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 Siria228 Tecnologías y Aplicaciones3 352...

📈 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 40 205 suscriptores, ocupando la posición 3 352 en la categoría Tecnologías y Aplicaciones y el puesto 228 en la región Siria.

📊 Métricas de audiencia y dinámica

Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 40 205 suscriptores.

Según los últimos datos del 02 julio, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de 343, y en las últimas 24 horas de 10, conservando un alto alcance.

Estado de verificación: No verificado
Tasa de interacción (ER): El promedio de interacción de la audiencia es 1.99%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 2.28% de reacciones respecto al total de suscriptores.
Alcance de las publicaciones: Cada publicación recibe en promedio 800 visualizaciones. En el primer día suele acumular 915 visualizaciones.
Reacciones e interacción: La audiencia responde de forma activa: el promedio de reacciones por publicación es 3.
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 03 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.

40 205

Suscriptores

+1024 horas

+837 días

+34330 días

800

Visitas de la publicación

~ 91524 horas

~ 1 23548 horas

1.99%

Tasa de compromiso

~ 3

Mensajes por día

Ads index

beta

Archivo de publicaciones

40 207

📌 Data Visualization Explained (Part 2): An Introduction to Visual Variables 🗂 Category: DATA VISUALIZATION 🕒 Date: 2025-10-01 | ⏱️ Read time: 7 min read A non-technical and accessible guide to the underlying concept behind visual design: visual encoding channels

40 207

📌 How to Improve the Efficiency of Your PyTorch Training Loop 🗂 Category: DEEP LEARNING 🕒 Date: 2025-10-01 | ⏱️ Read time: 14 min read Learn how to diagnose and resolve bottlenecks in PyTorch using the numworkers, pinmemory, and profiler…

40 207

📌 Are Foundation Models Ready for Your Production Tabular Data? 🗂 Category: LARGE DATA MODELS 🕒 Date: 2025-10-01 | ⏱️ Read time: 15 min read A complete review of architectures to make zero-shot predictions in the most common types of…

40 207

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40 207

“I turned $1,000 into $4,500 in just 2 weeks — but nobody believed me until they saw my account.” Want to know the exact signals I used? The secret’s hidden right here — but hurry, only a few will see this in time. #ad InsideAds

40 207

📌 The Data Strategy Choice Cascade 🗂 Category: 🕒 Date: 2024-09-16 | ⏱️ Read time: 23 min read What your data strategy should look like

40 207

📌 How to Implement State-of-the-Art Masked AutoEncoders (MAE) 🗂 Category: DATA SCIENCE 🕒 Date: 2024-09-16 | ⏱️ Read time: 8 min read A Step-by-Step Guide to Building MAE with Vision Transformers

40 207

📌 Unit Disk Uniform Sampling 🗂 Category: DATA SCIENCE 🕒 Date: 2024-09-16 | ⏱️ Read time: 15 min read Discover the optimal transformations to apply on the standard 0,1 uniform random generator for uniformly…

40 207

📌 Vision Mamba: Like a Vision Transformer but Better 🗂 Category: DATA SCIENCE 🕒 Date: 2024-09-16 | ⏱️ Read time: 26 min read Part 4 – Towards Mamba State Space Models for Images, Videos and Time Series

40 207

📌 Teaching Your Model to Learn from Itself 🗂 Category: DATA SCIENCE 🕒 Date: 2024-09-16 | ⏱️ Read time: 6 min read In machine learning, more data leads to better results. But labeling data can be expensive…

40 207

📌 Disability, Accessibility, and AI 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-09-16 | ⏱️ Read time: 11 min read A discussion of how AI can help and harm people with disabilities

40 207

📌 Introducing NumPy, Part 4: Doing Math with Arrays 🗂 Category: 🕒 Date: 2024-09-16 | ⏱️ Read time: 12 min read Plus reading and writing array data!

40 207

📌 PySpark Explained: The InferSchema Problem 🗂 Category: DATA SCIENCE 🕒 Date: 2024-09-16 | ⏱️ Read time: 10 min read Think before using this common option when reading large CSV’s

40 207

“Nobody believed you could grow small capital—until I saw this.” $1,000 turned into real profit before my eyes. The secret? Bonus fuel & copytrading with Elite Gold. Want proof? See how it’s actually done before the bonus ends. #ad InsideAds

40 207

📌 Football and Geometry – Passing Networks 🗂 Category: DATA SCIENCE 🕒 Date: 2024-09-16 | ⏱️ Read time: 12 min read Analyzing Bayer Leverkusen’s Passing Networks from Last Season

40 207

📌 Model Management with MLflow, Azure, and Docker 🗂 Category: MACHINE LEARNING 🕒 Date: 2024-09-17 | ⏱️ Read time: 11 min read A guide to tracking experiments and managing models

40 207

📌 The Math Behind Kernel Density Estimation 🗂 Category: DATA SCIENCE 🕒 Date: 2024-09-17 | ⏱️ Read time: 13 min read Exploring the foundations, concepts, and math of kernel density estimation

40 207

📌 Polars + NVIDIA GPU Tutorial 🗂 Category: DATA SCIENCE 🕒 Date: 2024-09-17 | ⏱️ Read time: 4 min read Using Polars with NVIDIA GPU can speed up your data pipelines

40 207

📌 GPU Accelerated Polars – Intuitively and Exhaustively Explained 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-09-17 | ⏱️ Read time: 16 min read Fast Dataframes for Big Problems

40 207

Repost from Machine Learning with Python

Today I am 3️⃣0️⃣ years old, I am excited to make more successes and achievements My previous year was full of exciting events and economic, political and programmatic noise, but I kept moving forward Best regards Eng. @HusseinSheikho 🔤