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 364...

📈 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

785

Visitas de la publicación

~ 76024 horas

~ 1 06948 horas

1.96%

Tasa de compromiso

~ 3

Mensajes por día

Ads index

beta

Archivo de publicaciones

40 150

📌 A Day in the Life of a Data Scientist 🗂 Category: CAREER ADVICE 🕒 Date: 2024-06-08 | ⏱️ Read time: 8 min read What do I actually do all day, anyway?

40 150

📌 Python Data Analysis: What Do We Know About Modern Artists? 🗂 Category: DATA SCIENCE 🕒 Date: 2024-06-08 | ⏱️ Read time: 15 min read Finding patterns in the media landscape with Wikipedia, Python, and NetworkX

40 150

📌 Paper review – Communicative Agents for Software Development 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-06-08 | ⏱️ Read time: 12 min read After reading and reviewing the Generative Agents paper, I decided to explore the world of…

40 150

📌 SQL Knowledge You Need For Data Science 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-06-08 | ⏱️ Read time: 11 min read Topics, resources and advice for becoming proficient in SQL.

40 150

📌 Validating the Causal Impact of the Synthetic Control Method 🗂 Category: DATA SCIENCE 🕒 Date: 2024-06-08 | ⏱️ Read time: 11 min read Causal AI, exploring the integration of causal reasoning into machine learning

40 150

📌 What “Dream Big” Meant for Data Science Innovation at LinkedIn 🗂 Category: BUSINESS 🕒 Date: 2024-06-09 | ⏱️ Read time: 10 min read Here’s how to inspire and lead people for bigger data science projects

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📌 Here is what using an LLM for monsters taught me about programming 🗂 Category: PROGRAMMING 🕒 Date: 2024-06-09 | ⏱️ Read time: 9 min read How I learned to use AI as an alternative to generate amazing random data.

40 150

What do you think about the content of these articles? Useful Content 👍 Unhelpful content 👎

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📌 Hands On Optimization with Expected Improvement and Gaussian Process Regression, in Python 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-06-09 | ⏱️ Read time: 12 min read A friendly guide to Expected Improvement for Global Optimization, in Python

40 150

📌 Pandas Indexes And Headers, Have You Ever Been Confused? 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-06-09 | ⏱️ Read time: 8 min read From single-level index and headers to multi-level, why and how?

40 150

📌 How LLMs Will Democratize Exploratory Data Analysis 🗂 Category: DATA SCIENCE 🕒 Date: 2024-06-09 | ⏱️ Read time: 19 min read Or, When you feel your life’s too hard, just go have a talk with Claude

40 150

📌 It’s Time to Finally Memorize those Dang Classification Metrics! 🗂 Category: DATA SCIENCE 🕒 Date: 2024-06-10 | ⏱️ Read time: 11 min read Intuition behind the metrics and how I finally memorized them

40 150

📌 From Masked Image Modeling to Autoregressive Image Modeling 🗂 Category: DEEP LEARNING 🕒 Date: 2024-06-10 | ⏱️ Read time: 5 min read A brief review of the image foundation model pre-training objectives

40 150

📌 Building LLM Apps: A Clear Step-By-Step Guide 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-06-10 | ⏱️ Read time: 14 min read Comprehensive Steps for Building LLM-Native Apps: From Initial Idea to Experimentation, Evaluation, and Productization

40 150

📌 Deploy a LightGBM ML Model With GitHub Actions 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-06-10 | ⏱️ Read time: 9 min read A beginner’s guide to getting out of Jupyter notebooks and deploying ML models

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📌 How Do Computers Actually Compute? 🗂 Category: DATA SCIENCE 🕒 Date: 2024-06-10 | ⏱️ Read time: 10 min read A Budding Data Scientist’s Introduction to Computer Hardware

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📌 TDS Newsletter: How to Keep LLMs Effective and Reliable Over Time 🗂 Category: THE VARIABLE 🕒 Date: 2025-10-09 | ⏱️ Read time: 4 min read Those of you who’ve worked with LLM-powered applications know this: by now, building and deploying these tools…

40 150

📌 TDS Newsletter: The Rapid Transformation of Data Science in the Age of AI 🗂 Category: THE VARIABLE 🕒 Date: 2025-10-16 | ⏱️ Read time: 3 min read How data science became a strikingly different discipline in the span of a couple of…

40 150

📌 Statistical Method mcRigor Enhances the Rigor of Metacell Partitioning in Single-Cell Data Analysis 🗂 Category: DATA SCIENCE 🕒 Date: 2025-10-17 | ⏱️ Read time: 6 min read mcRigor detects dubious metacells within each metacell partition and selects the optimal metacell partitioning method…

40 150

📌 How I Used Machine Learning to Predict 41% of Project Delays Before They Happened 🗂 Category: PROJECT MANAGEMENT 🕒 Date: 2025-10-17 | ⏱️ Read time: 12 min read How data science can help project managers anticipate risks and save time