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Coding Courses

Coding Courses

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📈 Analytical overview of Telegram channel Coding Courses

Channel Coding Courses (@coding_learning1) in the English language segment is an active participant. Currently, the community unites 18 359 subscribers, ranking 10 928 in the Education category and 22 999 in the India region.

📊 Audience metrics and dynamics

Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 18 359 subscribers.

According to the latest data from 29 July, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 413 over the last 30 days and by 11 over the last 24 hours, overall reach remains high.

  • Verification status: Not verified
  • Engagement rate (ER): The average audience engagement rate is 13.41%. Within the first 24 hours after publication, content typically collects N/A% reactions from the total number of subscribers.
  • Post reach: On average, each post receives 2 461 views. Within the first day, a publication typically gains 0 views.
  • Reactions and interaction: The audience actively supports content: the average number of reactions per post is 23.
  • Thematic interests: Content is focused on key topics such as javascript, array, html, learning, recognition.

📝 Description and content policy

The author describes the resource as a platform for expressing subjective opinions:
🔴Declaimer🔴 We do not hold this content or stored in our drive, we just redirect telegram or website link where this content is already shared

Thanks to the high frequency of updates (latest data received on 30 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 Education category.

18 359
Subscribers
+1124 hours
+1237 days
+41330 days
Posts Archive
16. Career Advice + Extra Bits.zip725.55 MB

15. Storytelling + Communication How To Present Your Work.zip118.68 MB

14_Neural_Networks_Deep_Learning,_Transfer_Learning_and_TensorFlow.zip510.30 MB

14_Neural_Networks_Deep_Learning,_Transfer_Learning_and_TensorFlow.zip620.01 MB

14_Neural_Networks_Deep_Learning,_Transfer_Learning_and_TensorFlow.zip632.05 MB

14_Neural_Networks_Deep_Learning,_Transfer_Learning_and_TensorFlow.zip643.33 MB

14_Neural_Networks_Deep_Learning,_Transfer_Learning_and_TensorFlow.zip690.87 MB

14_Neural_Networks_Deep_Learning,_Transfer_Learning_and_TensorFlow.zip688.82 MB

13. Data Engineering.zip281.75 MB

12. Milestone Project 2 Supervised Learning - Part 03.zip519.78 MB

12. Milestone Project 2 Supervised Learning - Part 02.zip501.13 MB

12. Milestone Project 2 Supervised Learning - Part 01.zip716.98 MB

11. Milestone Project 1 Supervised Learning - Part 03.zip384.97 MB

11. Milestone Project 1 Supervised Learning - Part 02.zip522.51 MB

11. Milestone Project 1 Supervised Learning - Part 01.zip758.30 MB

10. Supervised Learning Classification + Regression.zip0.01 KB

9. Scikit-learn Creating Machine Learning Models - Part 06.zip514.31 MB

9. Scikit-learn Creating Machine Learning Models - Part 05.zip470.18 MB

9. Scikit-learn Creating Machine Learning Models - Part 04.zip579.65 MB

9. Scikit-learn Creating Machine Learning Models - Part 03.zip547.32 MB