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Machine Learning

Machine Learning

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Real Machine Learning โ€” simple, practical, and built on experience. Learn step by step with clear explanations and working code. Admin: @HusseinSheikho || @Hussein_Sheikho

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๐Ÿ“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 40 151 subscribers, ranking 3 380 in the Technologies & Applications category and 228 in the Syria region.

๐Ÿ“Š Audience metrics and dynamics

Since its creation on ะฝะตะฒั–ะดะพะผะพ, the project has demonstrated rapid growth, gathering an audience of 40 151 subscribers.

According to the latest data from 29 June, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 380 over the last 30 days and by 3 over the last 24 hours, overall reach remains high.

  • Verification status: Not verified
  • Engagement rate (ER): The average audience engagement rate is 2.08%. 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 837 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.

๐Ÿ“ Description and content policy

The author describes the resource as a platform for expressing subjective opinions:
โ€œReal Machine Learning โ€” simple, practical, and built on experience. Learn step by step with clear explanations and working code. Admin: @HusseinSheikho || @Hussein_Sheikhoโ€

Thanks to the high frequency of updates (latest data received on 30 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.

40 151
Subscribers
+324 hours
+1157 days
+38030 days
Posts Archive
๐Ÿ“Œ Event Study Designs: A Beginnerโ€™s Guide ๐Ÿ—‚ Category: DATA SCIENCE ๐Ÿ•’ Date: 2024-07-20 | โฑ๏ธ Read time: 10 min read Event St
๐Ÿ“Œ Event Study Designs: A Beginnerโ€™s Guide ๐Ÿ—‚ Category: DATA SCIENCE ๐Ÿ•’ Date: 2024-07-20 | โฑ๏ธ Read time: 10 min read Event Study Designs: What are they and what are they not.

๐Ÿ“Œ Full Guide to Building a Professional Portfolio with Python, Markdown, Git, and GitHub Pages ๐Ÿ—‚ Category: ๐Ÿ•’ Date: 2024-07
๐Ÿ“Œ Full Guide to Building a Professional Portfolio with Python, Markdown, Git, and GitHub Pages ๐Ÿ—‚ Category: ๐Ÿ•’ Date: 2024-07-20 | โฑ๏ธ Read time: 11 min read This article is a end-to-end guide to build a professional portfolio for developpers and dataโ€ฆ

๐Ÿ“Œ Modern Enterprise Data Modeling ๐Ÿ—‚ Category: DATA ENGINEERING ๐Ÿ•’ Date: 2024-07-20 | โฑ๏ธ Read time: 13 min read How to addre
๐Ÿ“Œ Modern Enterprise Data Modeling ๐Ÿ—‚ Category: DATA ENGINEERING ๐Ÿ•’ Date: 2024-07-20 | โฑ๏ธ Read time: 13 min read How to address the shortcomings of shallow, outdated models and future-proof your modeling strategy

๐Ÿ“Œ Forecasting in the Age of Foundation Models ๐Ÿ—‚ Category: ARTIFICIAL INTELLIGENCE ๐Ÿ•’ Date: 2024-07-20 | โฑ๏ธ Read time: 17 mi
๐Ÿ“Œ Forecasting in the Age of Foundation Models ๐Ÿ—‚ Category: ARTIFICIAL INTELLIGENCE ๐Ÿ•’ Date: 2024-07-20 | โฑ๏ธ Read time: 17 min read Benchmarking Lag-Llama against XGBoost

๐Ÿ“Œ Letโ€™s reproduce NanoGPT with JAX!(Part 1) ๐Ÿ—‚ Category: ๐Ÿ•’ Date: 2024-07-21 | โฑ๏ธ Read time: 8 min read Part 1: Build 124M G
๐Ÿ“Œ Letโ€™s reproduce NanoGPT with JAX!(Part 1) ๐Ÿ—‚ Category: ๐Ÿ•’ Date: 2024-07-21 | โฑ๏ธ Read time: 8 min read Part 1: Build 124M GPT2 with JAX. Part 2: Optimize the training speed in Singleโ€ฆ

๐Ÿ“Œ How To Start Technical Writing & Blogging ๐Ÿ—‚ Category: ARTIFICIAL INTELLIGENCE ๐Ÿ•’ Date: 2024-07-21 | โฑ๏ธ Read time: 8 min r
๐Ÿ“Œ How To Start Technical Writing & Blogging ๐Ÿ—‚ Category: ARTIFICIAL INTELLIGENCE ๐Ÿ•’ Date: 2024-07-21 | โฑ๏ธ Read time: 8 min read Why writing data science blogs changed my career.

