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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 68 175 subscribers, ranking 2 377 in the Education category and 4 732 in the India region.

📊 Audience metrics and dynamics

Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 68 175 subscribers.

According to the latest data from 07 September, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 26 over the last 30 days and by 15 over the last 24 hours, overall reach remains high.

  • Verification status: Not verified
  • Engagement rate (ER): The average audience engagement rate is 2.39%. Within the first 24 hours after publication, content typically collects 1.49% reactions from the total number of subscribers.
  • Post reach: On average, each post receives 1 627 views. Within the first day, a publication typically gains 1 019 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 08 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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from wordcloud import WordCloud

import matplotlib.pyplot as plt

# Read text from a file
with open('cl.txt', 'r', encoding='utf-8') as file:
text = file.read()

# Generate word cloud
wordcloud = WordCloud(width=800, height=400, background_color='white').generate(text)

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