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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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📈 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 67 810 suscriptores, ocupando la posición 2 412 en la categoría Educación y el puesto 5 047 en la región India.

📊 Métricas de audiencia y dinámica

Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 67 810 suscriptores.

Según los últimos datos del 08 junio, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de 50, y en las últimas 24 horas de -5, conservando un alto alcance.

  • Estado de verificación: No verificado
  • Tasa de interacción (ER): El promedio de interacción de la audiencia es 2.79%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 2.60% de reacciones respecto al total de suscriptores.
  • Alcance de las publicaciones: Cada publicación recibe en promedio 1 895 visualizaciones. En el primer día suele acumular 1 764 visualizaciones.
  • Reacciones e interacción: La audiencia responde de forma activa: el promedio de reacciones por publicación es 7.
  • 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 09 junio, 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.

67 810
Suscriptores
-524 horas
+227 días
+5030 días
Archivo de publicaciones
“Learn AI” is everywhere. But where do the builders actually start? Here’s the real path, the courses, papers and repos that
“Learn AI” is everywhere. But where do the builders actually start? Here’s the real path, the courses, papers and repos that matter. Videos: Everything here ⇒ https://lnkd.in/ePfB8_rk ➡️ LLM Introduction → https://lnkd.in/ernZFpvB ➡️ LLMs from Scratch - Stanford CS229 → https://lnkd.in/etUh6_mn ➡️ Agentic AI Overview →https://lnkd.in/ecpmzAyq ➡️ Building and Evaluating Agents → https://lnkd.in/e5KFeZGW ➡️ Building Effective Agents → https://lnkd.in/eqxvBg79 ➡️ Building Agents with MCP → https://lnkd.in/eZd2ym2K ➡️ Building an Agent from Scratch → https://lnkd.in/eiZahJGn Courses: All Courses here ⇒ https://lnkd.in/eKKs9ves ➡️ HuggingFace's Agent Course → https://lnkd.in/e7dUTYuE ➡️ MCP with Anthropic → https://lnkd.in/eMEnkCPP ➡️ Building Vector DB with Pinecone → https://lnkd.in/eP2tMGVs ➡️ Vector DB from Embeddings to Apps → https://lnkd.in/eP2tMGVs ➡️ Agent Memory → https://lnkd.in/egC8h9_Z ➡️ Building and Evaluating RAG apps → https://lnkd.in/ewy3sApa ➡️ Building Browser Agents → https://lnkd.in/ewy3sApa ➡️ LLMOps → https://lnkd.in/ex4xnE8t ➡️ Evaluating AI Agents → https://lnkd.in/eBkTNTGW ➡️ Computer Use with Anthropic → https://lnkd.in/ebHUc-ZU ➡️ Multi-Agent Use → https://lnkd.in/e4f4HtkR ➡️ Improving LLM Accuracy → https://lnkd.in/eVUXGT4M ➡️ Agent Design Patterns → https://lnkd.in/euhUq3W9 ➡️ Multi Agent Systems → https://lnkd.in/evBnavk9 Guides: Access all ⇒ https://lnkd.in/e-GA-HRh ➡️ Google's Agent → https://lnkd.in/encAzwKf ➡️ Google's Agent Companion → https://lnkd.in/e3-XtYKg ➡️ Building Effective Agents by Anthropic → https://lnkd.in/egifJ_wJ ➡️ Claude Code Best practices → https://lnkd.in/eJnqfQju ➡️ OpenAI's Practical Guide to Building Agents → https://lnkd.in/e-GA-HRh Repos: ➡️ GenAI Agents → https://lnkd.in/eAscvs_i ➡️ Microsoft's AI Agents for Beginners → https://lnkd.in/d59MVgic ➡️ Prompt Engineering Guide → https://lnkd.in/ewsbFwrP ➡️ AI Agent Papers → https://lnkd.in/esMHrxJX Papers: 🟡 ReAct → https://lnkd.in/eZ-Z-WFb 🟡 Generative Agents → https://lnkd.in/eDAeSEAq 🟡 Toolformer → https://lnkd.in/e_Vcz5K9 🟡 Chain-of-Thought Prompting → https://lnkd.in/eRCT_Xwq 🟡 Tree of Thoughts → https://lnkd.in/eiadYm8S 🟡 Reflexion → https://lnkd.in/eggND2rZ 🟡 Retrieval-Augmented Generation Survey → https://lnkd.in/eARbqdYE Access all ⇒ https://lnkd.in/e-GA-HRh By: https://t.me/CodeProgrammer 🟡

