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Machine Learning

Machine Learning

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

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📈 Analytical overview of Telegram channel Machine Learning

Channel Machine Learning (@machinelearning9) in the English language segment is an active participant. Currently, the community unites 40 373 subscribers, ranking 3 327 in the Technologies & Applications category and 225 in the Syria region.

📊 Audience metrics and dynamics

Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 40 373 subscribers.

According to the latest data from 12 July, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 399 over the last 30 days and by 24 over the last 24 hours, overall reach remains high.

  • Verification status: Not verified
  • Engagement rate (ER): The average audience engagement rate is 2.42%. Within the first 24 hours after publication, content typically collects 1.74% reactions from the total number of subscribers.
  • Post reach: On average, each post receives 979 views. Within the first day, a publication typically gains 703 views.
  • Reactions and interaction: The audience actively supports content: the average number of reactions per post is 4.
  • Thematic interests: Content is focused on key topics such as distance, insidead, gpu, learning, degree.

📝 Description and content policy

The author describes the resource as a platform for expressing subjective opinions:
Real Machine Learning — 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 13 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.

40 373
Subscribers
+2424 hours
+1257 days
+39930 days
Posts Archive
📌 A Visual Guide to Tuning Random Forest Hyperparameters 🗂 Category: DATA SCIENCE 🕒 Date: 2025-09-04 | ⏱️ Read time: 8 min
📌 A Visual Guide to Tuning Random Forest Hyperparameters 🗂 Category: DATA SCIENCE 🕒 Date: 2025-09-04 | ⏱️ Read time: 8 min read How hyperparameter tuning visually changes random forests

📌 Should We Use LLMs As If They Were Swiss Knives? 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2025-09-04 | ⏱️ Read time:
📌 Should We Use LLMs As If They Were Swiss Knives? 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2025-09-04 | ⏱️ Read time: 9 min read A logic game performance comparison between popular LLMs and a custom-made algorithm

📌 Tool Masking: The Layer MCP Forgot 🗂 Category: AGENTIC AI 🕒 Date: 2025-09-05 | ⏱️ Read time: 16 min read Tool masking fo
📌 Tool Masking: The Layer MCP Forgot 🗂 Category: AGENTIC AI 🕒 Date: 2025-09-05 | ⏱️ Read time: 16 min read Tool masking for AI improves AI agents: shape MCP tool surfaces to cut tokens and…

📌 Zero-Inflated Data: A Comparison of Regression Models 🗂 Category: DATA SCIENCE 🕒 Date: 2025-09-05 | ⏱️ Read time: 13 min
📌 Zero-Inflated Data: A Comparison of Regression Models 🗂 Category: DATA SCIENCE 🕒 Date: 2025-09-05 | ⏱️ Read time: 13 min read How to detect it and which model to choose.

📌 AI Operations Under the Hood: Challenges and Best Practices 🗂 Category: LLM APPLICATIONS 🕒 Date: 2025-09-05 | ⏱️ Read ti
📌 AI Operations Under the Hood: Challenges and Best Practices 🗂 Category: LLM APPLICATIONS 🕒 Date: 2025-09-05 | ⏱️ Read time: 18 min read Building robust, reproducible, and reliable GenAI applications requires a framework of continuous improvement, rigorous evaluation,…

📌 Showcasing Your Work on HuggingFace Spaces 🗂 Category: PRODUCTIVITY 🕒 Date: 2025-09-05 | ⏱️ Read time: 9 min read Buildi
📌 Showcasing Your Work on HuggingFace Spaces 🗂 Category: PRODUCTIVITY 🕒 Date: 2025-09-05 | ⏱️ Read time: 9 min read Building an app is exciting – but sharing it is where the real value kicks…

📌 How to Context Engineer to Optimize Question Answering Pipelines 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2025-09-05 |
📌 How to Context Engineer to Optimize Question Answering Pipelines 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2025-09-05 | ⏱️ Read time: 9 min read Learn how to apply context engineering to enhance your question answering systems.

📌 Tool Masking: The Layer MCP Forgot 🗂 Category: AGENTIC AI 🕒 Date: 2025-09-05T07:00:00-05:00 | ⏱ Read time: 16 min read T
📌 Tool Masking: The Layer MCP Forgot 🗂 Category: AGENTIC AI 🕒 Date: 2025-09-05T07:00:00-05:00 | ⏱ Read time: 16 min read Tool masking for AI improves AI agents: shape MCP tool surfaces to cut tokens and…

📌 Zero-Inflated Data: A Comparison of Regression Models 🗂 Category: DATA SCIENCE 🕒 Date: 2025-09-05T08:30:00-05:00 | ⏱ Rea
📌 Zero-Inflated Data: A Comparison of Regression Models 🗂 Category: DATA SCIENCE 🕒 Date: 2025-09-05T08:30:00-05:00 | ⏱ Read time: 13 min read How to detect it and which model to choose.

