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 365...

📈 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

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

40 204

📌 Open-Source Data Observability with Elementary – From Zero to Hero (Part 1) 🗂 Category: DATA ENGINEERING 🕒 Date: 2024-09-10 | ⏱️ Read time: 7 min read A step-by-step hands-on guide I wish I had when I was a beginner

40 204

📌 Open-Source Data Observability with Elementary - From Zero to Hero (Part 2) 🗂 Category: 🕒 Date: 2024-09-10 | ⏱️ Read time: 7 min read The guide to take your dbt tests to the next level for free

40 204

📌 Linear Programming Optimization: The Simplex Method 🗂 Category: STATISTICS 🕒 Date: 2024-09-10 | ⏱️ Read time: 15 min read Part 3: The algorithm under the hood

40 204

📌 Automating Research Workflows with LLMs 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-09-10 | ⏱️ Read time: 14 min read Augmenting researchers with atomic usage of AI

40 204

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40 204

📌 Introducing NumPy, Part 1: Understanding Arrays 🗂 Category: DATA SCIENCE 🕒 Date: 2024-09-10 | ⏱️ Read time: 22 min read Creating, describing, and accessing attributes

40 204

📌 How Tiny Neural Networks Represent Basic Functions 🗂 Category: MACHINE LEARNING 🕒 Date: 2024-09-10 | ⏱️ Read time: 9 min read A gentle introduction to mechanistic interpretability through simple algorithmic examples

40 204

📌 To Care, or Not to Care: Using XmR Charts to Differentiate Signals from Noise in Metrics 🗂 Category: DATA SCIENCE 🕒 Date: 2024-09-10 | ⏱️ Read time: 12 min read A Step-by-Step Guide to Creating and Interpreting XmR Charts for Effective Data Analysis

40 204

📌 How to Create a Powerful AI Email Search for Gmail with RAG 🗂 Category: 🕒 Date: 2024-09-10 | ⏱️ Read time: 17 min read Learn how you can develop an application to search emails using RAG

40 204

📌 How I Streamline My Research and Presentation with LlamaIndex Workflows 🗂 Category: MACHINE LEARNING 🕒 Date: 2024-09-10 | ⏱️ Read time: 19 min read An example of orchestrating AI workflow with robustness, flexibility and controllability

40 204

📌 The Taylor Series, Explained 🗂 Category: CALCULUS 🕒 Date: 2024-09-11 | ⏱️ Read time: 16 min read A method for function approximation

40 204

📌 Is Your User Base Growing or Shrinking? 🗂 Category: 🕒 Date: 2024-09-11 | ⏱️ Read time: 6 min read How tracking customer segmentation and KPIs reveals the true health of your business

40 204

📌 Forecasting Germany’s Solar Energy Production: A Practical Approach with Prophet 🗂 Category: DATA SCIENCE 🕒 Date: 2024-09-11 | ⏱️ Read time: 8 min read Analysis and implementation with Python

40 204

📌 Market Basket Analysis Using High Utility Itemset Mining 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-09-11 | ⏱️ Read time: 10 min read Finding high-value patterns in transactions

40 204

📌 A Step-by-Step Guide to Build a Graph Learning System for a Movie Recommender 🗂 Category: DEEP LEARNING 🕒 Date: 2024-09-11 | ⏱️ Read time: 15 min read Built with PyTorch Geometric and using MovieLens DataSet

40 204

📌 Deploying your Llama Model via vLLM using SageMaker Endpoint 🗂 Category: DATA SCIENCE 🕒 Date: 2024-09-12 | ⏱️ Read time: 9 min read Leveraging AWS’s MLOps platform to serve your LLM models

40 204

📌 How to Build a Competency Framework for Data Science Teams 🗂 Category: DATA SCIENCE 🕒 Date: 2024-09-12 | ⏱️ Read time: 11 min read For those leading Data Science teams, here are 6 essential competencies that separate juniors from…

40 204

📌 Smarter, Not Harder: How AI’s Self-Doubt Unlocks Peak Performance 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2025-10-02 | ⏱️ Read time: 10 min read “Deep Think with Confidence,” a smarter way to scale reasoning tasks without wasting a massive amount…

40 204

📌 What Makes a Language Look Like Itself? 🗂 Category: NATURAL LANGUAGE PROCESSING 🕒 Date: 2025-10-02 | ⏱️ Read time: 8 min read How simple statistics reveal the visual fingerprints of 20 languages

40 204

📌 AI Engineering and Evals as New Layers of Software Work 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2025-10-02 | ⏱️ Read time: 8 min read How to maintain reliability in inherently stochastic systems