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 68 475 suscriptores, ocupando la posición 2 406 en la categoría Educación y el puesto 4 586 en la región India.
📊 Métricas de audiencia y dinámica
Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 68 475 suscriptores.
Según los últimos datos del 03 noviembre, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de -74, y en las últimas 24 horas de 12, conservando un alto alcance.
- Estado de verificación: No verificado
- Tasa de interacción (ER): El promedio de interacción de la audiencia es 4.07%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 1.55% de reacciones respecto al total de suscriptores.
- Alcance de las publicaciones: Cada publicación recibe en promedio 2 789 visualizaciones. En el primer día suele acumular 1 062 visualizaciones.
- Reacciones e interacción: La audiencia responde de forma activa: el promedio de reacciones por publicación es 8.
- 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 04 noviembre, 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.
68 475
Suscriptores
+1224 horas
-827 días
-7430 días
Archivo de publicaciones
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📂 Tags: #transformers #python #nlp
http://t.me/codeprogrammer 🔒
The Transformer's decoder clearly explained 👇
📂 Tags: #transformers #python #nlp
http://t.me/codeprogrammer 🔒
The Transformer's encoder clearly explained 👇
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✅ Screenshot-To-Code
🔨 This is a simple application that converts a screenshot into HTML/Tailwind CSS code .
✔️ The app uses GPT-4 Vision to generate code and DALL-E 3 to create similar images .
✅ The app has a React/Vite frontend and a FastAPI backend , and requires an OpenAI API key with access to the GPT-4 Vision API .
🔗 links: https://github.com/abi/screenshot-to-code
📂 Tags: #html #openai #chatgpt
http://t.me/codeprogrammer 🔒
Data Science Cheat Sheets
Quick help to make a data scientist's life easier ✅
http://t.me/codeprogrammer 🔒
💡 #deeplearning #AI #ML #python
Data Science Cheat Sheets
Quick help to make a data scientist's life easier
About Dataset
A collection of cheat sheets for various data-science related languages and topics
http://t.me/codeprogrammer 🔒
💡 #deeplearning #AI #ML #python
Andrew Ng just released two new AI Python courses for beginners!
The course teaches how to write code using AI.
If you're thinking about learning to code, now is the perfect time to do so.
https://deeplearning.ai/short-courses/ai-python-for-beginners/
http://t.me/codeprogrammer 🔒
💡 #deeplearning #AI #ML #python
Coursera has launched a collaboration with the MAJOR platform to enable students to self-fund using the MAJOR platform.
Students can now access free Coursera scholarships through MAJOR.
Don't miss the opportunity: Click here.
القناة دى قمة فى الروعة في البرمجة وفيها حوالى 40 دورة انصحكوا تشتركوا فيها 👏💙💞
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ودا جروب تليجرام تقدر تحصل فيه كورسات برمجية فى اى مجال حرفيا
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لو انت متخصص فى تراك ال PHP Laravel دا جروب رائع
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ودا لينك لمجموعة قنوات جميلة فى مجال البرمجة
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القناة دى قمة فى الروعة في البرمجة وفيها حوالى 40 دورة انصحكوا تشتركوا فيها 👏💙💞
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لو انت متخصص فى تراك ال PHP Laravel دا جروب رائع
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اما لو متخصص فى ال .Net Core فدا جروب عليه مشاريع كبيرة جدا
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اما لو بتحب البايثون
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ودا فيديو ازاى تقدر تكسب فلوس وانت طالب
https://youtu.be/aqTdGNm9tcg
🚀 Popular SQL Challenges You Should Know!
🔥1. How to Find the Second Highest Value in a Column Need to find the second highest salary? Use a combination of ORDER BY and LIMIT to get it easily:
SELECT MAX(salary) AS second_highest_salary
FROM employees
WHERE salary < (SELECT MAX(salary) FROM employees);
2. How to Find the N-th Highest Salary in a Table Want to find the N-th highest salary? Just order the salaries in descending order and use LIMIT to pick the exact one you need. For example, to find the 3rd highest salary:
SELECT salary
FROM employees
ORDER BY salary DESC
LIMIT 2,1;
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Building Agents: Free Course
We just released a course with > 20 videos & notebooks focused on building agents. All code is open-source and the course is free!
Context
Back in June, I gave at talk at @aiDotEngineer on building agents with LangGraph. I got ~2 hrs of questions. We took these questions along with lots of feedback we've heard from users and built a course!
Module 1: Foundations
The first module includes several notebooks & videos that focus on what is an agent explained in simple terms, how to build various types of agents (routers, ReAct, etc), how to debug them w LangGraph Studio, and how to deploy them w LangGraph Cloud.
Module 2: Memory
One of the biggest questions we've heard is how to build long-running agents, which can remember important details. We show how memory works with LangGraph, and how to use various databases (SQLite, Postgres) to serve as agent memory.
Module 3: Human-In-The-Loop
Another central question with agents is allowing humans to approve actions (tools use) or modify the agent state (add feedback). We show various human in the loop interaction patterns that are supported in LangGraph, and also show how to stream the graph state during agent execution for human review.
Module 4: Controllability
The final module focuses on various design patterns for agent control flow, including parallelization of tasks and creating multi-agent teams with their own tasks / internal memory. This builds up into a customizable multi agent system for research that pulls together themes from the entire course.
Course (links to code, all videos):
https://academy.langchain.com/courses/intro-to-langgraph
http://t.me/codeprogrammer 🔒
💡 #deeplearning #AI #ML #python
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New course with @Intel! Multimodal RAG: Chat with Videos is available starting today!
Learn from @Vasudev_Lal to build an AI chat system that answers questions from video content!
Learn to:
📷 Create embeddings from videos
🧩 Build a RAG pipeline for data retrieval
💬 Use Large Vision-Language Models (LVLMs) for Q&A using both text and image inputs.
In this course, you will make API calls to access multimodal models hosted by @PredictionGuard on Intel’s cloud.
Enroll Free Here:
https://www.deeplearning.ai/short-courses/multimodal-rag-chat-with-videos/
http://t.me/codeprogrammer 🔒
💡 #deeplearning #AI #ML #python