📌 AI Operations Under the Hood: Challenges and Best Practices 🗂 Category: LLM APPLICATIONS 🕒 Date: 2025-09-05T10:00:00-05:
📌 AI Operations Under the Hood: Challenges and Best Practices 🗂 Category: LLM APPLICATIONS 🕒 Date: 2025-09-05T10:00:00-05:00 | ⏱ Read time: 18 min read Building robust, reproducible, and reliable GenAI applications requires a framework of continuous improvement, rigorous evaluation,…

📌 Showcasing Your Work on HuggingFace Spaces 🗂 Category: PRODUCTIVITY 🕒 Date: 2025-09-05T11:30:00-05:00 | ⏱ Read time: 9 m
📌 Showcasing Your Work on HuggingFace Spaces 🗂 Category: PRODUCTIVITY 🕒 Date: 2025-09-05T11:30:00-05:00 | ⏱ Read time: 9 min read Building an app is exciting – but sharing it is where the real value kicks…

📌 How to Context Engineer to Optimize Question Answering Pipelines 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2025-09-05T12
📌 How to Context Engineer to Optimize Question Answering Pipelines 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2025-09-05T12:30:00-05:00 | ⏱ Read time: 9 min read Learn how to apply context engineering to enhance your question answering systems.

🔥 Trending Repository: ML-From-Scratch 📝 Description: Machine Learning From Scratch. Bare bones NumPy implementations of machine learning models and algorithms with a focus on accessibility. Aims to cover everything from linear regression to deep learning. 🔗 Repository URL: https://github.com/eriklindernoren/ML-From-Scratch 📖 Readme: https://github.com/eriklindernoren/ML-From-Scratch#readme 📊 Statistics: 🌟 Stars: 27.8K stars 👀 Watchers: 951 🍴 Forks: 4.8K forks 💻 Programming Languages: Python 🏷️ Related Topics:
#data_science #machine_learning #data_mining #deep_learning #genetic_algorithm #deep_reinforcement_learning #machine_learning_from_scratch
================================== 🧠 By: https://t.me/DataScienceM

No one believed I could double my portfolio in just 30 days… but then I discovered the Prop Mastery tricks top traders keep s
No one believed I could double my portfolio in just 30 days… but then I discovered the Prop Mastery tricks top traders keep secret. Want to know how? The real strategies are hidden right here — and only a few catch on before it’s too late! #إعلان InsideAds

“I was laughed at when I bought crypto in 2019. Now my portfolio’s up 1200% — and friends keep asking for ‘the secret’… But n
“I was laughed at when I bought crypto in 2019. Now my portfolio’s up 1200% — and friends keep asking for ‘the secret’… But nobody talks about the brutal truths I learned along the way. Want to see what everyone’s missing? 👉 See it here #إعلان InsideAds

Think you know your football history? Who scored a hat-trick against Real Madrid in yellow and black? Every day, Footy Riddle
Think you know your football history? Who scored a hat-trick against Real Madrid in yellow and black? Every day, Footy Riddles ⚽ drops clever puzzles about iconic players, unforgettable moments, and legendary matches. Challenge your football brain and see if you can guess before anyone else! Only the quickest fans know all the answers—are you one of them? #إعلان InsideAds

90% трейдеров сливают из-за ручного анализа и эмоций? TRUE — первый AI-протокол на Solana, который сам учится на on-chain дан
90% трейдеров сливают из-за ручного анализа и эмоций? TRUE — первый AI-протокол на Solana, который сам учится на on-chain данных, выдает персональные стратегии и делает трейдинг понятным на естественном языке. Именно сейчас можно получить доступ к запуску и участвовать в токенсейле $TRUE, пока условия максимально выгодные. Трейдинг будущего начинается здесь — не упусти шанс быть первым! Присоединяйся #إعلان InsideAds

What if you could skip all the chart noise and just ask AI to invest for you? 90% of traders lose money… but the game changes
What if you could skip all the chart noise and just ask AI to invest for you? 90% of traders lose money… but the game changes when you chat instead of chart. Be early—join the AI revolution before everyone else wakes up. #إعلان InsideAds

🔥 Trending Repository: abogen 📝 Description: Generate audiobooks from EPUBs, PDFs and text with synchronized captions. 🔗 R
🔥 Trending Repository: abogen 📝 Description: Generate audiobooks from EPUBs, PDFs and text with synchronized captions. 🔗 Repository URL: https://github.com/denizsafak/abogen 🌐 Website: https://pypi.org/project/abogen/ 📖 Readme: https://github.com/denizsafak/abogen#readme 📊 Statistics: 🌟 Stars: 3.1K stars 👀 Watchers: 18 🍴 Forks: 159 forks 💻 Programming Languages: Python - Batchfile - Dockerfile 🏷️ Related Topics:
#text_to_speech #audiobook #tts #speech_synthesis #subtitles #audiobooks #narrator #content_creator #voice_synthesis #epub_converter #kokoro #content_creation #text_to_audio #media_generation #kokoro_tts #kokoro_82m
================================== 🧠 By: https://t.me/DataScienceM

🔥 Trending Repository: cognitive-load 📝 Description: 🧠 Cognitive Load is what matters 🔗 Repository URL: https://github.co
🔥 Trending Repository: cognitive-load 📝 Description: 🧠 Cognitive Load is what matters 🔗 Repository URL: https://github.com/zakirullin/cognitive-load 📖 Readme: https://github.com/zakirullin/cognitive-load#readme 📊 Statistics: 🌟 Stars: 9.4K stars 👀 Watchers: 86 🍴 Forks: 200 forks 💻 Programming Languages: Not available 🏷️ Related Topics: Not available ================================== 🧠 By: https://t.me/DataScienceM