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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

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📈 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 40 150 suscriptores, ocupando la posición 3 364 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 40 150 suscriptores.

Según los últimos datos del 27 junio, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de 412, y en las últimas 24 horas de 5, conservando un alto alcance.

  • Estado de verificación: No verificado
  • Tasa de interacción (ER): El promedio de interacción de la audiencia es 1.96%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 1.89% de reacciones respecto al total de suscriptores.
  • Alcance de las publicaciones: Cada publicación recibe en promedio 785 visualizaciones. En el primer día suele acumular 760 visualizaciones.
  • Reacciones e interacción: La audiencia responde de forma activa: el promedio de reacciones por publicación es 2.
  • 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 28 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.

40 150
Suscriptores
+524 horas
+1067 días
+41230 días
Archivo de publicaciones
📌 Acquire Customers with Ecommerce Data Science 🗂 Category: DATA SCIENCE 🕒 Date: 2024-06-05 | ⏱️ Read time: 7 min read Dat
📌 Acquire Customers with Ecommerce Data Science 🗂 Category: DATA SCIENCE 🕒 Date: 2024-06-05 | ⏱️ Read time: 7 min read Data informed strategies help ecommerce businesses overcome advertising challenges

📌 Cross-validation with XGBoost – Enhancing Customer Churn Classification with Tidymodels 🗂 Category: DATA SCIENCE 🕒 Date:
📌 Cross-validation with XGBoost – Enhancing Customer Churn Classification with Tidymodels 🗂 Category: DATA SCIENCE 🕒 Date: 2024-06-06 | ⏱️ Read time: 6 min read Step-by-step guide to implementing cross-validation, feature engineering, and model evaluation with XGBoost in Tidymodels

📌 PAGA Explained: Graphical Abstractions of Single-Cell Data 🗂 Category: DATA VISUALIZATION 🕒 Date: 2024-06-06 | ⏱️ Read t
📌 PAGA Explained: Graphical Abstractions of Single-Cell Data 🗂 Category: DATA VISUALIZATION 🕒 Date: 2024-06-06 | ⏱️ Read time: 7 min read How a broader view of data can give us insights to its deeper meaning.

📌 My 30-Day Map Challenge 2023 🗂 Category: DATA SCIENCE 🕒 Date: 2024-06-06 | ⏱️ Read time: 9 min read An overview of selec
📌 My 30-Day Map Challenge 2023 🗂 Category: DATA SCIENCE 🕒 Date: 2024-06-06 | ⏱️ Read time: 9 min read An overview of selected map topics and algorithms

📌 Multilingual RAG, Algorithmic Thinking, Outlier Detection, and Other Problem-Solving Highlights 🗂 Category: DATA SCIENCE
📌 Multilingual RAG, Algorithmic Thinking, Outlier Detection, and Other Problem-Solving Highlights 🗂 Category: DATA SCIENCE 🕒 Date: 2024-06-06 | ⏱️ Read time: 4 min read Our weekly selection of must-read Editors’ Picks and original features

📌 SageMaker vs Vertex AI for Model Inference 🗂 Category: MACHINE LEARNING 🕒 Date: 2024-06-06 | ⏱️ Read time: 14 min read C
📌 SageMaker vs Vertex AI for Model Inference 🗂 Category: MACHINE LEARNING 🕒 Date: 2024-06-06 | ⏱️ Read time: 14 min read Comparing the AWS and GCP fully-managed services for ML workflows

📌 From Code to Insights: Software Engineering Best Practices for Data Analysts 🗂 Category: DATA SCIENCE 🕒 Date: 2024-06-06
📌 From Code to Insights: Software Engineering Best Practices for Data Analysts 🗂 Category: DATA SCIENCE 🕒 Date: 2024-06-06 | ⏱️ Read time: 20 min read Top 10 engineering lessons every data analyst should know

📌 Applied LLM Quantisation with AWS Sagemaker | Analytics.gov 🗂 Category: 🕒 Date: 2024-06-07 | ⏱️ Read time: 19 min read H
📌 Applied LLM Quantisation with AWS Sagemaker | Analytics.gov 🗂 Category: 🕒 Date: 2024-06-07 | ⏱️ Read time: 19 min read Host production-ready LLMs endpoints at twice the speed but one fifth the cost.

