¡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 40 114 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 40 114 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.
40 114
Suscriptores
+3824 horas
+637 días
+40130 días
Archivo de publicaciones
40 118
📌 How to Leverage Slash Commands to Code Effectively
🗂 Category: LLM APPLICATIONS
🕒 Date: 2026-01-11 | ⏱️ Read time: 8 min read
Learn how I utilize slash commands to be a more efficient engineer
#DataScience #AI #Python
40 118
📌 Automatic Prompt Optimization for Multimodal Vision Agents: A Self-Driving Car Example
🗂 Category: AGENTIC AI
🕒 Date: 2026-01-11 | ⏱️ Read time: 23 min read
Walkthrough using open-source prompt optimization algorithms in Python to improve the accuracy of an autonomous…
#DataScience #AI #Python
40 118
Repost from Machine Learning with Python
🧠 𝐊-𝐍𝐞𝐚𝐫𝐞𝐬𝐭 𝐍𝐞𝐢𝐠𝐡𝐛𝐨𝐫𝐬 (𝐊𝐍𝐍)
🔹 𝐖𝐡𝐚𝐭 𝐈 𝐜𝐨𝐯𝐞𝐫𝐞𝐝 𝐭𝐨𝐝𝐚𝐲
𝐖𝐡𝐚𝐭 𝐊𝐍𝐍 𝐢𝐬 𝐚𝐧𝐝 𝐡𝐨𝐰 𝐢𝐭 𝐰𝐨𝐫𝐤𝐬
𝐃𝐢𝐟𝐟𝐞𝐫𝐞𝐧𝐜𝐞 𝐛𝐞𝐭𝐰𝐞𝐞𝐧 𝐊𝐍𝐍 𝐟𝐨𝐫 𝐂𝐥𝐚𝐬𝐬𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧 𝐯𝐬 𝐑𝐞𝐠𝐫𝐞𝐬𝐬𝐢𝐨𝐧
𝐑𝐨𝐥𝐞 𝐨𝐟 𝐊 (𝐡𝐲𝐩𝐞𝐫𝐩𝐚𝐫𝐚𝐦𝐞𝐭𝐞𝐫)
𝐃𝐢𝐬𝐭𝐚𝐧𝐜𝐞 𝐦𝐞𝐭𝐫𝐢𝐜𝐬: 𝐄𝐮𝐜𝐥𝐢𝐝𝐞𝐚𝐧 𝐯𝐬 𝐌𝐚𝐧𝐡𝐚𝐭𝐭𝐚𝐧
𝐖𝐡𝐲 𝐊𝐍𝐍 𝐢𝐬 𝐜𝐚𝐥𝐥𝐞𝐝 𝐚 𝐥𝐚𝐳𝐲 / 𝐢𝐧𝐬𝐭𝐚𝐧𝐜𝐞-𝐛𝐚𝐬𝐞𝐝 𝐥𝐞𝐚𝐫𝐧𝐞𝐫
🎯 𝐓𝐨𝐩 𝟏𝟎 𝐈𝐧𝐭𝐞𝐫𝐯𝐢𝐞𝐰 𝐐𝐮𝐞𝐬𝐭𝐢𝐨𝐧𝐬 (𝐌𝐮𝐬𝐭-𝐊𝐧𝐨𝐰)
1️⃣ 𝘞𝘩𝘢𝘵 𝘪𝘴 𝘒-𝘕𝘦𝘢𝘳𝘦𝘴𝘵 𝘕𝘦𝘪𝘨𝘩𝘣𝘰𝘳𝘴 (𝘒𝘕𝘕)?
2️⃣ 𝘞𝘩𝘺 𝘪𝘴 𝘒𝘕𝘕 𝘤𝘢𝘭𝘭𝘦𝘥 𝘢 𝘭𝘢𝘻𝘺 𝘭𝘦𝘢𝘳𝘯𝘪𝘯𝘨 𝘢𝘭𝘨𝘰𝘳𝘪𝘵𝘩𝘮?
