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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 40 193 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 40 193 subscribers.

According to the latest data from 01 July, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 355 over the last 30 days and by 21 over the last 24 hours, overall reach remains high.

  • Verification status: Not verified
  • Engagement rate (ER): The average audience engagement rate is 2.04%. Within the first 24 hours after publication, content typically collects 2.12% reactions from the total number of subscribers.
  • Post reach: On average, each post receives 818 views. Within the first day, a publication typically gains 851 views.
  • Reactions and interaction: The audience actively supports content: the average number of reactions per post is 2.
  • 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 02 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.

40 193
Subscribers
+2124 hours
+857 days
+35530 days
Posts Archive
πŸ“Œ Optimizing Marketing Campaigns with Budgeted Multi-Armed Bandits πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-08-16 | ⏱️ Read t
πŸ“Œ Optimizing Marketing Campaigns with Budgeted Multi-Armed Bandits πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-08-16 | ⏱️ Read time: 10 min read With demos, our new solution, and a video

πŸ“Œ We Built an Open-Source Data Quality Testframework for PySpark πŸ—‚ Category: DATA ENGINEERING πŸ•’ Date: 2024-08-16 | ⏱️ Read
πŸ“Œ We Built an Open-Source Data Quality Testframework for PySpark πŸ—‚ Category: DATA ENGINEERING πŸ•’ Date: 2024-08-16 | ⏱️ Read time: 6 min read Measure and report your data quality with ease

πŸ“Œ Bad Assumptions – The Downfall of Even Experienced Data Scientists πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-08-16 | ⏱️ Read
πŸ“Œ Bad Assumptions – The Downfall of Even Experienced Data Scientists πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-08-16 | ⏱️ Read time: 11 min read Data can be deceptive, so be on your toes!

πŸ“Œ The Art of Chunking: Boosting AI Performance in RAG Architectures πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-08-18
πŸ“Œ The Art of Chunking: Boosting AI Performance in RAG Architectures πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-08-18 | ⏱️ Read time: 1 min read The Key to Effective AI-Driven Retrieval

πŸ“Œ The Art of Chunking: Boosting AI Performance in RAG Architectures πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-08-18
πŸ“Œ The Art of Chunking: Boosting AI Performance in RAG Architectures πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-08-18 | ⏱️ Read time: 15 min read The Key to Effective AI-Driven Retrieval

πŸ“Œ The Evolution of SQL πŸ—‚ Category: DATA ENGINEERING πŸ•’ Date: 2024-08-18 | ⏱️ Read time: 13 min read Unlocking the power of
πŸ“Œ The Evolution of SQL πŸ—‚ Category: DATA ENGINEERING πŸ•’ Date: 2024-08-18 | ⏱️ Read time: 13 min read Unlocking the power of large language models

πŸ“Œ The End of Required Work: Universal Basic Income and AI-Driven Prosperity πŸ—‚ Category: πŸ•’ Date: 2024-08-19 | ⏱️ Read time:
πŸ“Œ The End of Required Work: Universal Basic Income and AI-Driven Prosperity πŸ—‚ Category: πŸ•’ Date: 2024-08-19 | ⏱️ Read time: 16 min read How a tax on AI work might let everyone share the imminent bounty from AI…

πŸ“Œ Don’t Limit Your RAG Knowledgebase to Just Text πŸ—‚ Category: BUSINESS πŸ•’ Date: 2024-08-19 | ⏱️ Read time: 8 min read Steal
πŸ“Œ Don’t Limit Your RAG Knowledgebase to Just Text πŸ—‚ Category: BUSINESS πŸ•’ Date: 2024-08-19 | ⏱️ Read time: 8 min read Steal this plug-n-play Python script to easily implement images into your chatbot’s Knowledgebase

πŸ“Œ Unsupervised Learning Series: Exploring the Mean-Shift Algorithm πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-08-19
πŸ“Œ Unsupervised Learning Series: Exploring the Mean-Shift Algorithm πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-08-19 | ⏱️ Read time: 11 min read Let’s learn one of the most famous density-based clustering algorithms, Mean-Shift.

