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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 40 145 subscribers, ranking 3 375 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 40 145 subscribers.

According to the latest data from 28 June, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 378 over the last 30 days and by 7 over the last 24 hours, overall reach remains high.

  • Verification status: Not verified
  • Engagement rate (ER): The average audience engagement rate is 2.09%. Within the first 24 hours after publication, content typically collects 1.91% reactions from the total number of subscribers.
  • Post reach: On average, each post receives 841 views. Within the first day, a publication typically gains 766 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 29 June, 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.

40 145
Subscribers
+724 hours
+1147 days
+37830 days
Posts Archive
πŸ“Œ How Many Pokemon Fit? πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-06-12 | ⏱️ Read time: 10 min read Finding the best Pokemon t
πŸ“Œ How Many Pokemon Fit? πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-06-12 | ⏱️ Read time: 10 min read Finding the best Pokemon team by modeling and solving a knapsack problem with PokeAPI and…

πŸ“Œ Time Series Regression and Cross-Validation: A Tidy Approach πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-06-12 | ⏱️ Read time:
πŸ“Œ Time Series Regression and Cross-Validation: A Tidy Approach πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-06-12 | ⏱️ Read time: 8 min read Step by step guide to EDA, feature engineering, cross validation and model comparison with tidymodels,…

πŸ“Œ A Python Engineer’s Introduction to 3D Gaussian Splatting (Part 2) πŸ—‚ Category: MACHINE LEARNING πŸ•’ Date: 2024-06-13 | ⏱️
πŸ“Œ A Python Engineer’s Introduction to 3D Gaussian Splatting (Part 2) πŸ—‚ Category: MACHINE LEARNING πŸ•’ Date: 2024-06-13 | ⏱️ Read time: 8 min read Understanding and coding how Gaussian’s are used within 3D Gaussian Splatting

πŸ“Œ AI Agent Unit Testing in Langfuse πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-06-13 | ⏱️ Read time: 10 min read Creating a sca
πŸ“Œ AI Agent Unit Testing in Langfuse πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-06-13 | ⏱️ Read time: 10 min read Creating a scalable testing solution for AI agents for operation by non-coders

πŸ“Œ My Easy Guide to Pre vs. Post Treatment Tests πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-06-13 | ⏱️ Read time: 13 min read A
πŸ“Œ My Easy Guide to Pre vs. Post Treatment Tests πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-06-13 | ⏱️ Read time: 13 min read A quick introduction to Before and After Tests with code.

πŸ“Œ Sparse Autoencoders, Additive Decision Trees, and Other Emerging Topics in AI Interpretability πŸ—‚ Category: DATA SCIENCE οΏ½
πŸ“Œ Sparse Autoencoders, Additive Decision Trees, and Other Emerging Topics in AI Interpretability πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-06-13 | ⏱️ Read time: 4 min read Our weekly selection of must-read Editors’ Picks and original features

πŸ“Œ Take a Look Under the hood πŸ—‚ Category: LARGE LANGUAGE MODELS πŸ•’ Date: 2024-06-13 | ⏱️ Read time: 13 min read Using Monose
πŸ“Œ Take a Look Under the hood πŸ—‚ Category: LARGE LANGUAGE MODELS πŸ•’ Date: 2024-06-13 | ⏱️ Read time: 13 min read Using Monosemanticity to understand the concepts a Large Language Model learned

πŸ“Œ Improving Business Performance with Machine Learning πŸ—‚ Category: MACHINE LEARNING πŸ•’ Date: 2024-06-13 | ⏱️ Read time: 18
πŸ“Œ Improving Business Performance with Machine Learning πŸ—‚ Category: MACHINE LEARNING πŸ•’ Date: 2024-06-13 | ⏱️ Read time: 18 min read Whether you are a data scientist, analyst, or business analyst, your goal is to deliver…

I was shocked how easy it is: I connected my signals,…and trades started happening. Nobody told me you could earn passively l
I was shocked how easy it is: I connected my signals,…and trades started happening. Nobody told me you could earn passively like that! This is the automation traders are hiding β€” it just works. Curious how? πŸ‘‰ See the real tool in action #ad InsideAds

