es
Feedback
Machine Learning

Machine Learning

Ir al canal en Telegram

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 40 202 suscriptores, ocupando la posición 3 365 en la categoría Tecnologías y Aplicaciones y el puesto 227 en la región Siria.

📊 Métricas de audiencia y dinámica

Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 40 202 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.

40 202
Suscriptores
+1024 horas
+837 días
+34330 días
Archivo de publicaciones
📌 Essential Guide to Continuous Ranked Probability Score (CRPS) for Forecasting 🗂 Category: DATA SCIENCE 🕒 Date: 2024-08-3
📌 Essential Guide to Continuous Ranked Probability Score (CRPS) for Forecasting 🗂 Category: DATA SCIENCE 🕒 Date: 2024-08-31 | ⏱️ Read time: 7 min read Learn how to evaluate probabilistic forecasts and how CRPS relates to other metrics

📌 How to Deal with Time Series Outliers 🗂 Category: DATA SCIENCE 🕒 Date: 2024-08-31 | ⏱️ Read time: 6 min read Understandi
📌 How to Deal with Time Series Outliers 🗂 Category: DATA SCIENCE 🕒 Date: 2024-08-31 | ⏱️ Read time: 6 min read Understanding, detecting and replacing outliers in time series

📌 Data Scientists Can’t Excel in Python Without Mastering These Functions 🗂 Category: DATA SCIENCE 🕒 Date: 2024-08-31 | ⏱️
📌 Data Scientists Can’t Excel in Python Without Mastering These Functions 🗂 Category: DATA SCIENCE 🕒 Date: 2024-08-31 | ⏱️ Read time: 11 min read Introduction of Python’s core functions, use cases, scripts, and underlying mechanisms

📌 Streamline Property Data Management: Advanced Data Extraction & Retrieval with Indexify 🗂 Category: 🕒 Date: 2024-08-31 |
📌 Streamline Property Data Management: Advanced Data Extraction & Retrieval with Indexify 🗂 Category: 🕒 Date: 2024-08-31 | ⏱️ Read time: 15 min read A Step-by-Step Guide to Document Querying with Indexify

📌 The DIY Path to AI Product Management: Picking a Starter Project 🗂 Category: CHATGPT 🕒 Date: 2024-08-31 | ⏱️ Read time:
📌 The DIY Path to AI Product Management: Picking a Starter Project 🗂 Category: CHATGPT 🕒 Date: 2024-08-31 | ⏱️ Read time: 8 min read Building real-world skills through hands-on trial and error.

📌 Building Scalable Data Platforms 🗂 Category: ANALYTICS 🕒 Date: 2024-09-01 | ⏱️ Read time: 14 min read Data Mesh trends i
📌 Building Scalable Data Platforms 🗂 Category: ANALYTICS 🕒 Date: 2024-09-01 | ⏱️ Read time: 14 min read Data Mesh trends in data platform design

📌 Training AI Models on CPU 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-09-01 | ⏱️ Read time: 16 min read Revisiting
📌 Training AI Models on CPU 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-09-01 | ⏱️ Read time: 16 min read Revisiting CPU for ML in an Era of GPU Scarcity

📌 Create Your Own Meal Planner Using ChatGPT 🗂 Category: CHATGPT 🕒 Date: 2024-09-02 | ⏱️ Read time: 19 min read A brief gu
📌 Create Your Own Meal Planner Using ChatGPT 🗂 Category: CHATGPT 🕒 Date: 2024-09-02 | ⏱️ Read time: 19 min read A brief guide to prompt engineering

📌 Mathematics of Love: Optimizing a Dining-Room Seating Arrangement for Weddings with Python 🗂 Category: DATA SCIENCE 🕒 Da
📌 Mathematics of Love: Optimizing a Dining-Room Seating Arrangement for Weddings with Python 🗂 Category: DATA SCIENCE 🕒 Date: 2024-09-02 | ⏱️ Read time: 19 min read Solving the Restricted Quadratic Multi-Knapsack Problem (RQMKP) with mathematical programming and Python

