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 Siria227 Tecnologías y Aplicaciones3 375...

📈 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 40 145 suscriptores, ocupando la posición 3 375 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 40 145 suscriptores.

Según los últimos datos del 28 junio, 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 7, conservando un alto alcance.

Estado de verificación: No verificado
Tasa de interacción (ER): El promedio de interacción de la audiencia es 2.09%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 1.91% de reacciones respecto al total de suscriptores.
Alcance de las publicaciones: Cada publicación recibe en promedio 841 visualizaciones. En el primer día suele acumular 766 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 29 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.

40 145

Suscriptores

+724 horas

+1147 días

+37830 días

841

Visitas de la publicación

~ 76624 horas

~ 1 08948 horas

2.09%

Tasa de compromiso

~ 3

Mensajes por día

Ads index

beta

Archivo de publicaciones

40 147

📌 A New Method to Detect “Confabulations” Hallucinated by Large Language Models 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-06-25 | ⏱️ Read time: 12 min read By calculating semantic entropy with a second LLM, we can better flag answers as unreliable…

40 147

📌 Making LLMs Write Better and Better Code for Self-Driving Using LangProp 🗂 Category: CHATGPT 🕒 Date: 2024-06-25 | ⏱️ Read time: 11 min read Analogy from classical machine learning: LLM (Large Language Model) = optimizer; code = parameters; LangProp…

40 147

📌 Improving RAG Performance Using Rerankers 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-06-25 | ⏱️ Read time: 11 min read A tutorial on using rerankers to improve your RAG pipeline

40 147

📌 The Intuitive Basics of Optimization 🗂 Category: DATA SCIENCE 🕒 Date: 2024-06-26 | ⏱️ Read time: 14 min read A gentle introduction to the amazing field of optimization

40 147

📌 Business Planning with Python – Revenue Optimization 🗂 Category: BUSINESS 🕒 Date: 2024-06-26 | ⏱️ Read time: 14 min read How can you use data analytics to help small businesses maximize their revenue while maintaining…

40 147

📌 How Bend Works: A Parallel Programming Language That “Feels Like Python but Scales Like CUDA” 🗂 Category: 🕒 Date: 2024-06-26 | ⏱️ Read time: 26 min read A brief introduction to Lambda Calculus, Interaction Combinators, and how they are used to parallelize…

40 147

📌 The Ultimate Guide to Finding Outliers in Your Time-Series Data (Part 2) 🗂 Category: DATA SCIENCE 🕒 Date: 2024-06-26 | ⏱️ Read time: 1 min read Effective machine learning methods and tools for outlier detection in time-series analysis

40 147

📌 A Complete Guide to Master Step Functions on AWS 🗂 Category: SCIENCE AND TECHNOLOGY 🕒 Date: 2024-06-27 | ⏱️ Read time: 10 min read Workflow orchestration made easier

40 147

📌 3 Challenges to Being a Data Scientist in 2024 🗂 Category: CAREER ADVICE 🕒 Date: 2024-06-27 | ⏱️ Read time: 7 min read Given the current climate, is data science for you?

40 147

📌 Classification Loss Functions: Intuition and Applications 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-06-27 | ⏱️ Read time: 9 min read A simpler way to understand derivations of loss functions for classification and when/how to apply…

40 147

📌 Prompt Engineering: Tips, Approaches, and Future Directions 🗂 Category: DATA SCIENCE 🕒 Date: 2024-06-27 | ⏱️ Read time: 5 min read Our weekly selection of must-read Editors’ Picks and original features

40 147

📌 Understanding Transformers 🗂 Category: DEEP LEARNING 🕒 Date: 2024-06-27 | ⏱️ Read time: 12 min read A straightforward breakdown of “Attention is All You Need”¹

40 147

📌 I Invented a Way to Speak to an AI, Keeping Your Privacy 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-06-28 | ⏱️ Read time: 9 min read The tech is called “Silent Voice.”

40 147

📌 The Math Behind Risk – Part 1 🗂 Category: DATA SCIENCE 🕒 Date: 2024-06-28 | ⏱️ Read time: 11 min read Does the attack really have an advantage in the game of world conquest?

40 147

📌 The History of Convolutional Neural Networks for Image Classification (1989- Today) 🗂 Category: DEEP LEARNING 🕒 Date: 2024-06-28 | ⏱️ Read time: 18 min read A tour through the history of Computer Vision!

40 147

📌 Safeguarding Demand Forecasting with Causal Graphs 🗂 Category: DATA SCIENCE 🕒 Date: 2024-06-28 | ⏱️ Read time: 11 min read Causal AI, exploring the integration of causal reasoning into machine learning

40 147

📌 Diving Deep into AutoGen and Agentic Frameworks 🗂 Category: 🕒 Date: 2024-06-28 | ⏱️ Read time: 13 min read This blog post will go into the details of the “AutoGen: Enabling Next-Gen LLM Applications…

40 147

📌 Estimate the unobserved – Moving-Average Model Estimation with Maximum Likelihood in Python 🗂 Category: DATA SCIENCE 🕒 Date: 2024-06-28 | ⏱️ Read time: 8 min read How unobserved covariates’ coefficients can be estimated with MLE

40 147

📌 CRAG – Intuitively and Exhaustively Explained 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-06-28 | ⏱️ Read time: 13 min read Defining the Limits of Retrieval Augmented Generation

40 147

📌 System Design: Load Balancer 🗂 Category: 🕒 Date: 2024-06-28 | ⏱️ Read time: 9 min read Orchestrating strategies for optimal workload distribution in microservice applications