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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π 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 68 482 subscribers, ranking 2 406 in the Education category and 4 586 in the India region.
π Audience metrics and dynamics
Since its creation on Π½Π΅Π²ΡΠ΄ΠΎΠΌΠΎ, the project has demonstrated rapid growth, gathering an audience of 68 482 subscribers.
According to the latest data from 04 November, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by -75 over the last 30 days and by 7 over the last 24 hours, overall reach remains high.
- Verification status: Not verified
- Engagement rate (ER): The average audience engagement rate is 3.38%. Within the first 24 hours after publication, content typically collects 1.57% reactions from the total number of subscribers.
- Post reach: On average, each post receives 2 314 views. Within the first day, a publication typically gains 1 073 views.
- Reactions and interaction: The audience actively supports content: the average number of reactions per post is 9.
- Thematic interests: Content is focused on key topics such as insidead, learning, degree, evaluation, algorithm.
π Description and content policy
The author describes the resource as a platform for expressing subjective opinions:
βLearn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers.
Admin: @HusseinSheikho || @Hussein_Sheikhoβ
Thanks to the high frequency of updates (latest data received on 05 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.
68 482
Subscribers
+724 hours
-597 days
-7530 days
Posts Archive
Fre Books and Courses (important guide)
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βͺ Installing and using this plugin is very simple.π
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5 of the best Kaggle datasets
πΈ For data science projects (in finance)
π¨π»βπ» If you are looking for datasets to do financial projects, the datasets presented on the Kaggle site can be a great option.
βͺ These datasets are usually clean and ready to use and are very suitable for machine learning models. Some of these datasets are even updated daily and you can use them for deeper analysis.π
1οΈβ£ S&P 500 stock dataset (daily update)
π Link: S&P 500 Stocks
2οΈβ£ Database of loans and debts
π Link: Loans & Liability
3οΈβ£ Dataset of frequent use of credit card
π Link: Credit Card Spending Habits
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π Link: Company Bankruptcy Prediction
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π Link: Credit score classification
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