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

40 202

📌 LLMs, AI Agents, the Economics of Generative AI, and Other August Must-Reads 🗂 Category: DATA SCIENCE 🕒 Date: 2024-08-29 | ⏱️ Read time: 5 min read The stories that resonated the most with our community in the past month

40 202

📌 The Essential Guide to Error-Checking and Reviewing Presentations 🗂 Category: DATA SCIENCE 🕒 Date: 2024-08-29 | ⏱️ Read time: 7 min read An overlooked skill for Data Scientists (and not only)

40 202

📌 How to Create Custom Color Palettes in Matplotlib – Discrete vs. Linear Colormaps, Explained 🗂 Category: DATA SCIENCE 🕒 Date: 2024-08-29 | ⏱️ Read time: 6 min read Actionable guide on how to bring custom colors to personalize your charts

40 202

📌 The Smarter Way of Using AI in Programming 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-08-29 | ⏱️ Read time: 7 min read avoid the outdated methods of integrating AI into your coding workflow by going beyond ChatGPT

40 202

📌 Stop Manually Sorting Your List In Python If Performance Is Concerned 🗂 Category: DATA SCIENCE 🕒 Date: 2024-08-29 | ⏱️ Read time: 8 min read A sorted collection library that is as fast as C-extensions

40 202

📌 Stop Being Data-Driven 🗂 Category: DATA SCIENCE 🕒 Date: 2024-08-30 | ⏱️ Read time: 12 min read Why we are fooled by data and how to stop it

40 202

📌 How to Build a Genetic Algorithm from Scratch in Python 🗂 Category: DATA SCIENCE 🕒 Date: 2024-08-30 | ⏱️ Read time: 16 min read A complete walkthrough on how one can build a Genetic Algorithm from scratch in Python,…

40 202

📌 Causal Machine Learning for Customer Retention: a Practical Guide with Python 🗂 Category: MACHINE LEARNING 🕒 Date: 2024-08-30 | ⏱️ Read time: 25 min read An accessible guide to leveraging causal machine learning for optimizing client retention strategies

40 202

📌 How to Build a Powerful Deep Research System 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2025-10-04 | ⏱️ Read time: 6 min read Learn how to access vasts amounts of information with your own deep research system

40 202

📌 Real-Time Intelligence in Microsoft Fabric: The Ultimate Guide 🗂 Category: DATA SCIENCE 🕒 Date: 2025-10-04 | ⏱️ Read time: 21 min read Once upon a time, handling streaming data was considered an avant-garde approach. Since the introduction of relational…

40 202

📌 The Power of Pandas Plots: Backends 🗂 Category: DATA SCIENCE 🕒 Date: 2024-08-30 | ⏱️ Read time: 6 min read Create interactive graphics from Pandas effortlessly

40 202

📌 Hands On Neural Networks and Time Series, with Python 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-08-30 | ⏱️ Read time: 14 min read From the very simple Feed Forward Neural Networks to the majestic transformers: everything you need…

40 202

📌 A Comprehensive Introduction to Marketing Data Engineering 🗂 Category: DATA ENGINEERING 🕒 Date: 2024-08-30 | ⏱️ Read time: 21 min read Fundamentals, responsibilities, and challenges

40 202

📌 Compressing Large Language Models (LLMs) 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2024-08-30 | ⏱️ Read time: 12 min read Make LLMs 10X smaller without sacrificing performance

40 202

Awesome interactive textbook on probability theory and statistics Inside are clear visualizations, interactive elements, and minimal dry theory. You can tweak distributions, sample datasets, play with confidence intervals, and clearly see how it all works Get it here, I recommend opening it on a desktop https://seeing-theory.brown.edu/ 👉 @DataScienceM

40 202

📌 ChatGPT vs. Claude vs. Gemini for Data Analysis (Part 3): Best AI Assistant for Machine Learning 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-08-30 | ⏱️ Read time: 12 min read How AI can accelerate your ML projects from feature engineering to model training

40 202

📌 Decision Tree Classifier, Explained: A Visual Guide with Code Examples for Beginners 🗂 Category: MACHINE LEARNING 🕒 Date: 2024-08-30 | ⏱️ Read time: 11 min read A fresh look on our favorite upside-down tree

40 202

📌 Navigating the New Types of LLM Agents and Architectures 🗂 Category: 🕒 Date: 2024-08-30 | ⏱️ Read time: 12 min read My thanks to John Gilhuly for his contributions to this piece If 2023 was the…

40 202

📌 Targeting variants for maximum impact 🗂 Category: MACHINE LEARNING 🕒 Date: 2024-08-30 | ⏱️ Read time: 8 min read How to use causal inference to improve key business metrics Egor Kraev and Alexander Polyakov…

40 202

📌 The Ultimate Guide to Vision Transformers 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-08-30 | ⏱️ Read time: 8 min read A comprehensive guide to the Vision Transformer (ViT) that revolutionized computer vision.