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

Red:Machine Learning with Python Siria225 Tecnologías y Aplicaciones3 331...

📈 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 40 334 suscriptores, ocupando la posición 3 331 en la categoría Tecnologías y Aplicaciones y el puesto 225 en la región Siria.

📊 Métricas de audiencia y dinámica

Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 40 334 suscriptores.

Según los últimos datos del 10 julio, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de 383, y en las últimas 24 horas de 25, conservando un alto alcance.

Estado de verificación: No verificado
Tasa de interacción (ER): El promedio de interacción de la audiencia es 2.35%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 1.95% de reacciones respecto al total de suscriptores.
Alcance de las publicaciones: Cada publicación recibe en promedio 948 visualizaciones. En el primer día suele acumular 786 visualizaciones.
Reacciones e interacción: La audiencia responde de forma activa: el promedio de reacciones por publicación es 4.
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 11 julio, 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.

40 334

Suscriptores

+2524 horas

+1227 días

+38330 días

948

Visitas de la publicación

~ 78624 horas

~ 1 06648 horas

2.35%

Tasa de compromiso

~ 2

Mensajes por día

Ads index

beta

Archivo de publicaciones

40 334

📌 Step-by-Step Guide to Build and Deploy an LLM-Powered Chat with Memory in Streamlit 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2025-05-01 | ⏱️ Read time: 17 min read And monitor your API usage on Google Cloud Console

40 334

📌 A Farewell to APMs — The Future of Observability is MCP tools 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2025-05-01 | ⏱️ Read time: 10 min read Like many other fields, the world of observability is about to be turned upside down

40 334

📌 Rust for Python Developers: Why You Should Take a Look at the Rust Programming Language 🗂 Category: PROGRAMMING 🕒 Date: 2025-05-02 | ⏱️ Read time: 13 min read Discover how Rust complements Python with speed, safety, and control — and why it’s worth…

40 334

📌 Agentic AI 101: Starting Your Journey Building AI Agents 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2025-05-02 | ⏱️ Read time: 12 min read Learn the fundamentals of how to create AI Agents.

40 334

📌 Talking to Kids About AI 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2025-05-02 | ⏱️ Read time: 16 min read “This is your brain on an LLM”, and other things you shouldn’t say

40 334

📌 Want Better Clusters? Try DeepType 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2025-05-02 | ⏱️ Read time: 9 min read A smarter way to cluster data using deep learning

40 334

📌 The Difference between Duplicate and Reference in Power Query 🗂 Category: DATA ENGINEERING 🕒 Date: 2025-05-02 | ⏱️ Read time: 9 min read In Power Query, we can duplicate or reference existing tables. But what are the differences…

40 334

📌 Why I stopped Using Cursor and Reverted to VSCode 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2025-05-02 | ⏱️ Read time: 6 min read Is GitHub Copilot the best AI-assistant for Data Scientists?

40 334

📌 The Shape‑First Tune‑Up Provides Organizations with a Means to Reduce MongoDB Expenses by 79% 🗂 Category: DATA ENGINEERING 🕒 Date: 2025-05-02 | ⏱️ Read time: 9 min read A real-world engineering fix that saved over $12K/month on MongoDB without upgrading infrastructure.

40 334

📌 Attaining LLM Certainty with AI Decision Circuits 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2025-05-02 | ⏱️ Read time: 15 min read Uncertainty is nothing new in technology — all modern systems overcome uncertain inputs and outputs…

40 334

📌 Build and Query Knowledge Graphs with LLMs 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2025-05-02 | ⏱️ Read time: 28 min read Going from document ingestion to smart queries — all with open tools and guided setup

40 334

📌 From a Point to L∞ 🗂 Category: MATH 🕒 Date: 2025-05-02 | ⏱️ Read time: 9 min read How AI uses distance

40 334

📌 Website Feature Engineering at Scale: PySpark, Python & Snowflake 🗂 Category: DATA SCIENCE 🕒 Date: 2025-05-05 | ⏱️ Read time: 9 min read Introduction and Problem Imagine you’re staring at a database containing thousands of merchants across multiple…

40 334

📌 Fine-Tuning vLLMs for Document Understanding 🗂 Category: MACHINE LEARNING 🕒 Date: 2025-05-05 | ⏱️ Read time: 25 min read Learn how you can fine-tune visual language models for specific tasks

40 334

📌 Making Sense of KPI Changes 🗂 Category: DATA SCIENCE 🕒 Date: 2025-05-05 | ⏱️ Read time: 15 min read A practical guide to understanding what’s really going on

40 334

📌 Diffusion Models, Explained Simply 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2025-05-05 | ⏱️ Read time: 7 min read From noise to art: how to generate high-quality images using diffusion models

40 334

📌 The CNN That Challenges ViT | ConvNeXt 🗂 Category: DEEP LEARNING 🕒 Date: 2025-05-05 | ⏱️ Read time: 24 min read A PyTorch implementation on the ConvNeXt architecture

40 334

📌 Think. Know. Act. How AI’s Core Capabilities Will Shape the Future of Work 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2025-05-06 | ⏱️ Read time: 13 min read It’s not just about technical depth, it’s about strategic clarity

40 334

📌 Benchmarking Tabular Reinforcement Learning Algorithms 🗂 Category: MACHINE LEARNING 🕒 Date: 2025-05-06 | ⏱️ Read time: 27 min read Comparing all methods from Part I of Sutton’s book on gridworld environments

40 334

📌 Make Your Data Move: Creating Animations in Python for Science and Machine Learning 🗂 Category: DATA VISUALIZATION 🕒 Date: 2025-05-06 | ⏱️ Read time: 6 min read Go beyond static plots with matplotlib.