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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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Machine Learning with Python (@codeprogrammer) Ingliz til segmentidagi kanali faol ishtirokchi. Hozirda hamjamiyat 68 482 obunachidan iborat bo'lib, Taʼlim toifasida 2 406-o'rinni va Hindiston mintaqasida 4 586-o'rinni egallagan.

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04 Noyabr, 2026 dagi oxirgi ma’lumotlarga ko‘ra kanal barqaror faollikka ega. Oxirgi 30 kunda obunachilar soni -75 ga, so‘nggi 24 soatda esa 7 ga o‘zgardi va umumiy qamrov yuqori darajada qolmoqda.

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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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Postlar arxiv

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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 ✅ https://t.me/DataScienceM

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Cross promotion or free ads Contact me @hussein_sheikho

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🚀 Join 👉🏻 "Code with Socialcoffee" 👈🏻 Channel to dive into exciting world of AI! - 🌟 Learn about the latest AI breakthroughs and advancements in the field. - 🔬 Get Free Access to AI software tools to boost productivity and enhance skills. - 💡 Explore the endless possibilities of AI technology and its impact on industries. - 💻 Gain insights on AI features like speech recognition and image processing. - 🌐 Stay updated on expert chat sessions and get your burning questions answered. - 💥 Be part of a vibrant AI community, share ideas, and network with like-minded individuals. - 🤩 Access free AI solutions! Join now 👉🏻 Code with Socialcoffee 👈🏻

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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

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

68 482

Deep Learning NLP AI Python ML Data Mining Tensorflow Keras 👇👇👇👇👇 @Machine_learn

68 482

https://t.me/CodeProgrammer/2224

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https://t.me/DataScienceT

68 482

https://t.me/DataScienceT

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https://t.me/Ebooks2023

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Generative Adversarial Networks https://realpython.com/generative-adversarial-networks

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Repost from Machine Learning with Python

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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

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https://realpython.com/customize-django-admin-python/?fbclid=IwAR0VNZ4r447hMn4BEUxv-qLw-Ol5ZqFvxhgLbpGvv-4v6HvAeZHq1slE88g

68 482

📩 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

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Deep Learning NLP AI Python ML Data Mining Tensorflow Keras 👇👇👇👇👇 @Machine_learn

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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

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