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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
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Red:📈 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 40 265 suscriptores, ocupando la posición 3 343 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 40 265 suscriptores.
Según los últimos datos del 06 julio, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de 336, y en las últimas 24 horas de -4, conservando un alto alcance.
- Estado de verificación: No verificado
- Tasa de interacción (ER): El promedio de interacción de la audiencia es 2.25%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 1.88% de reacciones respecto al total de suscriptores.
- Alcance de las publicaciones: Cada publicación recibe en promedio 906 visualizaciones. En el primer día suele acumular 758 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 07 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.
40 265
Suscriptores
-424 horas
+917 días
+33630 días
Archivo de publicaciones
40 265
📌 Introducing n-Step Temporal-Difference Methods
🗂 Category: MACHINE LEARNING
🕒 Date: 2024-12-29 | ⏱️ Read time: 10 min read
Dissecting “Reinforcement Learning” by Richard S. Sutton with custom Python implementations, Episode V
40 265
📌 I Combined the Blockchain and AI to Generate Art. Here’s What Happened Next.
🗂 Category: BLOCKCHAIN
🕒 Date: 2024-12-30 | ⏱️ Read time: 8 min read
Using LLMs to create artistic representations of data
40 265
📌 How to Build a Graph RAG App
🗂 Category:
🕒 Date: 2024-12-30 | ⏱️ Read time: 30 min read
The accompanying code for the app and notebook are here. Knowledge graphs (KGs) and Large Language…
40 265
📌 How to Build a Resume Optimizer with AI
🗂 Category:
🕒 Date: 2024-12-30 | ⏱️ Read time: 7 min read
Step-by-step guide with example Python code
40 265
📌 Mastering Model Uncertainty: Thresholding Techniques in Deep Learning
🗂 Category: DATA SCIENCE
🕒 Date: 2024-12-30 | ⏱️ Read time: 7 min read
A few words on thresholding, the softmax activation function, introducing an extra label, and considerations…
40 265
📌 From Default Python Line Chart to Journal-Quality Infographics
🗂 Category: ANALYTICS
🕒 Date: 2024-12-30 | ⏱️ Read time: 3 min read
Transform boring default Matplotlib line charts into stunning, customized visualizations
40 265
📌 The Key to Smarter Models: Tracking Feature Histories
🗂 Category: ARTIFICIAL INTELLIGENCE
🕒 Date: 2024-12-31 | ⏱️ Read time: 10 min read
Capture context and improve predictions with historical data
40 265
📌 The Math Behind In-Context Learning
🗂 Category: LARGE LANGUAGE MODELS
🕒 Date: 2024-12-31 | ⏱️ Read time: 6 min read
From attention to gradient descent: unraveling how transformers learn from examples
40 265
📌 Creating SMOTE Oversampling from Scratch
🗂 Category: DATA SCIENCE
🕒 Date: 2024-12-31 | ⏱️ Read time: 8 min read
A Python tutorial on how to implement oversampling and how to make custom variations
40 265
📌 Top 12 Skills Data Scientists Need to Succeed in 2025
🗂 Category: CAREER ADVICE
🕒 Date: 2024-12-31 | ⏱️ Read time: 27 min read
It’s (not) all about LLMs and AI tools
40 265
📌 Multi-Agentic RAG with Hugging Face Code Agents
🗂 Category:
🕒 Date: 2024-12-31 | ⏱️ Read time: 80 min read
Using Qwen2.5-7B-Instruct powered code agents to create a local, open source, multi-agentic RAG system
40 265
📌 Chi-Squared Test: Comparing Variations Through Soccer
🗂 Category: DATA SCIENCE
🕒 Date: 2025-01-01 | ⏱️ Read time: 13 min read
Understanding Different Types of Chi-Squared Tests: A/B Testing for Data Science Series (8)
40 265
📌 Transforming Data into Solutions: Building a Smart App with Python and AI
🗂 Category: ARTIFICIAL INTELLIGENCE
🕒 Date: 2025-01-01 | ⏱️ Read time: 13 min read
Some financial analysts worry that artificial intelligence may not justify the massive investments being made…
40 265
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40 265
📌 Mastering Sensor Fusion: Color Image Obstacle Detection with KITTI Data – Part 2
🗂 Category: DEEP LEARNING
🕒 Date: 2025-01-01 | ⏱️ Read time: 26 min read
How to use Color Image data for object detection in the context of obstacle detection
40 265
📌 Scaling Statistics: Incremental Standard Deviation in SQL with dbt
🗂 Category: DATA SCIENCE
🕒 Date: 2025-01-01 | ⏱️ Read time: 7 min read
Why scan yesterday’s data when you can increment today’s?
40 265
📌 AI-Powered Information Extraction and Matchmaking
🗂 Category: ARTIFICIAL INTELLIGENCE
🕒 Date: 2025-01-01 | ⏱️ Read time: 34 min read
Developing an application for extracting key profile information from CVs and recommending jobs aligned with…
40 265
📌 Mastering the Basics: How Linear Regression Unlocks the Secrets of Complex Models
🗂 Category: ARTIFICIAL INTELLIGENCE
🕒 Date: 2025-01-01 | ⏱️ Read time: 12 min read
Full explanation on Linear Regression and how it learns
40 265
📌 5 Simple Projects to Start Today: A Learning Roadmap for Data Engineering
🗂 Category: DATA ENGINEERING
🕒 Date: 2025-01-02 | ⏱️ Read time: 11 min read
Start with 5 practical projects to lay the foundation for your data engineering roadmap.
40 265
📌 How to Process 10k Images in Seconds
🗂 Category: DATA SCIENCE
🕒 Date: 2025-01-02 | ⏱️ Read time: 7 min read
Efficient image operations with multiprocessing in Python