πŸ“Œ Speaker’s Privacy Protection in DNN-Based Speech Processing Tools πŸ—‚ Category: πŸ•’ Date: 2024-08-20 | ⏱️ Read time: 11 min
πŸ“Œ Speaker’s Privacy Protection in DNN-Based Speech Processing Tools πŸ—‚ Category: πŸ•’ Date: 2024-08-20 | ⏱️ Read time: 11 min read A novel method in privacy-preserving speech processing which anonymizes the speaker attributes using space-filling vector…

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Think crypto mining is just for whales? Discover how anyone can earn tokens and unlock upgrades and artifacts with Padma Web3’s play-to-earn ecosystem. Boost your mana, invite friends, and turn your time into real rewards β€” no special equipment needed. Curious about the next big thing? See what everyone is mining right now. Start now! #ad InsideAds

πŸ“Œ K Nearest Neighbor Classifier, Explained: A Visual Guide with Code Examples for Beginners πŸ—‚ Category: MACHINE LEARNING πŸ•’
πŸ“Œ K Nearest Neighbor Classifier, Explained: A Visual Guide with Code Examples for Beginners πŸ—‚ Category: MACHINE LEARNING πŸ•’ Date: 2024-08-20 | ⏱️ Read time: 9 min read The friendly neighbor approach to machine learning

πŸ“Œ Empowering Data-Driven Decisions: Embedding Trust in Text-to-SQL AI Agents πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2
πŸ“Œ Empowering Data-Driven Decisions: Embedding Trust in Text-to-SQL AI Agents πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-08-20 | ⏱️ Read time: 19 min read Simplify complex data environments for users utilizing reliable AI Agent systems towards better data-driven decision-making

πŸ“Œ Path Representation in Python πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-08-20 | ⏱️ Read time: 5 min read Stop using strings
πŸ“Œ Path Representation in Python πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-08-20 | ⏱️ Read time: 5 min read Stop using strings to represent paths and use pathlib instead

πŸ“Œ How to Effortlessly Extract Receipt Information with OCR and GPT-4o mini πŸ—‚ Category: πŸ•’ Date: 2024-08-20 | ⏱️ Read time:
πŸ“Œ How to Effortlessly Extract Receipt Information with OCR and GPT-4o mini πŸ—‚ Category: πŸ•’ Date: 2024-08-20 | ⏱️ Read time: 16 min read Utilize OCR and the powerful GPT-4o mini model to perform information extraction on receipts

πŸ“Œ Distance Metric Learning for Outlier Detection πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-08-20 | ⏱️ Read time: 23 min read A
πŸ“Œ Distance Metric Learning for Outlier Detection πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-08-20 | ⏱️ Read time: 23 min read An outlier detection method that determines a relevant distance metric between records

πŸ“Œ ChatGPT vs. Claude vs. Gemini for Data Analysis (Part 2): Who’s the Best at EDA? πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ D
πŸ“Œ ChatGPT vs. Claude vs. Gemini for Data Analysis (Part 2): Who’s the Best at EDA? πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-08-20 | ⏱️ Read time: 14 min read Five criteria to compare ChatGPT, Claude, and Gemini in tackling Exploratory Data Analysis

πŸ“Œ How to Forecast Hierarchical Time Series πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-08-20 | ⏱️ Read time: 13 min r
πŸ“Œ How to Forecast Hierarchical Time Series πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-08-20 | ⏱️ Read time: 13 min read A beginner’s guide to forecast reconciliation

πŸ“Œ Implementing Convolutional Neural Networks in TensorFlow πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-08-20 | ⏱️ Rea
πŸ“Œ Implementing Convolutional Neural Networks in TensorFlow πŸ—‚ Category: ARTIFICIAL INTELLIGENCE πŸ•’ Date: 2024-08-20 | ⏱️ Read time: 6 min read Step-by-step code guide to building a Convolutional Neural Network

πŸ“Œ Solving a Constrained Project Scheduling Problem with Quantum Annealing πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-08-20 | ⏱️
πŸ“Œ Solving a Constrained Project Scheduling Problem with Quantum Annealing πŸ—‚ Category: DATA SCIENCE πŸ•’ Date: 2024-08-20 | ⏱️ Read time: 29 min read Solving the resource constrained project scheduling problem (RCPSP) with D-Wave’s hybrid constrained quadratic model (CQM)