πŸ“Œ Beyond AlphaFold: The Future Of LLM in Medicine πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-06-13 | ⏱️ Read time: 1
πŸ“Œ Beyond AlphaFold: The Future Of LLM in Medicine πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-06-13 | ⏱️ Read time: 17 min read AlphaFold leaves a complex legacy: What will be the future of LLM in biology and…

πŸ“Œ How I’d Become a Data Scientist (If I Had to Start Over) πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-06-14 | ⏱️ Read time: 12
πŸ“Œ How I’d Become a Data Scientist (If I Had to Start Over) πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-06-14 | ⏱️ Read time: 12 min read Roadmap and tips on how to land a job in data science

πŸ“Œ CUDA for AI – Intuitively and Exhaustively Explained πŸ—‚ Category: MACHINE LEARNING πŸ•’ Date: 2024-06-14 | ⏱️ Read time: 58
πŸ“Œ CUDA for AI – Intuitively and Exhaustively Explained πŸ—‚ Category: MACHINE LEARNING πŸ•’ Date: 2024-06-14 | ⏱️ Read time: 58 min read Parallelized AI from scratch in CUDA

πŸ“Œ Mapping the Pokemon World: A Network Analysis of Habitat-Based Encounters πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-06-14 |
πŸ“Œ Mapping the Pokemon World: A Network Analysis of Habitat-Based Encounters πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-06-14 | ⏱️ Read time: 19 min read An introduction to Network Analysis in Python, along with a practical example using Pokemon data…

πŸ“Œ Understanding Buffer of Thoughts (BoT) – Reasoning with Large Language Models πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-06-1
πŸ“Œ Understanding Buffer of Thoughts (BoT) – Reasoning with Large Language Models πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-06-14 | ⏱️ Read time: 12 min read New prompt engineering tool for complex reasoning, compared with Chain of thought (CoT) and Tree…

πŸ“Œ Gated Recurrent Units (GRU) – Improving RNNs πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-06-15 | ⏱️ Read time: 11 m
πŸ“Œ Gated Recurrent Units (GRU) – Improving RNNs πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-06-15 | ⏱️ Read time: 11 min read Explaining how Gated Recurrent Neural Networks work

πŸ“Œ Graph Visualization: 7 Steps from Easy to Advanced πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-06-15 | ⏱️ Read time: 10 min re
πŸ“Œ Graph Visualization: 7 Steps from Easy to Advanced πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-06-15 | ⏱️ Read time: 10 min read Making visualization with Python, NetworkX, and D3.JS

πŸ“Œ GPT from Scratch with MLX πŸ—‚ Category: DEEP LEARNING πŸ•’ Date: 2024-06-15 | ⏱️ Read time: 36 min read Define and train GPT-
πŸ“Œ GPT from Scratch with MLX πŸ—‚ Category: DEEP LEARNING πŸ•’ Date: 2024-06-15 | ⏱️ Read time: 36 min read Define and train GPT-2 on your MacBook

πŸ“Œ Erasing Clouds from Satellite Imagery Using GANs (Generative Adversarial Networks) πŸ—‚ Category: DEEP LEARNING πŸ•’ Date: 202
πŸ“Œ Erasing Clouds from Satellite Imagery Using GANs (Generative Adversarial Networks) πŸ—‚ Category: DEEP LEARNING πŸ•’ Date: 2024-06-15 | ⏱️ Read time: 12 min read Building GANs from scratch in python

πŸ“Œ Simple Model Retraining Automation via GitHub Actions πŸ—‚ Category: EDUCATION πŸ•’ Date: 2024-06-15 | ⏱️ Read time: 13 min re
πŸ“Œ Simple Model Retraining Automation via GitHub Actions πŸ—‚ Category: EDUCATION πŸ•’ Date: 2024-06-15 | ⏱️ Read time: 13 min read Easily streamline your modelling process with the GitHub Actions.

πŸ“Œ Analyzing Unstructured PDF Data w/ Embedding Models and LLMs πŸ—‚ Category: πŸ•’ Date: 2024-06-15 | ⏱️ Read time: 8 min read H
πŸ“Œ Analyzing Unstructured PDF Data w/ Embedding Models and LLMs πŸ—‚ Category: πŸ•’ Date: 2024-06-15 | ⏱️ Read time: 8 min read How to turn PDFs into actionable insights