3️⃣ 𝘋𝘪𝘧𝘧𝘦𝘳𝘦𝘯𝘤𝘦 𝘣𝘦𝘵𝘸𝘦𝘦𝘯 𝘒𝘕𝘕 𝘤𝘭𝘢𝘴𝘴𝘪𝘧𝘪𝘤𝘢𝘵𝘪𝘰𝘯 𝘢𝘯𝘥 𝘒𝘕𝘕 𝘳𝘦𝘨𝘳𝘦𝘴𝘴𝘪𝘰𝘯?
4️⃣ 𝘏𝘰𝘸 𝘥𝘰 𝘺𝘰𝘶 𝘤𝘩𝘰𝘰𝘴𝘦 𝘵𝘩𝘦 𝘷𝘢𝘭𝘶𝘦 𝘰𝘧 𝘒?
5️⃣ 𝘞𝘩𝘢𝘵 𝘩𝘢𝘱𝘱𝘦𝘯𝘴 𝘸𝘩𝘦𝘯 𝘒 𝘪𝘴 𝘵𝘰𝘰 𝘴𝘮𝘢𝘭𝘭 𝘰𝘳 𝘵𝘰𝘰 𝘭𝘢𝘳𝘨𝘦?
6️⃣ 𝘞𝘩𝘢𝘵 𝘥𝘪𝘴𝘵𝘢𝘯𝘤𝘦 𝘮𝘦𝘵𝘳𝘪𝘤𝘴 𝘢𝘳𝘦 𝘤𝘰𝘮𝘮𝘰𝘯𝘭𝘺 𝘶𝘴𝘦𝘥 𝘪𝘯 𝘒𝘕𝘕?
7️⃣ 𝘞𝘩𝘺 𝘥𝘰𝘦𝘴 𝘒𝘕𝘕 𝘱𝘦𝘳𝘧𝘰𝘳𝘮 𝘱𝘰𝘰𝘳𝘭𝘺 𝘰𝘯 𝘩𝘪𝘨𝘩-𝘥𝘪𝘮𝘦𝘯𝘴𝘪𝘰𝘯𝘢𝘭 𝘥𝘢𝘵𝘢?
8️⃣ 𝘞𝘩𝘢𝘵 𝘪𝘴 𝘵𝘩𝘦 𝘵𝘪𝘮𝘦 𝘤𝘰𝘮𝘱𝘭𝘦𝘹𝘪𝘵𝘺 𝘰𝘧 𝘒𝘕𝘕?
9️⃣ 𝘏𝘰𝘸 𝘥𝘰 𝘒𝘋-𝘛𝘳𝘦𝘦 𝘢𝘯𝘥 𝘉𝘢𝘭𝘭-𝘛𝘳𝘦𝘦 𝘪𝘮𝘱𝘳𝘰𝘷𝘦 𝘒𝘕𝘕 𝘱𝘦𝘳𝘧𝘰𝘳𝘮𝘢𝘯𝘤𝘦?
🔟 𝘞𝘩𝘦𝘯 𝘴𝘩𝘰𝘶𝘭𝘥 𝘺𝘰𝘶 𝘢𝘷𝘰𝘪𝘥 𝘶𝘴𝘪𝘯𝘨 #𝘒𝘕𝘕?
