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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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šŸ“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 68 482 subscribers, ranking 2 406 in the Education category and 4 586 in the India region.

šŸ“Š Audience metrics and dynamics

Since its creation on невіГомо, 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.

šŸ“ Description and content policy

The author describes the resource as a platform for expressing subjective opinions:
ā€œLearn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers. Admin: @HusseinSheikho || @Hussein_Sheikhoā€

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.

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68 482
Subscribers
+724 hours
-597 days
-7530 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 āœ… https://t.me/addlist/8_rRW2scgfRhOTc0 āœ… https://t.me/DataScienceM

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

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

šŸ“© Python Email Automation Script import smtplib from email.mime.text import MIMEText sender_email = "your_email@example.com"
šŸ“© 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

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

Building an Image Recognition API using Flask. Step 1: Set up the project environment 1. Create a new directory for your proj
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Building an Image Recognition API using Flask. Step 1: Set up the project environment 1. Create a new directory for your project and navigate to it. 2. Create a virtual environment (optional but recommended): (Image 1.) 3. Install the necessary libraries (image 2.) Step 2: Create a Flask Web Application Create a new file called app.py in the project directory (image 3.) Step 3: Launch the Flask Application Save the changes and run the Flask application (image 4.) Step 4: Test the API Your API is now up and running and you can send images to /predict via HTTP POST requests. You can use tools such as curl or Postman to test the API. • An example of using curl (image 5.) • An example using Python queries (image 6.) https://t.me/DataScienceT

Building an Image Recognition API using Flask. Download Project source code https://t.me/DataScienceT