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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 Essential Guide to Continuous Ranked Probability Score (CRPS) for Forecasting πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-08-3
πŸ“Œ Essential Guide to Continuous Ranked Probability Score (CRPS) for Forecasting πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-08-31 | ⏱️ Read time: 7 min read Learn how to evaluate probabilistic forecasts and how CRPS relates to other metrics

πŸ“Œ How to Deal with Time Series Outliers πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-08-31 | ⏱️ Read time: 6 min read Understandi
πŸ“Œ How to Deal with Time Series Outliers πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-08-31 | ⏱️ Read time: 6 min read Understanding, detecting and replacing outliers in time series

πŸ“Œ Data Scientists Can’t Excel in Python Without Mastering These Functions πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-08-31 | ⏱️
πŸ“Œ Data Scientists Can’t Excel in Python Without Mastering These Functions πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-08-31 | ⏱️ Read time: 11 min read Introduction of Python’s core functions, use cases, scripts, and underlying mechanisms

πŸ“Œ Streamline Property Data Management: Advanced Data Extraction & Retrieval with Indexify πŸ—‚ Category: πŸ•’ Date: 2024-08-31 |
πŸ“Œ Streamline Property Data Management: Advanced Data Extraction & Retrieval with Indexify πŸ—‚ Category: πŸ•’ Date: 2024-08-31 | ⏱️ Read time: 15 min read A Step-by-Step Guide to Document Querying with Indexify

πŸ“Œ The DIY Path to AI Product Management: Picking a Starter Project πŸ—‚ Category: CHATGPT πŸ•’ Date: 2024-08-31 | ⏱️ Read time:
πŸ“Œ The DIY Path to AI Product Management: Picking a Starter Project πŸ—‚ Category: CHATGPT πŸ•’ Date: 2024-08-31 | ⏱️ Read time: 8 min read Building real-world skills through hands-on trial and error.

πŸ“Œ Building Scalable Data Platforms πŸ—‚ Category: ANALYTICS πŸ•’ Date: 2024-09-01 | ⏱️ Read time: 14 min read Data Mesh trends i
πŸ“Œ Building Scalable Data Platforms πŸ—‚ Category: ANALYTICS πŸ•’ Date: 2024-09-01 | ⏱️ Read time: 14 min read Data Mesh trends in data platform design

πŸ“Œ Training AI Models on CPU πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-09-01 | ⏱️ Read time: 16 min read Revisiting
πŸ“Œ Training AI Models on CPU πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-09-01 | ⏱️ Read time: 16 min read Revisiting CPU for ML in an Era of GPU Scarcity

πŸ“Œ Create Your Own Meal Planner Using ChatGPT πŸ—‚ Category: CHATGPT πŸ•’ Date: 2024-09-02 | ⏱️ Read time: 19 min read A brief gu
πŸ“Œ Create Your Own Meal Planner Using ChatGPT πŸ—‚ Category: CHATGPT πŸ•’ Date: 2024-09-02 | ⏱️ Read time: 19 min read A brief guide to prompt engineering

πŸ“Œ Mathematics of Love: Optimizing a Dining-Room Seating Arrangement for Weddings with Python πŸ—‚ Category: DATA SCIENCE πŸ•’ Da
πŸ“Œ Mathematics of Love: Optimizing a Dining-Room Seating Arrangement for Weddings with Python πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-09-02 | ⏱️ Read time: 19 min read Solving the Restricted Quadratic Multi-Knapsack Problem (RQMKP) with mathematical programming and Python

πŸ“Œ An Easy Way to Remove Tourists from Photos πŸ—‚ Category: PYTHON πŸ•’ Date: 2024-09-02 | ⏱️ Read time: 9 min read Image cleanu
πŸ“Œ An Easy Way to Remove Tourists from Photos πŸ—‚ Category: PYTHON πŸ•’ Date: 2024-09-02 | ⏱️ Read time: 9 min read Image cleanup with Python, PIL, and OpenCV

