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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📈 Análisis del canal de Telegram Machine Learning with Python

El canal Machine Learning with Python (@codeprogrammer) en el segmento lingüístico de Inglés es un actor destacado. Actualmente la comunidad reúne a 68 136 suscriptores, ocupando la posición 2 365 en la categoría Educación y el puesto 4 731 en la región India.

📊 Métricas de audiencia y dinámica

Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 68 136 suscriptores.

Según los últimos datos del 31 agosto, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de 80, y en las últimas 24 horas de 1, conservando un alto alcance.

Estado de verificación: No verificado
Tasa de interacción (ER): El promedio de interacción de la audiencia es 4.09%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 1.54% de reacciones respecto al total de suscriptores.
Alcance de las publicaciones: Cada publicación recibe en promedio 2 784 visualizaciones. En el primer día suele acumular 1 052 visualizaciones.
Reacciones e interacción: La audiencia responde de forma activa: el promedio de reacciones por publicación es 5.
Intereses temáticos: El contenido se centra en temas clave como insidead, learning, degree, evaluation, algorithm.

📝 Descripción y política de contenido

El autor describe el recurso como un espacio para expresar opiniones subjetivas:
“Learn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers. Admin: @HusseinSheikho || @Hussein_Sheikho”

Gracias a la alta frecuencia de actualizaciones (últimos datos recibidos el 01 septiembre, 2026), el canal mantiene la vigencia y un amplio alcance. La analítica demuestra que la audiencia interactúa activamente con el contenido, lo que lo convierte en un punto de referencia dentro de la categoría Educación.

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68 136

Suscriptores

+124 horas

-97 días

+8030 días

2 784

Visitas de la publicación

~ 1 05224 horas

~ 1 13048 horas

4.09%

Tasa de compromiso

~ 2

Mensajes por día

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Archivo de publicaciones

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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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Repost from Machine Learning with Python

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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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