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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 Optimizing the Data Processing Performance in PySpark πŸ—‚ Category: πŸ•’ Date: 2024-11-07 | ⏱️ Read time: 10 min read PySpark
πŸ“Œ Optimizing the Data Processing Performance in PySpark πŸ—‚ Category: πŸ•’ Date: 2024-11-07 | ⏱️ Read time: 10 min read PySpark techniques and strategies to tackle common performance challenges: A practical walkthrough

πŸ“Œ Beyond Math and Python: The Other Key Data Science Skills You Should Develop πŸ—‚ Category: CAREER ADVICE πŸ•’ Date: 2024-11-0
πŸ“Œ Beyond Math and Python: The Other Key Data Science Skills You Should Develop πŸ—‚ Category: CAREER ADVICE πŸ•’ Date: 2024-11-07 | ⏱️ Read time: 4 min read Our weekly selection of must-read Editors’ Picks and original features

πŸ“Œ An Illusion of Life πŸ—‚ Category: LARGE LANGUAGE MODELS πŸ•’ Date: 2024-11-07 | ⏱️ Read time: 9 min read Could existing AI po
πŸ“Œ An Illusion of Life πŸ—‚ Category: LARGE LANGUAGE MODELS πŸ•’ Date: 2024-11-07 | ⏱️ Read time: 9 min read Could existing AI possibly be sentient? If not, what’s missing?

πŸ“Œ Watermarking for AI Text and Synthetic Proteins: Fighting Misinformation and Bioterrorism πŸ—‚ Category: πŸ•’ Date: 2024-11-07
πŸ“Œ Watermarking for AI Text and Synthetic Proteins: Fighting Misinformation and Bioterrorism πŸ—‚ Category: πŸ•’ Date: 2024-11-07 | ⏱️ Read time: 9 min read Understanding AI applications in bio for machine learning engineers

πŸ“Œ Rethinking LLM Benchmarks: Measuring True Reasoning Beyond Training Data πŸ—‚ Category: APPLE πŸ•’ Date: 2024-11-07 | ⏱️ Read
πŸ“Œ Rethinking LLM Benchmarks: Measuring True Reasoning Beyond Training Data πŸ—‚ Category: APPLE πŸ•’ Date: 2024-11-07 | ⏱️ Read time: 6 min read Apple’s New LLM Benchmark, GSM-Symbolic

πŸ“Œ Operational and Analytical Data πŸ—‚ Category: DATA ENGINEERING πŸ•’ Date: 2024-11-07 | ⏱️ Read time: 9 min read What is the d
πŸ“Œ Operational and Analytical Data πŸ—‚ Category: DATA ENGINEERING πŸ•’ Date: 2024-11-07 | ⏱️ Read time: 9 min read What is the difference and how should we treat data in the enterprise?

πŸ“Œ How to Query a Knowledge Graph with LLMs Using gRAG πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-11-07 | ⏱️ Read time: 28 min r
πŸ“Œ How to Query a Knowledge Graph with LLMs Using gRAG πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-11-07 | ⏱️ Read time: 28 min read Google, Microsoft, LinkedIn, and many more tech companies are using Graph RAG. Why? Let’s understand…

πŸ“Œ Why Is PoC Becoming Obsolete in the AI Era? πŸ—‚ Category: πŸ•’ Date: 2024-11-07 | ⏱️ Read time: 9 min read I recently had the
πŸ“Œ Why Is PoC Becoming Obsolete in the AI Era? πŸ—‚ Category: πŸ•’ Date: 2024-11-07 | ⏱️ Read time: 9 min read I recently had the chance to join the OxML 2024 program, which brings together people…

πŸ“Œ To Index or Not to Index πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-11-08 | ⏱️ Read time: 20 min read Leverage SQL indexing t
πŸ“Œ To Index or Not to Index πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-11-08 | ⏱️ Read time: 20 min read Leverage SQL indexing to speed up your queries. Learn when to index, when not to,…

πŸ“Œ A 6-Month Detailed Plan to Build Your Junior Data Science Portfolio πŸ—‚ Category: CAREER ADVICE πŸ•’ Date: 2024-11-08 | ⏱️ Re
πŸ“Œ A 6-Month Detailed Plan to Build Your Junior Data Science Portfolio πŸ—‚ Category: CAREER ADVICE πŸ•’ Date: 2024-11-08 | ⏱️ Read time: 13 min read Step-by-step guide to creating, polishing, and deploying a portfolio that helps you land your first…