๐Ÿ“Œ Advanced Data Modelling ๐Ÿ—‚ Category: ANALYTICS ๐Ÿ•’ Date: 2024-07-21 | โฑ๏ธ Read time: 16 min read Data model layers, environm
๐Ÿ“Œ Advanced Data Modelling ๐Ÿ—‚ Category: ANALYTICS ๐Ÿ•’ Date: 2024-07-21 | โฑ๏ธ Read time: 16 min read Data model layers, environments, tests and data quality explained

๐Ÿ“Œ Pythonโ€™s Parallel Paradigm Shift ๐Ÿ—‚ Category: DATA ENGINEERING ๐Ÿ•’ Date: 2024-07-22 | โฑ๏ธ Read time: 13 min read Exploring t
๐Ÿ“Œ Pythonโ€™s Parallel Paradigm Shift ๐Ÿ—‚ Category: DATA ENGINEERING ๐Ÿ•’ Date: 2024-07-22 | โฑ๏ธ Read time: 13 min read Exploring the performance potential of a GIL-free Python

๐Ÿ“Œ You Donโ€™t Need Matplotlib When Pandas Is Enough for Data Visualisation ๐Ÿ—‚ Category: DATA SCIENCE ๐Ÿ•’ Date: 2024-07-22 | โฑ๏ธ
๐Ÿ“Œ You Donโ€™t Need Matplotlib When Pandas Is Enough for Data Visualisation ๐Ÿ—‚ Category: DATA SCIENCE ๐Ÿ•’ Date: 2024-07-22 | โฑ๏ธ Read time: 6 min read One line of code to plot data makes routine EDA jobs easier

๐Ÿ“Œ Quantifying Burned Areas from Wildfires Using Satellite Imagery ๐Ÿ—‚ Category: CLIMATE CHANGE ๐Ÿ•’ Date: 2024-07-22 | โฑ๏ธ Read
๐Ÿ“Œ Quantifying Burned Areas from Wildfires Using Satellite Imagery ๐Ÿ—‚ Category: CLIMATE CHANGE ๐Ÿ•’ Date: 2024-07-22 | โฑ๏ธ Read time: 9 min read Determining the burned area in forests due to wildfires using Sentinel-2 images with Python inโ€ฆ

๐Ÿ“Œ LangChainโ€™s Parent Document Retriever โ€“ Revisited ๐Ÿ—‚ Category: ๐Ÿ•’ Date: 2024-07-22 | โฑ๏ธ Read time: 7 min read Enhance retr
๐Ÿ“Œ LangChainโ€™s Parent Document Retriever โ€“ Revisited ๐Ÿ—‚ Category: ๐Ÿ•’ Date: 2024-07-22 | โฑ๏ธ Read time: 7 min read Enhance retrieval with context using your vector database only

๐Ÿ“Œ What Is Causal Inference? ๐Ÿ—‚ Category: STATISTICS ๐Ÿ•’ Date: 2024-07-22 | โฑ๏ธ Read time: 13 min read A beginnerโ€™s guide to ca
๐Ÿ“Œ What Is Causal Inference? ๐Ÿ—‚ Category: STATISTICS ๐Ÿ•’ Date: 2024-07-22 | โฑ๏ธ Read time: 13 min read A beginnerโ€™s guide to causal inference methods: randomized controlled trials, difference-in-differences, synthetic control, and A/Bโ€ฆ

๐Ÿ“Œ Linear Programming Optimization: Foundations ๐Ÿ—‚ Category: DATA SCIENCE ๐Ÿ•’ Date: 2024-07-22 | โฑ๏ธ Read time: 12 min read Par
๐Ÿ“Œ Linear Programming Optimization: Foundations ๐Ÿ—‚ Category: DATA SCIENCE ๐Ÿ•’ Date: 2024-07-22 | โฑ๏ธ Read time: 12 min read Part 1 โ€“ Basic Concepts and Examples

