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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 40 202 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 40 202 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.

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.

40 202
Subscribers
+1024 hours
+837 days
+34330 days
Posts Archive
πŸ“Œ LLMs, AI Agents, the Economics of Generative AI, and Other August Must-Reads πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-08-29
πŸ“Œ LLMs, AI Agents, the Economics of Generative AI, and Other August Must-Reads πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-08-29 | ⏱️ Read time: 5 min read The stories that resonated the most with our community in the past month

πŸ“Œ The Essential Guide to Error-Checking and Reviewing Presentations πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-08-29 | ⏱️ Read
πŸ“Œ The Essential Guide to Error-Checking and Reviewing Presentations πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-08-29 | ⏱️ Read time: 7 min read An overlooked skill for Data Scientists (and not only)

πŸ“Œ How to Create Custom Color Palettes in Matplotlib – Discrete vs. Linear Colormaps, Explained πŸ—‚ Category: DATA SCIENCE πŸ•’
πŸ“Œ How to Create Custom Color Palettes in Matplotlib – Discrete vs. Linear Colormaps, Explained πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-08-29 | ⏱️ Read time: 6 min read Actionable guide on how to bring custom colors to personalize your charts

πŸ“Œ The Smarter Way of Using AI in Programming πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-08-29 | ⏱️ Read time: 7 min
πŸ“Œ The Smarter Way of Using AI in Programming πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-08-29 | ⏱️ Read time: 7 min read avoid the outdated methods of integrating AI into your coding workflow by going beyond ChatGPT

πŸ“Œ Stop Manually Sorting Your List In Python If Performance Is Concerned πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-08-29 | ⏱️ R
πŸ“Œ Stop Manually Sorting Your List In Python If Performance Is Concerned πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-08-29 | ⏱️ Read time: 8 min read A sorted collection library that is as fast as C-extensions

πŸ“Œ Stop Being Data-Driven πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-08-30 | ⏱️ Read time: 12 min read Why we are fooled by data
πŸ“Œ Stop Being Data-Driven πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-08-30 | ⏱️ Read time: 12 min read Why we are fooled by data and how to stop it

πŸ“Œ How to Build a Genetic Algorithm from Scratch in Python πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-08-30 | ⏱️ Read time: 16 m
πŸ“Œ How to Build a Genetic Algorithm from Scratch in Python πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-08-30 | ⏱️ Read time: 16 min read A complete walkthrough on how one can build a Genetic Algorithm from scratch in Python,…

πŸ“Œ Causal Machine Learning for Customer Retention: a Practical Guide with Python πŸ—‚ Category: MACHINE LEARNING πŸ•’ Date: 2024-
πŸ“Œ Causal Machine Learning for Customer Retention: a Practical Guide with Python πŸ—‚ Category: MACHINE LEARNING πŸ•’ Date: 2024-08-30 | ⏱️ Read time: 25 min read An accessible guide to leveraging causal machine learning for optimizing client retention strategies

πŸ“Œ How to Build a Powerful Deep Research System πŸ—‚ Category: LARGE LANGUAGE MODELS πŸ•’ Date: 2025-10-04 | ⏱️ Read time: 6 min
πŸ“Œ How to Build a Powerful Deep Research System πŸ—‚ Category: LARGE LANGUAGE MODELS πŸ•’ Date: 2025-10-04 | ⏱️ Read time: 6 min read Learn how to access vasts amounts of information with your own deep research system

πŸ“Œ Real-Time Intelligence in Microsoft Fabric: The Ultimate Guide πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2025-10-04 | ⏱️ Read tim
πŸ“Œ Real-Time Intelligence in Microsoft Fabric: The Ultimate Guide πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2025-10-04 | ⏱️ Read time: 21 min read Once upon a time, handling streaming data was considered an avant-garde approach. Since the introduction of relational…

