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

40 145

📌 How Many Pokemon Fit? 🗂 Category: DATA SCIENCE 🕒 Date: 2024-06-12 | ⏱️ Read time: 10 min read Finding the best Pokemon team by modeling and solving a knapsack problem with PokeAPI and…

40 145

📌 Time Series Regression and Cross-Validation: A Tidy Approach 🗂 Category: DATA SCIENCE 🕒 Date: 2024-06-12 | ⏱️ Read time: 8 min read Step by step guide to EDA, feature engineering, cross validation and model comparison with tidymodels,…

40 145

📌 A Python Engineer’s Introduction to 3D Gaussian Splatting (Part 2) 🗂 Category: MACHINE LEARNING 🕒 Date: 2024-06-13 | ⏱️ Read time: 8 min read Understanding and coding how Gaussian’s are used within 3D Gaussian Splatting

40 145

📌 AI Agent Unit Testing in Langfuse 🗂 Category: DATA SCIENCE 🕒 Date: 2024-06-13 | ⏱️ Read time: 10 min read Creating a scalable testing solution for AI agents for operation by non-coders

40 145

📌 My Easy Guide to Pre vs. Post Treatment Tests 🗂 Category: DATA SCIENCE 🕒 Date: 2024-06-13 | ⏱️ Read time: 13 min read A quick introduction to Before and After Tests with code.

40 145

📌 Sparse Autoencoders, Additive Decision Trees, and Other Emerging Topics in AI Interpretability 🗂 Category: DATA SCIENCE 🕒 Date: 2024-06-13 | ⏱️ Read time: 4 min read Our weekly selection of must-read Editors’ Picks and original features

40 145

📌 Take a Look Under the hood 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2024-06-13 | ⏱️ Read time: 13 min read Using Monosemanticity to understand the concepts a Large Language Model learned

40 145

📌 Improving Business Performance with Machine Learning 🗂 Category: MACHINE LEARNING 🕒 Date: 2024-06-13 | ⏱️ Read time: 18 min read Whether you are a data scientist, analyst, or business analyst, your goal is to deliver…

40 145

I was shocked how easy it is: I connected my signals,…and trades started happening. Nobody told me you could earn passively like that! This is the automation traders are hiding — it just works. Curious how? 👉 See the real tool in action #ad InsideAds

40 145

📌 Beyond AlphaFold: The Future Of LLM in Medicine 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-06-13 | ⏱️ Read time: 17 min read AlphaFold leaves a complex legacy: What will be the future of LLM in biology and…

40 145

📌 How I’d Become a Data Scientist (If I Had to Start Over) 🗂 Category: DATA SCIENCE 🕒 Date: 2024-06-14 | ⏱️ Read time: 12 min read Roadmap and tips on how to land a job in data science

40 145

📌 CUDA for AI – Intuitively and Exhaustively Explained 🗂 Category: MACHINE LEARNING 🕒 Date: 2024-06-14 | ⏱️ Read time: 58 min read Parallelized AI from scratch in CUDA

40 145

📌 Mapping the Pokemon World: A Network Analysis of Habitat-Based Encounters 🗂 Category: DATA SCIENCE 🕒 Date: 2024-06-14 | ⏱️ Read time: 19 min read An introduction to Network Analysis in Python, along with a practical example using Pokemon data…

40 145

📌 Understanding Buffer of Thoughts (BoT) – Reasoning with Large Language Models 🗂 Category: DATA SCIENCE 🕒 Date: 2024-06-14 | ⏱️ Read time: 12 min read New prompt engineering tool for complex reasoning, compared with Chain of thought (CoT) and Tree…

40 145

📌 Gated Recurrent Units (GRU) – Improving RNNs 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-06-15 | ⏱️ Read time: 11 min read Explaining how Gated Recurrent Neural Networks work

40 145

📌 Graph Visualization: 7 Steps from Easy to Advanced 🗂 Category: DATA SCIENCE 🕒 Date: 2024-06-15 | ⏱️ Read time: 10 min read Making visualization with Python, NetworkX, and D3.JS

40 145

📌 GPT from Scratch with MLX 🗂 Category: DEEP LEARNING 🕒 Date: 2024-06-15 | ⏱️ Read time: 36 min read Define and train GPT-2 on your MacBook

40 145

📌 Erasing Clouds from Satellite Imagery Using GANs (Generative Adversarial Networks) 🗂 Category: DEEP LEARNING 🕒 Date: 2024-06-15 | ⏱️ Read time: 12 min read Building GANs from scratch in python

40 145

📌 Simple Model Retraining Automation via GitHub Actions 🗂 Category: EDUCATION 🕒 Date: 2024-06-15 | ⏱️ Read time: 13 min read Easily streamline your modelling process with the GitHub Actions.

40 145

📌 Analyzing Unstructured PDF Data w/ Embedding Models and LLMs 🗂 Category: 🕒 Date: 2024-06-15 | ⏱️ Read time: 8 min read How to turn PDFs into actionable insights