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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 68 136 subscribers, ranking 2 365 in the Education category and 4 731 in the India region.

📊 Audience metrics and dynamics

Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 68 136 subscribers.

According to the latest data from 31 August, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 80 over the last 30 days and by 1 over the last 24 hours, overall reach remains high.

  • Verification status: Not verified
  • Engagement rate (ER): The average audience engagement rate is 4.09%. Within the first 24 hours after publication, content typically collects 1.54% reactions from the total number of subscribers.
  • Post reach: On average, each post receives 2 784 views. Within the first day, a publication typically gains 1 052 views.
  • Reactions and interaction: The audience actively supports content: the average number of reactions per post is 5.
  • 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 01 September, 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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68 136
Subscribers
+124 hours
-97 days
+8030 days
Posts Archive
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This repository contains a collection of everything needed to work with libraries related to AI and LLM. More than 120 libraries, sorted by stages of LLM development: → Training, fine-tuning, and evaluation of LLM models → Integration and deployment of applications with LLM and RAG → Fast and scalable model launching → Working with data: extraction, structuring, and synthetic generation → Creating autonomous agents based on LLM → Prompt optimization and ensuring safe use in production 🌟 link: https://github.com/Shubhamsaboo/awesome-llm-apps 👉 @codeprogrammer

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