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Machine Learning with Python

Machine Learning with Python

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Learn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers. Admin: @HusseinSheikho || @Hussein_Sheikho

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📈 Analytical overview of Telegram channel Machine Learning with Python

Channel Machine Learning with Python (@codeprogrammer) in the English language segment is an active participant. Currently, the community unites 68 477 subscribers, ranking 2 400 in the Education category and 4 598 in the India region.

📊 Audience metrics and dynamics

Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 68 477 subscribers.

According to the latest data from 31 October, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 12 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 3.99%. Within the first 24 hours after publication, content typically collects 1.46% reactions from the total number of subscribers.
  • Post reach: On average, each post receives 2 735 views. Within the first day, a publication typically gains 998 views.
  • Reactions and interaction: The audience actively supports content: the average number of reactions per post is 8.
  • Thematic interests: Content is focused on key topics such as insidead, learning, degree, evaluation, algorithm.

📝 Description and content policy

The author describes the resource as a platform for expressing subjective opinions:
“Learn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers. Admin: @HusseinSheikho || @Hussein_Sheikho”

Thanks to the high frequency of updates (latest data received on 02 November, 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 Education category.

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68 477
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-947 days
+1230 days
Posts Archive
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Repost from Machine Learning
🔥 Trending Repository: free-programming-books 📝 Description: 📚 Freely available programming books 🔗 Repository URL: https://github.com/EbookFoundation/free-programming-books 🌐 Website: https://ebookfoundation.github.io/free-programming-books/ 📖 Readme: https://github.com/EbookFoundation/free-programming-books#readme 📊 Statistics: 🌟 Stars: 366K stars 👀 Watchers: 🍴 Forks: 63.9K forks 💻 Programming Languages: Python - HTML 🏷️ Related Topics:
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================================== 🧠 By: https://t.me/DataScienceM

Eye control with Python EyeGestures is an open-source Python library for gaze tracking. It uses regular cameras, webcams, or smartphone cameras to track eye movement, fixations, and blinks, and convert these into events such as cursor control by eye movement. The JavaScript version allows running everything directly in the browser. Installation:
$ git clone https://github.com/NativeSensors/EyeGestures.git
$ cd EyeGestures
$ pip install -r requirements.txt
or
python3 -m pip install eyeGestures
Open source code, documentation, and usage examples - click 👉 https://t.me/DataScienceN

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This GitHub repo is a gold mine for EVERY data scientist! DS Interactive Python repo has interactive dashboards to learn statistics, ML models, and other DS concepts. Topics include PCA, bagging & boosting, clustering, neural networks, etc. Fully open-source and free! https://github.com/GeostatsGuy/DataScienceInteractivePython https://t.me/CodeProgrammer 🔗

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Repost from Data Analytics
📌 How to Create a Real-Time Gesture-to-Text Translator Using Python and Mediapipe ✍️ OMOTAYO OMOYEMI 🏷️ #Computer_Vision
📌 How to Create a Real-Time Gesture-to-Text Translator Using Python and Mediapipe ✍️ OMOTAYO OMOYEMI 🏷️ #Computer_Vision

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One of the best free courses on LLMs is the Recent Advances on Foundation Models course from the University of Waterloo, and
One of the best free courses on LLMs is the Recent Advances on Foundation Models course from the University of Waterloo, and it has 21 lectures under these five sections: 1. Introduction to Foundation Models 2. Transformer Architecture 3. Large Language Models 4. (Large) Multimodal Models 5. Augmenting Foundation Models ➡️ Course link: https://cs.uwaterloo.ca/~wenhuche/teaching/cs886/ https://t.me/CodeProgrammer 🔗

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Let's Learn MCP with Python - Tutorial Series Here's your comprehensive guide to understanding and building Model Context Protocol (MCP) Servers for Python developers through interactive learning experiences. Repo: https://github.com/microsoft/lets-learn-mcp-python?tab=readme-ov-file

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