๐Ÿ“Œ Boost Your Data Science Job Hunt During Tech Layoffs, Part I ๐Ÿ—‚ Category: DATA SCIENCE ๐Ÿ•’ Date: 2024-07-23 | โฑ๏ธ Read time:
๐Ÿ“Œ Boost Your Data Science Job Hunt During Tech Layoffs, Part I ๐Ÿ—‚ Category: DATA SCIENCE ๐Ÿ•’ Date: 2024-07-23 | โฑ๏ธ Read time: 7 min read With these 5 actionable steps

๐Ÿ“Œ Dreaming in Blocks โ€” MineWorld, the Minecraft World Model ๐Ÿ—‚ Category: ARTIFICIAL INTELLIGENCE ๐Ÿ•’ Date: 2025-10-10 | โฑ๏ธ Re
๐Ÿ“Œ Dreaming in Blocksโ€Šโ€”โ€ŠMineWorld, the Minecraft World Model ๐Ÿ—‚ Category: ARTIFICIAL INTELLIGENCE ๐Ÿ•’ Date: 2025-10-10 | โฑ๏ธ Read time: 11 min read Explaining โ€œMineWorld: A real-time and open-source interactive world model on Minecraftโ€ in simple terms.

๐Ÿ“Œ 10 Data + AI Observations for Fall 2025 ๐Ÿ—‚ Category: DATA SCIENCE ๐Ÿ•’ Date: 2025-10-10 | โฑ๏ธ Read time: 10 min read Whatโ€™s h
๐Ÿ“Œ 10 Data + AI Observations for Fall 2025 ๐Ÿ—‚ Category: DATA SCIENCE ๐Ÿ•’ Date: 2025-10-10 | โฑ๏ธ Read time: 10 min read Whatโ€™s happeningโ€”and whatโ€™s nextโ€” for data and AI at the close of 2025.

๐Ÿ“Œ Python Poetry โ€“ The Best Data Science Dependency Management Tool? ๐Ÿ—‚ Category: DATA SCIENCE ๐Ÿ•’ Date: 2024-07-30 | โฑ๏ธ Read
๐Ÿ“Œ Python Poetry โ€“ The Best Data Science Dependency Management Tool? ๐Ÿ—‚ Category: DATA SCIENCE ๐Ÿ•’ Date: 2024-07-30 | โฑ๏ธ Read time: 8 min read Poetry makes deploying machine learning applications a breeze โ€“ learn how!

๐Ÿ“Œ How the Rise of Tabular Foundation Models Is Reshaping Data Science ๐Ÿ—‚ Category: ARTIFICIAL INTELLIGENCE ๐Ÿ•’ Date: 2025-10-
๐Ÿ“Œ How the Rise of Tabular Foundation Models Is Reshaping Data Science ๐Ÿ—‚ Category: ARTIFICIAL INTELLIGENCE ๐Ÿ•’ Date: 2025-10-09 | โฑ๏ธ Read time: 13 min read A turning point for data analysis?

๐Ÿ“Œ Past is Prologue: How Conversational Analytics Is Changing Data Work ๐Ÿ—‚ Category: DATA SCIENCE ๐Ÿ•’ Date: 2025-10-09 | โฑ๏ธ Re
๐Ÿ“Œ Past is Prologue: How Conversational Analytics Is Changing Data Work ๐Ÿ—‚ Category: DATA SCIENCE ๐Ÿ•’ Date: 2025-10-09 | โฑ๏ธ Read time: 7 min read The future of reporting will be about encoding the value proposition of a product intoโ€ฆ

๐Ÿ“Œ The Intersection of Memory and Grounding in AI Systems ๐Ÿ—‚ Category: ARTIFICIAL INTELLIGENCE ๐Ÿ•’ Date: 2024-07-24 | โฑ๏ธ Read
๐Ÿ“Œ The Intersection of Memory and Grounding in AI Systems ๐Ÿ—‚ Category: ARTIFICIAL INTELLIGENCE ๐Ÿ•’ Date: 2024-07-24 | โฑ๏ธ Read time: 8 min read Understanding the 4 key types of memory, the methods of language model grounding, and theโ€ฆ