πŸ“Œ Encoding Categorical Data, Explained: A Visual Guide with Code Example for Beginners πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 20
πŸ“Œ Encoding Categorical Data, Explained: A Visual Guide with Code Example for Beginners πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-09-02 | ⏱️ Read time: 10 min read Six ways of matchmaking categories and numbers

πŸ“Œ Use R to build Clinical Flowchart with shinyCyJS πŸ—‚ Category: πŸ•’ Date: 2024-09-03 | ⏱️ Read time: 6 min read Customizable
πŸ“Œ Use R to build Clinical Flowchart with shinyCyJS πŸ—‚ Category: πŸ•’ Date: 2024-09-03 | ⏱️ Read time: 6 min read Customizable R package for Graph / Network visualization

πŸ“Œ Subway Route Data Extraction with Overpass API: A Step-by-Step Guide πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-09-03 | ⏱️ Re
πŸ“Œ Subway Route Data Extraction with Overpass API: A Step-by-Step Guide πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-09-03 | ⏱️ Read time: 11 min read Simplify Geodata Extraction from OpenStreetMaps via the Overpass API

πŸ“Œ Information in Noise πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-09-03 | ⏱️ Read time: 4 min read Two Techniques for Visualizi
πŸ“Œ Information in Noise πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-09-03 | ⏱️ Read time: 4 min read Two Techniques for Visualizing Many Time-Series at Once

πŸ“Œ 5 Pillars for a Hyper-Optimized AI Workflow πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-09-03 | ⏱️ Read time: 8 min
πŸ“Œ 5 Pillars for a Hyper-Optimized AI Workflow πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-09-03 | ⏱️ Read time: 8 min read A gentle introduction to a methodology for creating production-ready, extensible & highly optimized AI workflows

πŸ“Œ Line-By-Line, Let’s Reproduce GPT-2: Section 3 – Training πŸ—‚ Category: πŸ•’ Date: 2024-09-03 | ⏱️ Read time: 20 min read Thi
πŸ“Œ Line-By-Line, Let’s Reproduce GPT-2: Section 3 – Training πŸ—‚ Category: πŸ•’ Date: 2024-09-03 | ⏱️ Read time: 20 min read This blog post will go line-by-line through the code in Section 3 of Andrej Karpathy’s…

πŸ“Œ Using Generative AI To Get Insights From Disorderly Data πŸ—‚ Category: πŸ•’ Date: 2024-09-03 | ⏱️ Read time: 41 min read Best
πŸ“Œ Using Generative AI To Get Insights From Disorderly Data πŸ—‚ Category: πŸ•’ Date: 2024-09-03 | ⏱️ Read time: 41 min read Best practices for using Large Language Models to extract actionable insights even with poor metadata

πŸ“Œ Here Comes Mamba: The Selective State Space Model πŸ—‚ Category: DEEP LEARNING πŸ•’ Date: 2024-09-03 | ⏱️ Read time: 22 min re
πŸ“Œ Here Comes Mamba: The Selective State Space Model πŸ—‚ Category: DEEP LEARNING πŸ•’ Date: 2024-09-03 | ⏱️ Read time: 22 min read Part 3 – Towards Mamba State Space Models for Images, Videos and Time Series

πŸ“Œ Diving Deeper with Structured Outputs πŸ—‚ Category: LARGE LANGUAGE MODELS πŸ•’ Date: 2024-09-03 | ⏱️ Read time: 10 min read E
πŸ“Œ Diving Deeper with Structured Outputs πŸ—‚ Category: LARGE LANGUAGE MODELS πŸ•’ Date: 2024-09-03 | ⏱️ Read time: 10 min read Enhancing our understanding and optimal usage of structured outputs

πŸ“Œ Approximating Stochastic Functions with Multivariate Outputs πŸ—‚ Category: πŸ•’ Date: 2024-09-04 | ⏱️ Read time: 25 min read
πŸ“Œ Approximating Stochastic Functions with Multivariate Outputs πŸ—‚ Category: πŸ•’ Date: 2024-09-04 | ⏱️ Read time: 25 min read A generic approach for training probabilistic machine learning models