📌 How LLMs Think 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-06-07 | ⏱️ Read time: 11 min read Research paper in pill
📌 How LLMs Think 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-06-07 | ⏱️ Read time: 11 min read Research paper in pills: “Scaling Monosemanticity: Extracting Interpretable Features from Claude 3 Sonnet”

📌 YOLO – By Hand 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-06-07 | ⏱️ Read time: 6 min read A breakdown of the math
📌 YOLO – By Hand 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-06-07 | ⏱️ Read time: 6 min read A breakdown of the math within YOLO

📌 Fraud Prediction with Machine Learning in the Financial Industry: A Data Scientist’s Experience 🗂 Category: ARTIFICIAL IN
📌 Fraud Prediction with Machine Learning in the Financial Industry: A Data Scientist’s Experience 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-06-07 | ⏱️ Read time: 6 min read Insights and experiences from a data scientist on the frontlines

📌 Automating Prompt Engineering with DSPy and Haystack 🗂 Category: 🕒 Date: 2024-06-07 | ⏱️ Read time: 10 min read Teach yo
📌 Automating Prompt Engineering with DSPy and Haystack 🗂 Category: 🕒 Date: 2024-06-07 | ⏱️ Read time: 10 min read Teach your LLM how to talk through examples

📌 AI Assistants, Copilots, and Agents in Data & Analytics: What’s the Difference? 🗂 Category: MACHINE LEARNING 🕒 Date: 202
📌 AI Assistants, Copilots, and Agents in Data & Analytics: What’s the Difference? 🗂 Category: MACHINE LEARNING 🕒 Date: 2024-06-07 | ⏱️ Read time: 8 min read Understanding AI autonomy: assistants, copilots, agents, and their impact on business value

📌 Scale Is All You Need for Lip-Sync? 🗂 Category: DEEP LEARNING 🕒 Date: 2024-06-07 | ⏱️ Read time: 14 min read Alibaba’s E
📌 Scale Is All You Need for Lip-Sync? 🗂 Category: DEEP LEARNING 🕒 Date: 2024-06-07 | ⏱️ Read time: 14 min read Alibaba’s EMO and Microsoft’s VASA-1 are crazy good. Let’s break down how they work.

📌 Python 3.14 and the End of the GIL 🗂 Category: PROGRAMMING 🕒 Date: 2025-10-18 | ⏱️ Read time: 16 min read Exploring the
📌 Python 3.14 and the End of the GIL 🗂 Category: PROGRAMMING 🕒 Date: 2025-10-18 | ⏱️ Read time: 16 min read Exploring the opportunities and challenges of a GIL-free Python

📌 Can We Save the AI Economy? 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2025-10-18 | ⏱️ Read time: 23 min read And do we
📌 Can We Save the AI Economy? 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2025-10-18 | ⏱️ Read time: 23 min read And do we want to?

📌 How to Build a Generative Search Engine for Your Local Files Using Llama 3 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 202
📌 How to Build a Generative Search Engine for Your Local Files Using Llama 3 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2024-06-08 | ⏱️ Read time: 15 min read Use Qdrant, NVidia NIM API, or Llama 3 8B locally for your local GenAI assistant

📌 What Is a Good Imputation for Missing Values? 🗂 Category: STATISTICS 🕒 Date: 2024-06-08 | ⏱️ Read time: 21 min read My c
📌 What Is a Good Imputation for Missing Values? 🗂 Category: STATISTICS 🕒 Date: 2024-06-08 | ⏱️ Read time: 21 min read My current take on what imputation should be

📌 Principal Component Analysis Made Easy: A Step-by-Step Tutorial 🗂 Category: DATA SCIENCE 🕒 Date: 2024-06-08 | ⏱️ Read ti
📌 Principal Component Analysis Made Easy: A Step-by-Step Tutorial 🗂 Category: DATA SCIENCE 🕒 Date: 2024-06-08 | ⏱️ Read time: 10 min read Implement the PCA algorithm from scratch with Python

📌 Tiny Time Mixers (TTM): A Powerful Zero-Shot Forecasting Model by IBM 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-0
📌 Tiny Time Mixers (TTM): A Powerful Zero-Shot Forecasting Model by IBM 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-06-08 | ⏱️ Read time: 11 min read A new lightweight open-source foundation model