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

Network:Machine Learning with Python India4 731 Education2 365...

📈 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 68 136 subscribers, ranking 2 365 in the Education category and 4 731 in the India region.

📊 Audience metrics and dynamics

Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 68 136 subscribers.

According to the latest data from 31 August, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 80 over the last 30 days and by 1 over the last 24 hours, overall reach remains high.

Verification status: Not verified
Engagement rate (ER): The average audience engagement rate is 4.09%. Within the first 24 hours after publication, content typically collects 1.54% reactions from the total number of subscribers.
Post reach: On average, each post receives 2 784 views. Within the first day, a publication typically gains 1 052 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 01 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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68 136

Subscribers

+124 hours

-97 days

+8030 days

2 784

Post views

~ 1 05224 hours

~ 1 13048 hours

4.09%

Engagement rate

~ 2

Posts per day

Ads index

beta

Posts Archive

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https://t.me/PythonArab

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Repost from AI & ML Papers

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Topic: Python Script to Convert a Shared ChatGPT Link to PDF – Step-by-Step Guide --- ### Objective In this lesson, we’ll build a Python script that: • Takes a ChatGPT share link (e.g., https://chat.openai.com/share/abc123) • Downloads the HTML content of the chat • Converts it to a PDF file using pdfkit and wkhtmltopdf This is useful for archiving, sharing, or printing ChatGPT conversations in a clean format. --- ### 1. Prerequisites Before starting, you need the following libraries and tools: #### • Install pdfkit and requests

pip install pdfkit requests

#### • Install wkhtmltopdf Download from: [https://wkhtmltopdf.org/downloads.html](https://wkhtmltopdf.org/downloads.html) Make sure to add the path of the installed binary to your system PATH. --- ### 2. Python Script: Convert Shared ChatGPT URL to PDF

import pdfkit
import requests
import os

# Define output filename
output_file = "chatgpt_conversation.pdf"

# ChatGPT shared URL (user input)
chat_url = input("Enter the ChatGPT share URL: ").strip()

# Verify the URL format
if not chat_url.startswith("https://chat.openai.com/share/"):
    print("Invalid URL. Must start with https://chat.openai.com/share/")
    exit()

try:
    # Download HTML content
    response = requests.get(chat_url)
    if response.status_code != 200:
        raise Exception(f"Failed to load the chat: {response.status_code}")

    html_content = response.text

    # Save HTML to temporary file
    with open("temp_chat.html", "w", encoding="utf-8") as f:
        f.write(html_content)

    # Convert HTML to PDF
    pdfkit.from_file("temp_chat.html", output_file)

    print(f"\n✅ PDF saved as: {output_file}")

    # Optional: remove temp file
    os.remove("temp_chat.html")

except Exception as e:
    print(f"❌ Error: {e}")

--- ### 3. Notes • This approach works only if the shared page is publicly accessible (which ChatGPT share links are). • The PDF output will contain the web page version, including theme and layout. • You can customize the PDF output using pdfkit options (like page size, margins, etc.). --- ### 4. Optional Enhancements • Add GUI with Tkinter • Accept multiple URLs • Add PDF metadata (title, author, etc.) • Add support for offline rendering using BeautifulSoup to clean content --- ### Exercise • Try converting multiple ChatGPT share links to PDF • Customize the styling with your own CSS • Add a timestamp or watermark to the PDF --- #Python #ChatGPT #PDF #WebScraping #Automation #pdfkit #tkinter

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Repost from Python Courses & Resources

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Topic: Handling Datasets of All Types – Part 1 of 5: Introduction and Basic Concepts --- 1. What is a Dataset? • A dataset is a structured collection of data, usually organized in rows and columns, used for analysis or training machine learning models. --- 2. Types of Datasets • Structured Data: Tables, spreadsheets with rows and columns (e.g., CSV, Excel). • Unstructured Data: Images, text, audio, video. • Semi-structured Data: JSON, XML files containing hierarchical data. --- 3. Common Dataset Formats • CSV (Comma-Separated Values) • Excel (.xls, .xlsx) • JSON (JavaScript Object Notation) • XML (eXtensible Markup Language) • Images (JPEG, PNG, TIFF) • Audio (WAV, MP3) --- 4. Loading Datasets in Python • Use libraries like pandas for structured data:

import pandas as pd
df = pd.read_csv('data.csv')

• Use libraries like json for JSON files:

import json
with open('data.json') as f:
    data = json.load(f)

--- 5. Basic Dataset Exploration • Check shape and size:

print(df.shape)

• Preview data:

print(df.head())

• Check for missing values:

print(df.isnull().sum())

--- 6. Summary • Understanding dataset types is crucial before processing. • Loading and exploring datasets helps identify cleaning and preprocessing needs. --- Exercise • Load a CSV and JSON dataset in Python, print their shapes, and identify missing values. --- #DataScience #Datasets #DataLoading #Python #DataExploration https://t.me/DataScienceM

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Repost from Python Courses & Resources

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Repost from Machine Learning

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Repost from AI & ML Papers

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Repost from Python Courses & Resources

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Repost from Python Courses & Resources

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Repost from Python Courses & Resources

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t.me/PythonArab arab group about python and ML

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Repost from Machine Learning with Python

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

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NUMPY FOR DS.pdf4.50 MB

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What is torch.nn really?

When I started working with PyTorch, my biggest question was: "What is torch.nn?".

This article explains it quite well. 📌 Read

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