Start small and build steady income: learn the basics inside the app, earn your first tokens, and unlock higher rewards as yo
Start small and build steady income: learn the basics inside the app, earn your first tokens, and unlock higher rewards as you progress. Bring friends later to multiply results without extra risk. Start now! #ad InsideAds

Think crypto mining is just for whales? Discover how anyone can earn tokens and unlock upgrades and artifacts with Padma Web3
Think crypto mining is just for whales? Discover how anyone can earn tokens and unlock upgrades and artifacts with Padma Web3’s play-to-earn ecosystem. Boost your mana, invite friends, and turn your time into real rewards — no special equipment needed. Curious about the next big thing? See what everyone is mining right now. Start now! #ad InsideAds

Python Cheat Sheet (very very important) 📖 Compact Python cheat sheet covering setup, syntax, data types, variables, strings, control flow, functions, classes, errors, and I/O. Link: https://discord.com/channels/942740928706281524/1423994784720359567/1424711790947864669

Big surprise in our channels on Discord https://discord.gg/PGZku7DrSz

Repost from Machine Learning
📌 Missing Value Imputation, Explained: A Visual Guide with Code Examples for Beginners 🗂 Category: MACHINE LEARNING 🕒 Date
📌 Missing Value Imputation, Explained: A Visual Guide with Code Examples for Beginners 🗂 Category: MACHINE LEARNING 🕒 Date: 2024-08-27 | ⏱️ Read time: 13 min read One (tiny) dataset, six imputation methods?

Repost from Data Analytics
🖥 Extremely useful collection of 800+ SQL questions frequently asked in interviews. It also includes tasks for self-study and many examples. The collection is perfect for those who want to improve their SQL skills, refresh their knowledge, and test themselves. ▪️ GitHub https://t.me/addlist/8_rRW2scgfRhOTc0 ⚡️

Great find for developers: free cheat sheets on Deep Learning and PyTorch A detailed guide to creating and training neural ne
Great find for developers: free cheat sheets on Deep Learning and PyTorch A detailed guide to creating and training neural networks - link Basic principles and practice of working with PyTorch - link 👉 @CODEPROGRAMMER

Awesome interactive textbook on probability theory and statistics Inside are clear visualizations, interactive elements, and minimal dry theory. You can tweak distributions, sample datasets, play with confidence intervals, and clearly see how it all works Get it here, I recommend opening it on a desktop https://seeing-theory.brown.edu/ 👉 @DataScienceM

Repost from Machine Learning
📌 Extracting Structured Vehicle Data from Images 🗂 Category: 🕒 Date: 2025-01-27 | ⏱️ Read time: 10 min read Build an Autom
📌 Extracting Structured Vehicle Data from Images 🗂 Category: 🕒 Date: 2025-01-27 | ⏱️ Read time: 10 min read Build an Automated Vehicle Documentation System that Extracts Structured Information from Images, using OpenAI API,…

Awesome interactive textbook on probability theory and statistics Inside are clear visualizations, interactive elements, and minimal dry theory. You can tweak distributions, sample datasets, play with confidence intervals, and clearly see how it all works Get it here, I recommend opening it on a desktop https://seeing-theory.brown.edu/ 👉 @DataScienceM

Repost from Machine Learning
Awesome interactive textbook on probability theory and statistics Inside are clear visualizations, interactive elements, and minimal dry theory. You can tweak distributions, sample datasets, play with confidence intervals, and clearly see how it all works Get it here, I recommend opening it on a desktop https://seeing-theory.brown.edu/ 👉 @DataScienceM I spent years chasing success until I found the 7 daily habits no one talks about—now everything’s changed for me. Most people miss the real secret. See what you’ve been overlooking: Success Tips 🔥 | InsideAds

