πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date:
πŸ“Œ Understanding Flash Attention: Writing the Algorithm from Scratch in Triton πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2025-01-15 | ⏱️ Read time: 7 min read Find out how Flash Attention works. Afterward, we’ll refine our understanding by writing a GPU…

πŸ“Œ Qubits Explained: Everything You Need to Know πŸ—‚ Category: PHYSICS πŸ•’ Date: 2025-01-15 | ⏱️ Read time: 11 min read A deep
πŸ“Œ Qubits Explained: Everything You Need to Know πŸ—‚ Category: PHYSICS πŸ•’ Date: 2025-01-15 | ⏱️ Read time: 11 min read A deep dive into the building block of quantum computers.

πŸ“Œ Water Cooler Small Talk, Ep 6: Benford’s Law πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2025-01-15 | ⏱️ Read time: 10 min read A l
πŸ“Œ Water Cooler Small Talk, Ep 6: Benford’s Law πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2025-01-15 | ⏱️ Read time: 10 min read A look into the strange first digit distribution of naturally occurring datasets

πŸ“Œ Developing an AI-Powered Smart Guide for Business Planning & Entrepreneurship πŸ—‚ Category: ENTREPRENEURSHIP πŸ•’ Date: 2025-
πŸ“Œ Developing an AI-Powered Smart Guide for Business Planning & Entrepreneurship πŸ—‚ Category: ENTREPRENEURSHIP πŸ•’ Date: 2025-01-16 | ⏱️ Read time: 35 min read A LangGraph-based advanced agentic RAG with standard business guides, AI-based web search, trusted sources, and…

πŸ“Œ The Death of Human-Written Code Tutorials in the ChatGPT Era … Or Not? πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2025-
πŸ“Œ The Death of Human-Written Code Tutorials in the ChatGPT Era β€¦ Or Not? πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2025-01-16 | ⏱️ Read time: 11 min read An argument in favor of human-written coding tutorials in the new age of LLMs.

πŸ“Œ Why Data Scientists Can’t Afford Too Many Dimensions and What They Can Do About It πŸ—‚ Category: DEEP LEARNING πŸ•’ Date: 202
πŸ“Œ Why Data Scientists Can’t Afford Too Many Dimensions and What They Can Do About It πŸ—‚ Category: DEEP LEARNING πŸ•’ Date: 2025-01-16 | ⏱️ Read time: 17 min read An in-depth article about dimensionality reduction and its most popular methods

πŸ“Œ Charts, Dashboards, Maps, and More: Data Visualization in the Spotlight πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2025-01-16 | ⏱️
πŸ“Œ Charts, Dashboards, Maps, and More: Data Visualization in the Spotlight πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2025-01-16 | ⏱️ Read time: 4 min read Our weekly selection of must-read Editors’ Picks and original features

πŸ“Œ The Large Language Model Course πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2025-01-16 | ⏱️ Read time: 21 min read How t
πŸ“Œ The Large Language Model Course πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2025-01-16 | ⏱️ Read time: 21 min read How to become an LLM Scientist or Engineer from scratch

πŸ“Œ No Peeking Ahead: Time-Aware Graph Fraud Detection πŸ—‚ Category: MACHINE LEARNING πŸ•’ Date: 2025-09-14 | ⏱️ Read time: 15 mi
πŸ“Œ No Peeking Ahead: Time-Aware Graph Fraud Detection πŸ—‚ Category: MACHINE LEARNING πŸ•’ Date: 2025-09-14 | ⏱️ Read time: 15 min read How to implement leak-free graph fraud detection