https://t.me/CodeProgrammer ⭐️
40 118
📌 Federated Learning, Part 1: The Basics of Training Models Where the Data Lives
🗂 Category: FEDERATED LEARNING
🕒 Date: 2026-01-10 | ⏱️ Read time: 10 min read
Understanding the foundations of federated learning
#DataScience #AI #Python
40 118
📌 How LLMs Handle Infinite Context With Finite Memory
🗂 Category: LARGE LANGUAGE MODELS
🕒 Date: 2026-01-09 | ⏱️ Read time: 10 min read
Achieving infinite context with 114× less memory
#DataScience #AI #Python
40 118
📌 Beyond the Flat Table: Building an Enterprise-Grade Financial Model in Power BI
🗂 Category: DATA SCIENCE
🕒 Date: 2026-01-10 | ⏱️ Read time: 11 min read
A step-by-step journey through data transformation, star schema modeling, and DAX variance analysis with lessons…
#DataScience #AI #Python
40 118
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40 118
📌 TDS Newsletter: December Must-Reads on GraphRAG, Data Contracts, and More
🗂 Category: THE VARIABLE
🕒 Date: 2026-01-08 | ⏱️ Read time: 3 min read
Don’t miss our most popular articles of the previous month
#DataScience #AI #Python
40 118
📌 Teaching a Neural Network the Mandelbrot Set
🗂 Category: MACHINE LEARNING
🕒 Date: 2026-01-09 | ⏱️ Read time: 10 min read
And why Fourier features change everything
#DataScience #AI #Python
40 118
📌 Mastering Non-Linear Data: A Guide to Scikit-Learn’s SplineTransformer
🗂 Category: MACHINE LEARNING
🕒 Date: 2026-01-09 | ⏱️ Read time: 7 min read
Forget stiff lines and wild polynomials. Discover why Splines are the “Goldilocks” of feature engineering,…
#DataScience #AI #Python
40 118
100$ to 10k$ SOL Challenge!
As promised, i will do another challenge for those who missed the previous one!
Last one we completed in 4 days, let’s do this one even quicker!
Join my PRIVATE group 👇
Want to know the secrets nobody talks about? Click here and see what you’ve been missing.
Your next trade could change everything.
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40 118
📌 Data Science Spotlight: Selected Problems from Advent of Code 2025
🗂 Category: DATA SCIENCE
🕒 Date: 2026-01-09 | ⏱️ Read time: 19 min read
Hands-on walkthroughs of problems and solution approaches that power real‑world data science use cases
#DataScience #AI #Python
40 118
📌 Faster Is Not Always Better: Choosing the Right PostgreSQL Insert Strategy in Python (+Benchmarks)
🗂 Category: DATA ENGINEERING
🕒 Date: 2026-01-08 | ⏱️ Read time: 6 min read
PostgreSQL is fast. Whether your Python code can or should keep up depends on context.…
#DataScience #AI #Python
40 118
📌 How to Improve the Performance of Visual Anomaly Detection Models
🗂 Category: COMPUTER VISION
🕒 Date: 2026-01-08 | ⏱️ Read time: 6 min read
Apply the best methods from academia to get the most out of practical applications
#DataScience #AI #Python
40 118
A great app for building and programming desktop, Android, and Telegram bots using only prompts
Just send what you want and it will design everything for you and the possibility to make money from your app 👍
40 118
📌 Retrieval for Time-Series: How Looking Back Improves Forecasts
🗂 Category: DATA SCIENCE
🕒 Date: 2026-01-08 | ⏱️ Read time: 13 min read
Why Retrieval Helps in Time Series Forecasting We all know how it goes: Time-series data…
#DataScience #AI #Python
40 118
📌 Beyond Prompting: The Power of Context Engineering
🗂 Category: ARTIFICIAL INTELLIGENCE
🕒 Date: 2026-01-08 | ⏱️ Read time: 60 min read
Using ACE to create self-improving LLM workflows and structured playbooks
#DataScience #AI #Python
40 118
The single most undervalued fact of linear algebra: matrices are graphs, and graphs are matrices.
Encoding matrices as graphs is a cheat code, making complex behavior simple to study.
https://t.me/DataScienceM
40 118
📌 Why Supply Chain is the Best Domain for Data Scientists in 2026 (And How to Learn It)
🗂 Category: DATA SCIENCE
🕒 Date: 2026-01-07 | ⏱️ Read time: 13 min read
My take after 10 years in Supply Chain on why this can be an excellent…
#DataScience #AI #Python
