Machine Learning
Open in Telegram
Real Machine Learning — simple, practical, and built on experience. Learn step by step with clear explanations and working code. Admin: @HusseinSheikho || @Hussein_Sheikho
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Network:📈 Analytical overview of Telegram channel Machine Learning
Channel Machine Learning (@machinelearning9) in the English language segment is an active participant. Currently, the community unites 41 257 subscribers, ranking 3 163 in the Technologies & Applications category and 216 in the Syria region.
📊 Audience metrics and dynamics
Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 41 257 subscribers.
According to the latest data from 14 September, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 320 over the last 30 days and by 14 over the last 24 hours, overall reach remains high.
- Verification status: Not verified
- Engagement rate (ER): The average audience engagement rate is 3.04%. Within the first 24 hours after publication, content typically collects 1.66% reactions from the total number of subscribers.
- Post reach: On average, each post receives 1 253 views. Within the first day, a publication typically gains 683 views.
- Reactions and interaction: The audience actively supports content: the average number of reactions per post is 3.
- Thematic interests: Content is focused on key topics such as distance, insidead, gpu, learning, degree.
📝 Description and content policy
The author describes the resource as a platform for expressing subjective opinions:
“Real Machine Learning — simple, practical, and built on experience.
Learn step by step with clear explanations and working code.
Admin: @HusseinSheikho || @Hussein_Sheikho”
Thanks to the high frequency of updates (latest data received on 15 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 Technologies & Applications category.
41 257
Subscribers
+1424 hours
+1297 days
+32030 days
Data loading in progress...
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| Date | Subscriber Growth | Mentions | Channels | |
| 15 September | +12 | |||
| 14 September | +19 | |||
| 13 September | +41 | |||
| 12 September | +43 | |||
| 11 September | +5 | |||
| 10 September | +17 | |||
| 09 September | +6 | |||
| 08 September | +20 | |||
| 07 September | +21 | |||
| 06 September | +1 | |||
| 05 September | +10 | |||
| 04 September | +21 | |||
| 03 September | +14 | |||
| 02 September | +24 | |||
| 01 September | +6 |
Channel Posts
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df['age'].hist(bins=30)
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plt.scatter(df['x'], df['y'])
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sns.heatmap(df.corr(numeric_only=True), annot=True, cmap='coolwarm')
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Step
Focus
Key Action
1
Collect & Structure
Load + shape the data
2
Identify Issues
Missing, duplicates, outliers, types
3
Understand & Decide
Stats + viz + right questions
EXPLORE. UNDERSTAND. TURN DATA INTO INSIGHTS.
Better data → Better decisions* | 5 |