πŸ“Œ The Power of Pandas Plots: Backends πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-08-30 | ⏱️ Read time: 6 min read Create intera
πŸ“Œ The Power of Pandas Plots: Backends πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-08-30 | ⏱️ Read time: 6 min read Create interactive graphics from Pandas effortlessly

πŸ“Œ Hands On Neural Networks and Time Series, with Python πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-08-30 | ⏱️ Read t
πŸ“Œ Hands On Neural Networks and Time Series, with Python πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-08-30 | ⏱️ Read time: 14 min read From the very simple Feed Forward Neural Networks to the majestic transformers: everything you need…

πŸ“Œ A Comprehensive Introduction to Marketing Data Engineering πŸ—‚ Category: DATA ENGINEERING πŸ•’ Date: 2024-08-30 | ⏱️ Read tim
πŸ“Œ A Comprehensive Introduction to Marketing Data Engineering πŸ—‚ Category: DATA ENGINEERING πŸ•’ Date: 2024-08-30 | ⏱️ Read time: 21 min read Fundamentals, responsibilities, and challenges

πŸ“Œ Compressing Large Language Models (LLMs) πŸ—‚ Category: LARGE LANGUAGE MODELS πŸ•’ Date: 2024-08-30 | ⏱️ Read time: 12 min rea
πŸ“Œ Compressing Large Language Models (LLMs) πŸ—‚ Category: LARGE LANGUAGE MODELS πŸ•’ Date: 2024-08-30 | ⏱️ Read time: 12 min read Make LLMs 10X smaller without sacrificing performance

Awesome interactive textbook on probability theory and statistics Inside are clear visualizations, interactive elements, and minimal dry theory. You can tweak distributions, sample datasets, play with confidence intervals, and clearly see how it all works Get it here, I recommend opening it on a desktop https://seeing-theory.brown.edu/ πŸ‘‰ @DataScienceM

πŸ“Œ ChatGPT vs. Claude vs. Gemini for Data Analysis (Part 3): Best AI Assistant for Machine Learning πŸ—‚ Category: ARTIFICIAL I
πŸ“Œ ChatGPT vs. Claude vs. Gemini for Data Analysis (Part 3): Best AI Assistant for Machine Learning πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-08-30 | ⏱️ Read time: 12 min read How AI can accelerate your ML projects from feature engineering to model training

πŸ“Œ Decision Tree Classifier, Explained: A Visual Guide with Code Examples for Beginners πŸ—‚ Category: MACHINE LEARNING πŸ•’ Date
πŸ“Œ Decision Tree Classifier, Explained: A Visual Guide with Code Examples for Beginners πŸ—‚ Category: MACHINE LEARNING πŸ•’ Date: 2024-08-30 | ⏱️ Read time: 11 min read A fresh look on our favorite upside-down tree

πŸ“Œ Navigating the New Types of LLM Agents and Architectures πŸ—‚ Category: πŸ•’ Date: 2024-08-30 | ⏱️ Read time: 12 min read My t
πŸ“Œ Navigating the New Types of LLM Agents and Architectures πŸ—‚ Category: πŸ•’ Date: 2024-08-30 | ⏱️ Read time: 12 min read My thanks to John Gilhuly for his contributions to this piece If 2023 was the…

πŸ“Œ Targeting variants for maximum impact πŸ—‚ Category: MACHINE LEARNING πŸ•’ Date: 2024-08-30 | ⏱️ Read time: 8 min read How to
πŸ“Œ Targeting variants for maximum impact πŸ—‚ Category: MACHINE LEARNING πŸ•’ Date: 2024-08-30 | ⏱️ Read time: 8 min read How to use causal inference to improve key business metrics Egor Kraev and Alexander Polyakov…

πŸ“Œ The Ultimate Guide to Vision Transformers πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-08-30 | ⏱️ Read time: 8 min r
πŸ“Œ The Ultimate Guide to Vision Transformers πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-08-30 | ⏱️ Read time: 8 min read A comprehensive guide to the Vision Transformer (ViT) that revolutionized computer vision.