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 332...

📈 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 40 310 suscriptores, ocupando la posición 3 332 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 40 310 suscriptores.

Según los últimos datos del 09 julio, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de 378, y en las últimas 24 horas de 30, conservando un alto alcance.

Estado de verificación: No verificado
Tasa de interacción (ER): El promedio de interacción de la audiencia es 2.23%. 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 897 visualizaciones. En el primer día suele acumular 788 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 10 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.

40 310

Suscriptores

+3024 horas

+1067 días

+37830 días

897

Visitas de la publicación

~ 78824 horas

~ 1 02448 horas

2.23%

Tasa de compromiso

~ 2

Mensajes por día

Ads index

beta

Archivo de publicaciones

40 307

📌 Preparing PDFs for RAGs 🗂 Category: DATA SCIENCE 🕒 Date: 2025-01-17 | ⏱️ Read time: 5 min read I created a graph storage from dozens of annual reports (with tables)

40 307

📌 A Practical Exploration of Sora – Intuitively and Exhaustively Explained 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2025-01-17 | ⏱️ Read time: 23 min read A new cutting edge video generation tool, and the theory behind it

40 307

📌 Where to Start when Data is Limited: A Guide 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2025-01-17 | ⏱️ Read time: 23 min read Overcome small data constraints & ambitious performance requirements-leveraging modern ML to surpass conventional methods.

40 307

📌 My Experience Switching From Power BI to Looker (as a Senior Data Analyst) 🗂 Category: MICROSOFT 🕒 Date: 2025-01-17 | ⏱️ Read time: 17 min read What you need to know before you switch from Power BI to Looker.

40 307

📌 Showcasing Soaring Wildfire Counts With Streamlit and Python: A Powerful Approach 🗂 Category: DATA VISUALIZATION 🕒 Date: 2025-01-18 | ⏱️ Read time: 13 min read Analyzing historical wildfire trends in Canada with public data

40 307

📌 Modern Data And Application Engineering Breaks the Loss of Business Context 🗂 Category: DATA ENGINEERING 🕒 Date: 2025-01-18 | ⏱️ Read time: 16 min read Here’s how your data retains its business relevance as it travels through your enterprise

40 307

📌 How to Log Your Data with MLflow 🗂 Category: DATA SCIENCE 🕒 Date: 2025-01-19 | ⏱️ Read time: 12 min read Mastering data logging in MLOps for your AI workflow

40 307

📌 Zero-Shot Player Tracking in Tennis with Kalman Filtering 🗂 Category: 🕒 Date: 2025-01-19 | ⏱️ Read time: 10 min read Automated tennis tracking without labels: GroundingDINO, Kalman filtering, and court homography.

40 307

📌 The Concepts Data Professionals Should Know in 2025: Part 1 🗂 Category: DATA ENGINEERING 🕒 Date: 2025-01-19 | ⏱️ Read time: 14 min read From Data Lakehouses to Event-Driven Architecture – Master 12 data concepts and turn them into…

40 307

📌 Designing, Building & Deploying an AI Chat App from Scratch (Part 1) 🗂 Category: 🕒 Date: 2025-01-20 | ⏱️ Read time: 19 min read Microservices Architecture and Local Development

40 307

📌 Designing, Building & Deploying an AI Chat App from Scratch (Part 2) 🗂 Category: 🕒 Date: 2025-01-20 | ⏱️ Read time: 20 min read Cloud Deployment and Scaling

40 307

📌 The Concepts Data Professionals Should Know in 2025: Part 2 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2025-01-20 | ⏱️ Read time: 14 min read From AI Agent to Human-In-The-Loop – Master 12 critical data concepts and turn them into…

40 307

📌 Neural Networks for Time-Series Imputation: Tackling Missing Data 🗂 Category: MACHINE LEARNING 🕒 Date: 2025-01-22 | ⏱️ Read time: 11 min read Part 3: Discover how a simple Keras sequential model can be effective

40 307

📌 Human Minds and Machine Learning Models 🗂 Category: MACHINE LEARNING 🕒 Date: 2025-01-22 | ⏱️ Read time: 14 min read Exploring the parallels and differences between psychology and machine learning

40 307

📌 How to Utilize ModernBERT and Synthetic Data for Robust Text Classification 🗂 Category: MACHINE LEARNING 🕒 Date: 2025-01-22 | ⏱️ Read time: 10 min read Learn how to fine-tune ModernBERT and create augmentations of text samples

40 307

📌 How to Evaluate LLM Summarization 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2025-01-22 | ⏱️ Read time: 18 min read A practical and effective guide for evaluating AI summaries

40 307

📌 Topic Modelling in Business Intelligence: FASTopic and BERTopic in Code 🗂 Category: MACHINE LEARNING 🕒 Date: 2025-01-22 | ⏱️ Read time: 11 min read A comparison of two cutting-edge dynamic topic models solving consumer complaints classification exercise

40 307

📌 Understanding Emergent Capabilities in LLMs: Lessons from Biological Systems 🗂 Category: 🕒 Date: 2025-01-22 | ⏱️ Read time: 24 min read How natural systems fundamental laws help explain AI’s unexpected abilities

40 307

📌 Harmonizing and Pooling Datasets for Health Research in R 🗂 Category: CODING 🕒 Date: 2025-01-22 | ⏱️ Read time: 11 min read R code to extract data from unique datasets and combine them in one harmonized dataset…

40 307

📌 Behind the Scenes of a Successful Data Analytics Project 🗂 Category: DATA SCIENCE 🕒 Date: 2025-01-23 | ⏱️ Read time: 10 min read Learn the steps to approach any data analytics project like a pro.