📌 An Easy Way to Remove Tourists from Photos 🗂 Category: PYTHON 🕒 Date: 2024-09-02 | ⏱️ Read time: 9 min read Image cleanu
📌 An Easy Way to Remove Tourists from Photos 🗂 Category: PYTHON 🕒 Date: 2024-09-02 | ⏱️ Read time: 9 min read Image cleanup with Python, PIL, and OpenCV

📌 Encoding Categorical Data, Explained: A Visual Guide with Code Example for Beginners 🗂 Category: DATA SCIENCE 🕒 Date: 20
📌 Encoding Categorical Data, Explained: A Visual Guide with Code Example for Beginners 🗂 Category: DATA SCIENCE 🕒 Date: 2024-09-02 | ⏱️ Read time: 10 min read Six ways of matchmaking categories and numbers

📌 Use R to build Clinical Flowchart with shinyCyJS 🗂 Category: 🕒 Date: 2024-09-03 | ⏱️ Read time: 6 min read Customizable
📌 Use R to build Clinical Flowchart with shinyCyJS 🗂 Category: 🕒 Date: 2024-09-03 | ⏱️ Read time: 6 min read Customizable R package for Graph / Network visualization

📌 Subway Route Data Extraction with Overpass API: A Step-by-Step Guide 🗂 Category: DATA SCIENCE 🕒 Date: 2024-09-03 | ⏱️ Re
📌 Subway Route Data Extraction with Overpass API: A Step-by-Step Guide 🗂 Category: DATA SCIENCE 🕒 Date: 2024-09-03 | ⏱️ Read time: 11 min read Simplify Geodata Extraction from OpenStreetMaps via the Overpass API

📌 Information in Noise 🗂 Category: DATA SCIENCE 🕒 Date: 2024-09-03 | ⏱️ Read time: 4 min read Two Techniques for Visualizi
📌 Information in Noise 🗂 Category: DATA SCIENCE 🕒 Date: 2024-09-03 | ⏱️ Read time: 4 min read Two Techniques for Visualizing Many Time-Series at Once

📌 5 Pillars for a Hyper-Optimized AI Workflow 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-09-03 | ⏱️ Read time: 8 min
📌 5 Pillars for a Hyper-Optimized AI Workflow 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-09-03 | ⏱️ Read time: 8 min read A gentle introduction to a methodology for creating production-ready, extensible & highly optimized AI workflows

📌 Line-By-Line, Let’s Reproduce GPT-2: Section 3 – Training 🗂 Category: 🕒 Date: 2024-09-03 | ⏱️ Read time: 20 min read Thi
📌 Line-By-Line, Let’s Reproduce GPT-2: Section 3 – Training 🗂 Category: 🕒 Date: 2024-09-03 | ⏱️ Read time: 20 min read This blog post will go line-by-line through the code in Section 3 of Andrej Karpathy’s…

📌 Using Generative AI To Get Insights From Disorderly Data 🗂 Category: 🕒 Date: 2024-09-03 | ⏱️ Read time: 41 min read Best
📌 Using Generative AI To Get Insights From Disorderly Data 🗂 Category: 🕒 Date: 2024-09-03 | ⏱️ Read time: 41 min read Best practices for using Large Language Models to extract actionable insights even with poor metadata

📌 Here Comes Mamba: The Selective State Space Model 🗂 Category: DEEP LEARNING 🕒 Date: 2024-09-03 | ⏱️ Read time: 22 min re
📌 Here Comes Mamba: The Selective State Space Model 🗂 Category: DEEP LEARNING 🕒 Date: 2024-09-03 | ⏱️ Read time: 22 min read Part 3 – Towards Mamba State Space Models for Images, Videos and Time Series

📌 Diving Deeper with Structured Outputs 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2024-09-03 | ⏱️ Read time: 10 min read E
📌 Diving Deeper with Structured Outputs 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2024-09-03 | ⏱️ Read time: 10 min read Enhancing our understanding and optimal usage of structured outputs

📌 Approximating Stochastic Functions with Multivariate Outputs 🗂 Category: 🕒 Date: 2024-09-04 | ⏱️ Read time: 25 min read
📌 Approximating Stochastic Functions with Multivariate Outputs 🗂 Category: 🕒 Date: 2024-09-04 | ⏱️ Read time: 25 min read A generic approach for training probabilistic machine learning models