πŸ“Œ Vision Transformer with BatchNorm: Optimizing the depth πŸ—‚ Category: DEEP LEARNING πŸ•’ Date: 2024-11-08 | ⏱️ Read time: 16
πŸ“Œ Vision Transformer with BatchNorm: Optimizing the depth πŸ—‚ Category: DEEP LEARNING πŸ•’ Date: 2024-11-08 | ⏱️ Read time: 16 min read How integrating BatchNorm in a standard Vision transformer architecture results in faster convergence for a…

πŸ“Œ Predicting Every Election Since 1916 πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-11-08 | ⏱️ Read time: 10 min read How β€œelecti
πŸ“Œ Predicting Every Election Since 1916 πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-11-08 | ⏱️ Read time: 10 min read How β€œelection pundit predictions” betray a misunderstanding of probability

πŸ“Œ Reranking Using Huggingface Transformers for Optimizing Retrieval in RAG Pipelines πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024
πŸ“Œ Reranking Using Huggingface Transformers for Optimizing Retrieval in RAG Pipelines πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-11-08 | ⏱️ Read time: 9 min read Understanding when reranking makes a difference

πŸ“Œ Preference Alignment for Everyone! πŸ—‚ Category: πŸ•’ Date: 2024-11-08 | ⏱️ Read time: 32 min read Frugal RLHF with multi-ada
πŸ“Œ Preference Alignment for Everyone! πŸ—‚ Category: πŸ•’ Date: 2024-11-08 | ⏱️ Read time: 32 min read Frugal RLHF with multi-adapter PPO on Amazon SageMaker

πŸ“Œ Introducing the New Anthropic Token Counting API πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-11-08 | ⏱️ Read time:
πŸ“Œ Introducing the New Anthropic Token Counting API πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-11-08 | ⏱️ Read time: 9 min read Keep a closer eye on your costs when using Claude

πŸ“Œ The Statistical Significance Scam πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-11-09 | ⏱️ Read time: 15 min read A detailed loo
πŸ“Œ The Statistical Significance Scam πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-11-09 | ⏱️ Read time: 15 min read A detailed look into the flaws of science’s favorite tool

πŸ“Œ Top Data Science Career Questions, Answered πŸ—‚ Category: CAREER ADVICE πŸ•’ Date: 2024-11-09 | ⏱️ Read time: 7 min read I’ve
πŸ“Œ Top Data Science Career Questions, Answered πŸ—‚ Category: CAREER ADVICE πŸ•’ Date: 2024-11-09 | ⏱️ Read time: 7 min read I’ve been a data scientist for over 3 years. This is what most people want…

πŸ“Œ Core AI For Any Rummy Variant πŸ—‚ Category: MACHINE LEARNING πŸ•’ Date: 2024-11-09 | ⏱️ Read time: 12 min read Step by Step g
πŸ“Œ Core AI For Any Rummy Variant πŸ—‚ Category: MACHINE LEARNING πŸ•’ Date: 2024-11-09 | ⏱️ Read time: 12 min read Step by Step guide to a Rummy AI

πŸ“Œ Creating Dynamic Pivots on Snowflake Tables with dbt πŸ—‚ Category: DATA ENGINEERING πŸ•’ Date: 2024-11-09 | ⏱️ Read time: 6 m
πŸ“Œ Creating Dynamic Pivots on Snowflake Tables with dbt πŸ—‚ Category: DATA ENGINEERING πŸ•’ Date: 2024-11-09 | ⏱️ Read time: 6 min read Leverage dbt and its advanced scripting functionality to generate dynamic pivot tables that adapt to…

πŸ“Œ Solving the classic Betting on the World Series problem using hill climbing πŸ—‚ Category: πŸ•’ Date: 2024-11-10 | ⏱️ Read tim
πŸ“Œ Solving the classic Betting on the World Series problem using hill climbing πŸ—‚ Category: πŸ•’ Date: 2024-11-10 | ⏱️ Read time: 18 min read A simple example of hill climbing – and solving a problem that’s difficult to solve…