Machine Learning with Python
Learn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers. Admin: @HusseinSheikho || @Hussein_Sheikho
Show moreπ 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 67 804 subscribers, ranking 2 419 in the Education category and 4 901 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 67 804 subscribers.
According to the latest data from 23 June, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 107 over the last 30 days and by 23 over the last 24 hours, overall reach remains high.
- Verification status: Not verified
- Engagement rate (ER): The average audience engagement rate is 4.73%. Within the first 24 hours after publication, content typically collects 1.30% reactions from the total number of subscribers.
- Post reach: On average, each post receives 3 210 views. Within the first day, a publication typically gains 884 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 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 24 June, 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.
s = pd.Series([10, 15, 20, 25, 30])
s.loc[s > 20].loc[s % 2 == 1]
The problem is that the second .loc again looks at the original s, not the already filtered result. The logic gets messy. π€―
It's more reliable to gather everything into one expression:
s = pd.Series([10, 15, 20, 25, 30])
mask = (s > 20) & (s % 2 == 1)
result = s.loc[mask]
One mask, one point of truth. β
It's easier to debug. Fewer surprises when the code grows. π
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