Repost from Machine Learning
📌 How to Build a Genetic Algorithm from Scratch in Python 🗂 Category: DATA SCIENCE 🕒 Date: 2024-08-30 | ⏱️ Read time: 16 m
📌 How to Build a Genetic Algorithm from Scratch in Python 🗂 Category: DATA SCIENCE 🕒 Date: 2024-08-30 | ⏱️ Read time: 16 min read A complete walkthrough on how one can build a Genetic Algorithm from scratch in Python,…

Python library RetinaFace for face detection and working with key points (eyes, nose, mouth) Supports face alignment, easily
Python library RetinaFace for face detection and working with key points (eyes, nose, mouth) Supports face alignment, easily installed via pip install retina-face, and works based on deep models from the insightface project. An excellent tool for tasks in computer vision and face recognition. Usage examples:
from retinaface import RetinaFace

resp = RetinaFace.detect_faces("img1.jpg")
print(resp)

{
    "face_1": {
        "score": 0.9993440508842468,
        "facial_area": [155, 81, 434, 443],
        "landmarks": {
          "right_eye": [257.82974, 209.64787],
          "left_eye": [374.93427, 251.78687],
          "nose": [303.4773, 299.91144],
          "mouth_right": [228.37329, 338.73193],
          "mouth_left": [320.21982, 374.58798]
        }
  }
}
👉 @DataScienceN

Creating QR codes with Python in just a few lines of code Anyone can generate their own QR code for a link, text, or even Wi-
Creating QR codes with Python in just a few lines of code Anyone can generate their own QR code for a link, text, or even Wi-Fi data. For this, the qrcode library and the PIL module are used
pip install qrcode pillow
import qrcode
from PIL import Image

data = input("Enter data for QR: ")
qr = qrcode.QRCode(version=3, box_size=8, border=4)
qr.add_data(data)
qr.make(fit=True)

image = qr.make_image(fill="black", back_color="aqua")
image.save("qr_code.png")
Image.open("qr_code.png")
The output is a ready QR code with any text or link. You can change colors, sizes, and style to fit your design 🙂 👉 https://t.me/CodeProgrammer

Repost from Machine Learning
📌 A Guide to Clustering Algorithms 🗂 Category: DATA SCIENCE 🕒 Date: 2024-09-06 | ⏱️ Read time: 6 min read An overview of c
📌 A Guide to Clustering Algorithms 🗂 Category: DATA SCIENCE 🕒 Date: 2024-09-06 | ⏱️ Read time: 6 min read An overview of clustering and the different families of clustering algorithms.

What if you could double your trading power—today? Start with just $200 at Elite Gold Trading, and get a $200 bonus from our
What if you could double your trading power—today? Start with just $200 at Elite Gold Trading, and get a $200 bonus from our partner broker, plus +20% on every future deposit. Don’t wait—join now and copy proven AI strategies in real time. Trade smarter, grow faster, and see real results. Get started here #ad InsideAds

Repost from Machine Learning
📌 Image Segmentation With K-Means Clustering 🗂 Category: MACHINE LEARNING 🕒 Date: 2024-09-05 | ⏱️ Read time: 11 min read A
📌 Image Segmentation With K-Means Clustering 🗂 Category: MACHINE LEARNING 🕒 Date: 2024-09-05 | ⏱️ Read time: 11 min read An introduction with Python

Google Collab notebooks to learn everything you need to master prompt engineering with Claude - from basic structure and role
Google Collab notebooks to learn everything you need to master prompt engineering with Claude - from basic structure and role prompting to advanced techniques like few-shot learning, avoiding hallucinations, and tool use. Perfect interactive lessons to level up your AI skills Link: https://github.com/anthropics/courses/tree/master/prompt_engineering_interactive_tutorial/Anthropic%201P