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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
Mostrar más📈 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
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
40 150
📌 How to Classify Lung Cancer Subtype from DNA Copy Numbers Using PyTorch
🗂 Category: DEEP LEARNING
🕒 Date: 2025-10-17 | ⏱️ Read time: 12 min read
A step-by-step introduction to understanding cancer from the perspective of a data scientist.
40 150
📌 Machine Learning Meets Panel Data: What Practitioners Need to Know
🗂 Category: MACHINE LEARNING
🕒 Date: 2025-10-17 | ⏱️ Read time: 8 min read
How to avoid overestimating machine learning models’ performance, usefulness, and real-world applicability due to hidden…
40 150
🤖🧠 Sora: OpenAI’s Breakthrough Text-to-Video Model Transforming Visual Creativity
🗓️ 18 Oct 2025
📚 AI News & Trends
Introduction Artificial Intelligence (AI) is rapidly transforming the creative world. From generating realistic images to composing music and writing code, AI has redefined how humans interact with technology. But one of the most revolutionary advancements in this domain is Sora, OpenAI’s text-to-video generative model that converts written prompts into hyper-realistic video clips. Ithas captured global ...
#Sora #OpenAI #TextToVideo #AI #VisualCreativity #GenerativeModel
40 150
🤖🧠 Sora: OpenAI’s Breakthrough Text-to-Video Model Transforming Visual Creativity
🗓️ 18 Oct 2025
📚 AI News & Trends
Introduction Artificial Intelligence (AI) is rapidly transforming the creative world. From generating realistic images to composing music and writing code, AI has redefined how humans interact with technology. But one of the most revolutionary advancements in this domain is Sora, OpenAI’s text-to-video generative model that converts written prompts into hyper-realistic video clips. Ithas captured global ...
#Sora #OpenAI #TextToVideo #AI #VisualCreativity #GenerativeModel
40 150
📌 Learnings from a Machine Learning Engineer — Part 2: The Data Sets
🗂 Category: MACHINE LEARNING
🕒 Date: 2025-02-13 | ⏱️ Read time: 10 min read
In Part 1, we discussed the importance of collecting good image data and assigning proper labels…
40 150
📌 Learnings from a Machine Learning Engineer — Part 4: The Model
🗂 Category: MACHINE LEARNING
🕒 Date: 2025-02-13 | ⏱️ Read time: 8 min read
In this latest part of my series, I will share what I have learned on…
40 150
📌 Learnings from a Machine Learning Engineer — Part 1: The Data
🗂 Category: MACHINE LEARNING
🕒 Date: 2025-02-13 | ⏱️ Read time: 13 min read
It is said that in order for a machine learning model to be successful, you…
40 150
📌 Learnings from a Machine Learning Engineer — Part 3: The Evaluation
🗂 Category: MACHINE LEARNING
🕒 Date: 2025-02-13 | ⏱️ Read time: 13 min read
In this third part of my series, I will explore the evaluation process which is…
40 150
📌 Learnings from a Machine Learning Engineer — Part 5: The Training
🗂 Category: MACHINE LEARNING
🕒 Date: 2025-02-13 | ⏱️ Read time: 16 min read
In this fifth part of my series, I will outline the steps for creating a…
40 150
📌 Building a Data Engineering Center of Excellence
🗂 Category: DATA ENGINEERING
🕒 Date: 2025-02-13 | ⏱️ Read time: 11 min read
As data continues to grow in importance and become more complex, the need for skilled…
40 150
Repost from Machine Learning with Python
AI Engineering roadmap that beginners can actually follow. Everything is based on 100% free, open-source, and community resources
All resources can be found here: GitHub
👉 @codeprogrammer
40 150
“I was blocked on Telegram at 2 AM and thought it was over. But then I found THIS.”
Everyone’s missing out on the one-click PROXY that just saved my chats. See the link — unlock Telegram when you need it most!
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40 150
📌 Analyzing Lake Mendota Ice Phenology with Python
🗂 Category: CLIMATE CHANGE
🕒 Date: 2024-06-10 | ⏱️ Read time: 10 min read
An analysis-ready dataset you can use right now
40 150
📌 Task-Aware RAG Strategies for When Sentence Similarity Fails
🗂 Category: DATA SCIENCE
🕒 Date: 2024-06-10 | ⏱️ Read time: 19 min read
When retrieval is based on an additional metric besides just sentence similarity, traditional methods perform…
40 150
🤖🧠 Agentic Entropy-Balanced Policy Optimization (AEPO): Balancing Exploration and Stability in Reinforcement Learning for Web Agents
🗓️ 17 Oct 2025
📚 AI News & Trends
AEPO (Agentic Entropy-Balanced Policy Optimization) represents a major advancement in the evolution of Agentic Reinforcement Learning (RL). As large language models (LLMs) increasingly act as autonomous web agents – searching, reasoning and interacting with tools – the need for balanced exploration and stability has become crucial. Traditional RL methods often rely heavily on entropy to ...
#AgenticRL #ReinforcementLearning #LLMs #WebAgents #EntropyBalanced #PolicyOptimization
40 150
📌 Spatial Index: Space-Filling Curves
🗂 Category: DATABASE DESIGN
🕒 Date: 2024-06-11 | ⏱️ Read time: 11 min read
Spatial Index and Space Filling Curves for Multi-dimensional data
40 150
📌 Reinforcement Learning, Part 4: Monte Carlo Control
🗂 Category: DATA SCIENCE
🕒 Date: 2024-06-11 | ⏱️ Read time: 17 min read
Harnessing Monte Carlo algorithms to discover the best strategies
40 150
📌 Anatomy of Windows Functions
🗂 Category: DATA ENGINEERING
🕒 Date: 2024-06-11 | ⏱️ Read time: 15 min read
Theory and practice of an underappreciated SQL operation
40 150
📌 SQL Explained: Ranking Analytics
🗂 Category: DATA ENGINEERING
🕒 Date: 2024-06-11 | ⏱️ Read time: 11 min read
What they are and how you use them
