¡Ya disponible! Investigación de Telegram 2025 — los principales insights del año 
Machine Learning
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Real Machine Learning — simple, practical, and built on experience. Learn step by step with clear explanations and working code. Admin: @HusseinSheikho || @Hussein_Sheikho
Mostrar más📈 Análisis del canal de Telegram Machine Learning
El canal Machine Learning (@machinelearning9) en el segmento lingüístico de Inglés es un actor destacado. Actualmente la comunidad reúne a 40 106 suscriptores, ocupando la posición 3 384 en la categoría Tecnologías y Aplicaciones y el puesto 231 en la región Siria.
📊 Métricas de audiencia y dinámica
Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 40 106 suscriptores.
Según los últimos datos del 24 junio, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de 401, y en las últimas 24 horas de 38, conservando un alto alcance.
- Estado de verificación: No verificado
- Tasa de interacción (ER): El promedio de interacción de la audiencia es 1.96%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 1.16% de reacciones respecto al total de suscriptores.
- Alcance de las publicaciones: Cada publicación recibe en promedio 788 visualizaciones. En el primer día suele acumular 465 visualizaciones.
- Reacciones e interacción: La audiencia responde de forma activa: el promedio de reacciones por publicación es 2.
- Intereses temáticos: El contenido se centra en temas clave como distance, insidead, gpu, learning, degree.
📝 Descripción y política de contenido
El autor describe el recurso como un espacio para expresar opiniones subjetivas:
“Real Machine Learning — simple, practical, and built on experience.
Learn step by step with clear explanations and working code.
Admin: @HusseinSheikho || @Hussein_Sheikho”
Gracias a la alta frecuencia de actualizaciones (últimos datos recibidos el 25 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 Tecnologías y Aplicaciones.
40 106
Suscriptores
+3824 horas
+637 días
+40130 días
Archivo de publicaciones
40 114
📌 Prompt Engineering vs RAG for Editing Resumes
🗂 Category: LLM APPLICATIONS
🕒 Date: 2026-01-04 | ⏱️ Read time: 12 min read
Running a code-free comparison in Azure
#DataScience #AI #Python
40 114
📌 How to Keep MCPs Useful in Agentic Pipelines
🗂 Category: AGENTIC AI
🕒 Date: 2026-01-03 | ⏱️ Read time: 10 min read
Check the tools your LLM uses before replacing it with just a more powerful model
#DataScience #AI #Python
40 114
📌 Optimizing Data Transfer in AI/ML Workloads
🗂 Category: DEEP LEARNING
🕒 Date: 2026-01-03 | ⏱️ Read time: 16 min read
A deep dive on data transfer bottlenecks, their identification, and their resolution with the help…
#DataScience #AI #Python
40 114
200$ to 20k$ SOL Challenge!
As promised, i will do another challenge for those who missed the previous one!
Last one we completed in 6 days, let’s do this one even quicker!
Join my free group Before closing 👇
https://t.me/+DAKLP7eUy9Y3ZjY0
#ad InsideAds
40 114
Repost from Machine Learning with Python
All assignments for the #Stanford The Modern Software Developer course are now available online.
This is the first full-fledged university course that covers how code-generative #LLMs are changing every stage of the development lifecycle. The assignments are designed to take you from a beginner to a confident expert in using AI to boost productivity in development.
Enjoy your studies! ✌️
https://github.com/mihail911/modern-software-dev-assignments
https://t.me/CodeProgrammer
40 114
📌 The Real Challenge in Data Storytelling: Getting Buy-In for Simplicity
🗂 Category: DATA SCIENCE
🕒 Date: 2026-01-02 | ⏱️ Read time: 7 min read
What happens when your clear dashboard meets stakeholders who want everything on one screen
#DataScience #AI #Python
40 114
📌 Off-Beat Careers That Are the Future Of Data
🗂 Category: DATA SCIENCE
🕒 Date: 2026-01-02 | ⏱️ Read time: 8 min read
The unconventional career paths you need to explore
#DataScience #AI #Python
40 114
📌 Drift Detection in Robust Machine Learning Systems
🗂 Category: MACHINE LEARNING
🕒 Date: 2026-01-02 | ⏱️ Read time: 18 min read
A prerequisite for long-term success of machine learning systems
#DataScience #AI #Python
40 114
200$ to 20k$ SOL Challenge!
As promised, i will do another challenge for those who missed the previous one!
Last one we completed in 6 days, let’s do this one even quicker!
Join my free group Before closing 👇
https://t.me/+DAKLP7eUy9Y3ZjY0
#ad InsideAds
40 114
📌 Deep Reinforcement Learning: The Actor-Critic Method
🗂 Category: REINFORCEMENT LEARNING
🕒 Date: 2026-01-01 | ⏱️ Read time: 19 min read
Robot friends collaborate to learn to fly a drone
#DataScience #AI #Python
40 114
Repost from Machine Learning with Python
Harvard has made its textbook on ML systems publicly available. It's extremely practical: not just about how to train models, but how to build production systems around them - what really matters.
The topics there are really top-notch:
> Building autograd, optimizers, attention, and mini-PyTorch from scratch to understand how the framework is structured internally. (This is really awesome)
> Basic things about DL: batches, computational accuracy, model architectures, and training
> Optimizing ML performance, hardware acceleration, benchmarking, and efficiency
So this isn't just an introductory course on ML, but a complete cycle from start to practical application. You can already read the book and view the code for free. For 2025, this is one of the strongest textbooks to have been released, so it's best not to miss out.
The repository is here, with a link to the book inside 👏
👉 @codeprogrammer
40 114
📌 EDA in Public (Part 3): RFM Analysis for Customer Segmentation in Pandas
🗂 Category: DATA SCIENCE
🕒 Date: 2026-01-01 | ⏱️ Read time: 13 min read
How to build, score, and interpret RFM segments step by step
#DataScience #AI #Python
40 114
📌 The Machine Learning “Advent Calendar” Bonus 2: Gradient Descent Variants in Excel
🗂 Category: MACHINE LEARNING
🕒 Date: 2025-12-31 | ⏱️ Read time: 8 min read
Gradient Descent, Momentum, RMSProp, and Adam all aim for the same minimum. They do not…
#DataScience #AI #Python
40 114
📌 Chunk Size as an Experimental Variable in RAG Systems
🗂 Category: LARGE LANGUAGE MODELS
🕒 Date: 2025-12-31 | ⏱️ Read time: 12 min read
Understanding retrieval in RAG systems by experimenting with different chunk sizes
#DataScience #AI #Python
40 114
📌 What Advent of Code Has Taught Me About Data Science
🗂 Category: PROGRAMMING
🕒 Date: 2025-12-31 | ⏱️ Read time: 10 min read
Five key learnings that I discovered during a programming challenge and how they apply to…
#DataScience #AI #Python
40 114
📌 Production-Ready LLMs Made Simple with the NeMo Agent Toolkit
🗂 Category: AGENTIC AI
🕒 Date: 2025-12-31 | ⏱️ Read time: 23 min read
From simple chat to multi-agent reasoning and real-time REST APIs
#DataScience #AI #Python
40 114
“I spent hours lost in endless Telegram groups—until I discovered this hidden search engine.” Argo🔍Search lets you find the best groups, channels, music, and news in seconds. No more wasting time scrolling!
Discover what others haven’t yet: Try it now and unlock Telegram like never before.
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