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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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πŸ“ˆ 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 67 833 subscribers, ranking 2 428 in the Education category and 5 035 in the India region.

πŸ“Š Audience metrics and dynamics

Since its creation on Π½Π΅Π²Ρ–Π΄ΠΎΠΌΠΎ, the project has demonstrated rapid growth, gathering an audience of 67 833 subscribers.

According to the latest data from 15 June, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 82 over the last 30 days and by 13 over the last 24 hours, overall reach remains high.

  • Verification status: Not verified
  • Engagement rate (ER): The average audience engagement rate is 4.40%. Within the first 24 hours after publication, content typically collects 1.74% reactions from the total number of subscribers.
  • Post reach: On average, each post receives 2 983 views. Within the first day, a publication typically gains 1 177 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.

πŸ“ Description and content policy

The author describes the resource as a platform for expressing subjective opinions:
β€œLearn 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 16 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.

67 833
Subscribers
+1324 hours
+187 days
+8230 days
Posts Archive
This channels is for Programmers, Coders, Software Engineers. 0- Python 1- Data Science 2- Machine Learning 3- Data Visualiza
This channels is for Programmers, Coders, Software Engineers. 0- Python 1- Data Science 2- Machine Learning 3- Data Visualization 4- Artificial Intelligence 5- Data Analysis 6- Statistics 7- Deep Learning 8- programming Languages βœ… https://t.me/addlist/8_rRW2scgfRhOTc0 βœ… https://t.me/DataScienceM

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This channels is for Programmers, Coders, Software Engineers. 0- Python 1- Data Science 2- Machine Learning 3- Data Visualiza
This channels is for Programmers, Coders, Software Engineers. 0- Python 1- Data Science 2- Machine Learning 3- Data Visualization 4- Artificial Intelligence 5- Data Analysis 6- Statistics 7- Deep Learning 8- programming Languages βœ… https://t.me/addlist/8_rRW2scgfRhOTc0 βœ… https://t.me/DataScienceM

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πŸš€ Join πŸ‘‰πŸ» "Code with Socialcoffee" πŸ‘ˆπŸ» Channel to dive into exciting world of AI! - 🌟 Learn about the latest AI breakthr
πŸš€ Join πŸ‘‰πŸ» "Code with Socialcoffee" πŸ‘ˆπŸ» Channel to dive into exciting world of AI! - 🌟 Learn about the latest AI breakthroughs and advancements in the field. - πŸ”¬ Get Free Access to AI software tools to boost productivity and enhance skills. - πŸ’‘ Explore the endless possibilities of AI technology and its impact on industries. - πŸ’» Gain insights on AI features like speech recognition and image processing. - 🌐 Stay updated on expert chat sessions and get your burning questions answered. - πŸ’₯ Be part of a vibrant AI community, share ideas, and network with like-minded individuals. - 🀩 Access free AI solutions! Join now πŸ‘‰πŸ» Code with Socialcoffee πŸ‘ˆπŸ»

A gentle introduction to K-Means Clustering in #R. Featuring Tidyclust! Enjoy. https://www.business-science.io/code-tools/202
A gentle introduction to K-Means Clustering in #R. Featuring Tidyclust! Enjoy. https://www.business-science.io/code-tools/2023/07/06/kmeans-clustering.html?el=twitter

Important book 50 Algorithms Every Programmer Should Know (2023) Read it https://t.me/DataScienceM/286

Deep Learning NLP AI Python ML Data Mining Tensorflow Keras πŸ‘‡πŸ‘‡πŸ‘‡πŸ‘‡πŸ‘‡ @Machine_learn
Deep Learning NLP AI Python ML Data Mining Tensorflow Keras πŸ‘‡πŸ‘‡πŸ‘‡πŸ‘‡πŸ‘‡ @Machine_learn

This channels is for Programmers, Coders, Software Engineers. 0- Python 1- Data Science 2- Machine Learning 3- Data Visualiza
This channels is for Programmers, Coders, Software Engineers. 0- Python 1- Data Science 2- Machine Learning 3- Data Visualization 4- Artificial Intelligence 5- Data Analysis 6- Statistics 7- Deep Learning 8- programming Languages https://t.me/addlist/8_rRW2scgfRhOTc0

We launched a special bot some time ago to download all scientific, software and mathematics books The bot contains more than thirty million books, and new books are downloaded first, In addition to the possibility of downloading all articles and scientific papers for free To request a subscription: talk to @Hussein_Sheikho

πŸ“© Python Email Automation Script import smtplib from email.mime.text import MIMEText sender_email = "your_email@example.com"
πŸ“© Python Email Automation Script import smtplib from email.mime.text import MIMEText sender_email = "your_email@example.com" recipient_email = "recipient_email@example.com" subject = "Automated Email" message = "This is an automated email sent using Python." # SMTP server configuration (example: Gmail) smtp_server = "smtp.gmail.com" smtp_port = 587 smtp_username = "your_username" smtp_password = "your_password" msg = MIMEText(message) msg["Subject"] = subject msg["From"] = sender_email msg["To"] = recipient_email try: server = smtplib.SMTP(smtp_server, smtp_port) server.starttls() server.login(smtp_username, smtp_password) server.sendmail(sender_email, recipient_email, msg.as_string()) print("Email sent successfully!") except Exception as e: print("Error sending email:", str(e)) finally: server.quit() https://t.me/DataScienceT