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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 40 237 subscribers, ranking 3 336 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 40 237 subscribers.

According to the latest data from 04 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 16 over the last 24 hours, overall reach remains high.

  • Verification status: Not verified
  • Engagement rate (ER): The average audience engagement rate is 1.92%. Within the first 24 hours after publication, content typically collects 1.89% reactions from the total number of subscribers.
  • Post reach: On average, each post receives 771 views. Within the first day, a publication typically gains 761 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 05 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.

40 237
Subscribers
+1624 hours
+837 days
+34330 days
Posts Archive
πŸ“Œ Jointly learning rewards and policies: an iterative Inverse Reinforcement Learning framework with… πŸ—‚ Category: MACHINE LE
πŸ“Œ Jointly learning rewards and policies: an iterative Inverse Reinforcement Learning framework with… πŸ—‚ Category: MACHINE LEARNING πŸ•’ Date: 2024-11-10 | ⏱️ Read time: 13 min read A novel tractable and interpretable algorithm to learn from expert demonstrations

πŸ“Œ AdaBoost Classifier, Explained: A Visual Guide with Code Examples πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-11-10 | ⏱️ Read
πŸ“Œ AdaBoost Classifier, Explained: A Visual Guide with Code Examples πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-11-10 | ⏱️ Read time: 15 min read Putting the weight where weak learners need it most

πŸ“Œ My Medium Journey as a Data Scientist: 6 Months, 18 Articles, and 3,000 Followers πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-
πŸ“Œ My Medium Journey as a Data Scientist: 6 Months, 18 Articles, and 3,000 Followers πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-11-11 | ⏱️ Read time: 10 min read Real numbers, earnings, and data-driven growth strategy for Medium writers

πŸ“Œ Advanced Time Series Forecasting With sktime πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-11-11 | ⏱️ Read time: 9 mi
πŸ“Œ Advanced Time Series Forecasting With sktime πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-11-11 | ⏱️ Read time: 9 min read Learn how to optimize model hyperparameters and even the architecture in a few lines of…

πŸ“Œ Calibrating Marketing Mix Models In Python πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-11-11 | ⏱️ Read time: 12 min read Part
πŸ“Œ Calibrating Marketing Mix Models In Python πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-11-11 | ⏱️ Read time: 12 min read Part 2 of a hands-on guide to help you master MMM in pymc

πŸ“Œ Detecting Anomalies in Social Media Volume Time Series πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-11-11 | ⏱️ Read time: 6 min
πŸ“Œ Detecting Anomalies in Social Media Volume Time Series πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-11-11 | ⏱️ Read time: 6 min read How I detect anomalies in social Media volumes: A Residual-Based Approach

πŸ“Œ Why ETL-Zero? Understanding the shift in Data Integration πŸ—‚ Category: πŸ•’ Date: 2024-11-11 | ⏱️ Read time: 11 min read Whe
πŸ“Œ Why ETL-Zero? Understanding the shift in Data Integration πŸ—‚ Category: πŸ•’ Date: 2024-11-11 | ⏱️ Read time: 11 min read When I was preparing for the Salesforce Data Cloud certification, I came across the term…

πŸ“Œ Bessel’s Correction: Why Do We Divide by nβˆ’1 Instead of n in Sample Variance? πŸ—‚ Category: MACHINE LEARNING πŸ•’ Date: 2024-
πŸ“Œ Bessel’s Correction: Why Do We Divide by nβˆ’1 Instead of n in Sample Variance? πŸ—‚ Category: MACHINE LEARNING πŸ•’ Date: 2024-11-11 | ⏱️ Read time: 9 min read Understanding the Unbiased Estimation of Population Variance

πŸ“Œ Decoding One-Hot Encoding: A Beginner’s Guide to Categorical Data πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-11-11
πŸ“Œ Decoding One-Hot Encoding: A Beginner’s Guide to Categorical Data πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-11-11 | ⏱️ Read time: 6 min read Learning to transform categorical data into a format that a machine learning model can understand

πŸ“Œ NER in Czech Documents with XLM-RoBERTa using Accelerate πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-11-12 | ⏱️ Read time: 10
πŸ“Œ NER in Czech Documents with XLM-RoBERTa using Accelerate πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-11-12 | ⏱️ Read time: 10 min read Decisions I made during the development of a document processing model that was successfully deployed

πŸ“Œ Economics of Hosting Open Source LLMs πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-11-12 | ⏱️ Read time: 23 min read Leveraging
πŸ“Œ Economics of Hosting Open Source LLMs πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-11-12 | ⏱️ Read time: 23 min read Leveraging various deployment options

πŸ“Œ From Parallel Computing Principles to Programming for CPU and GPU Architectures πŸ—‚ Category: MACHINE LEARNING πŸ•’ Date: 202
πŸ“Œ From Parallel Computing Principles to Programming for CPU and GPU Architectures πŸ—‚ Category: MACHINE LEARNING πŸ•’ Date: 2024-11-12 | ⏱️ Read time: 23 min read For early ML Engineers and Data Scientists, to understand memory fundamentals, parallel execution, and how…

πŸ“Œ Beyond RAG: Precision Filtering in a Semantic World πŸ—‚ Category: MACHINE LEARNING πŸ•’ Date: 2024-11-12 | ⏱️ Read time: 9 mi
πŸ“Œ Beyond RAG: Precision Filtering in a Semantic World πŸ—‚ Category: MACHINE LEARNING πŸ•’ Date: 2024-11-12 | ⏱️ Read time: 9 min read Aligning expectations with reality by using traditional ML to bridge the gap in a LLM’s…

πŸ“Œ Reporting in Excel Could Be Costing Your Business More Than You Think – Here’s How to Fix It… πŸ—‚ Category: DATA SCIENCE πŸ•’
πŸ“Œ Reporting in Excel Could Be Costing Your Business More Than You Think – Here’s How to Fix It… πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-11-12 | ⏱️ Read time: 7 min read Discover how you can save hours, eliminate costly data errors, and free up your team…

πŸ“Œ Boosting Algorithms in Machine Learning, Part II: Gradient Boosting πŸ—‚ Category: MACHINE LEARNING πŸ•’ Date: 2024-11-12 | ⏱️
πŸ“Œ Boosting Algorithms in Machine Learning, Part II: Gradient Boosting πŸ—‚ Category: MACHINE LEARNING πŸ•’ Date: 2024-11-12 | ⏱️ Read time: 11 min read Uncovering a simple yet powerful, award-winning machine learning algorithm

πŸ“Œ Game Theory, Part 3 – You are the average of the five people you spend the most time with πŸ—‚ Category: DATA SCIENCE πŸ•’ Dat
πŸ“Œ Game Theory, Part 3 – You are the average of the five people you spend the most time with πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-11-13 | ⏱️ Read time: 5 min read Is Tit-for-tat the best strategy in the Iterated Prisoner’s Dilemma game?

πŸ“Œ Increase Trust in Your Regression Model The Easy Way πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-11-13 | ⏱️ Read time: 5 min r
πŸ“Œ Increase Trust in Your Regression Model The Easy Way πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-11-13 | ⏱️ Read time: 5 min read How to use Conformalized Quantile Regression

πŸ“Œ The Ultimate Guide to Evaluating the Impact of Outlier Treatment in Time Series πŸ—‚ Category: MACHINE LEARNING πŸ•’ Date: 202
πŸ“Œ The Ultimate Guide to Evaluating the Impact of Outlier Treatment in Time Series πŸ—‚ Category: MACHINE LEARNING πŸ•’ Date: 2024-11-13 | ⏱️ Read time: 22 min read Sensitivity Analysis, Model Validation, Feature Importance & More!

πŸ“Œ Nobody Puts AI in a Corner! πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-11-13 | ⏱️ Read time: 9 min read Two short
πŸ“Œ Nobody Puts AI in a Corner! πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-11-13 | ⏱️ Read time: 9 min read Two short anecdotes about transformations, and what it takes if you want to become β€œAI-enabled”

πŸ“Œ Demystifying the Correlation Matrix in Data Science πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-11-13 | ⏱️ Read time: 16 min r
πŸ“Œ Demystifying the Correlation Matrix in Data Science πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-11-13 | ⏱️ Read time: 16 min read Understanding the Connections Between Variables: A Comprehensive Guide to Correlation Matrices and Their Applications