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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 40 193 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 40 193 suscriptores.
Según los últimos datos del 01 julio, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de 355, y en las últimas 24 horas de 21, conservando un alto alcance.
- Estado de verificación: No verificado
- Tasa de interacción (ER): El promedio de interacción de la audiencia es 2.04%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 2.12% de reacciones respecto al total de suscriptores.
- Alcance de las publicaciones: Cada publicación recibe en promedio 818 visualizaciones. En el primer día suele acumular 851 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 02 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.
40 193
Suscriptores
+2124 horas
+857 días
+35530 días
Archivo de publicaciones
40 193
📌 Integrating LLM Agents with LangChain into VICA
🗂 Category:
🕒 Date: 2024-08-20 | ⏱️ Read time: 17 min read
Learn how we use LLM Agents to improve and customise transactions in a chatbot!
40 193
📌 How To Get A Data Science Graduate Scheme / Internship
🗂 Category: DATA SCIENCE
🕒 Date: 2024-08-20 | ⏱️ Read time: 8 min read
My advice for university and college students wanting to get into data science
40 193
📌 Plotly Dash — A Structured Framework for a Multi-Page Dashboard
🗂 Category: DATA VISUALIZATION
🕒 Date: 2025-10-06 | ⏱️ Read time: 12 min read
An easy starting point for larger and more complicated Dash dashboards
40 193
📌 How To Build Effective Technical Guardrails for AI Applications
🗂 Category: ARTIFICIAL INTELLIGENCE
🕒 Date: 2025-10-06 | ⏱️ Read time: 13 min read
Exploring the most practical guardrails to implement at ground level
40 193
📌 How I Used ChatGPT to Land My Next Data Science Role
🗂 Category: DATA SCIENCE
🕒 Date: 2025-10-06 | ⏱️ Read time: 9 min read
Practical AI hacks for every stage of the job search — with real prompts and examples
40 193
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40 193
📌 Hands-on Time Series Anomaly Detection using Autoencoders, with Python
🗂 Category: DATA SCIENCE
🕒 Date: 2024-08-21 | ⏱️ Read time: 12 min read
Here’s how to use Autoencoders to detect signals with anomalies in a few lines of…
40 193
📌 What Do Large Language Models “Understand”?
🗂 Category: ARTIFICIAL INTELLIGENCE
🕒 Date: 2024-08-21 | ⏱️ Read time: 31 min read
A deep dive on the meaning of understanding and how it applies to LLMs
40 193
📌 AWS DeepRacer : A Practical Guide to Reducing The Sim2Real Gap – Part 1
🗂 Category: ROBOTICS
🕒 Date: 2024-08-21 | ⏱️ Read time: 10 min read
In this guide (which also happens to be my first Medium article), I will share…
40 193
📌 3 AI Use Cases (That Are Not a Chatbot)
🗂 Category: MACHINE LEARNING
🕒 Date: 2024-08-21 | ⏱️ Read time: 7 min read
Feature engineering, structuring unstructured data, and lead scoring
40 193
📌 Creating a RAG Chatbot with Langflow and Astra DB
🗂 Category: NATURAL LANGUAGE PROCESSING
🕒 Date: 2024-08-21 | ⏱️ Read time: 7 min read
A walkthrough on how to create a RAG chatbot using Langflow’s intuitive interface, integrating LLMs…
40 193
📌 Fine-Tune the Audio Spectrogram Transformer With Transformers
🗂 Category: DATA SCIENCE
🕒 Date: 2024-08-21 | ⏱️ Read time: 15 min read
Learn how to fine-tune the Audio Spectrogram Transformer model for audio classification of your own…
40 193
📌 Understanding the Limitations of ARIMA Forecasting
🗂 Category: DATA SCIENCE
🕒 Date: 2024-08-21 | ⏱️ Read time: 13 min read
A comparison between the SARIMA model and the Facebook Prophet model
40 193
📌 How to Create Well-Styled Streamlit Dataframes, Part 2: using AgGrid
🗂 Category: DATA SCIENCE
🕒 Date: 2024-08-21 | ⏱️ Read time: 12 min read
The pandas Styler is cool. But AgGrid is way cooler. Make your Streamlit dataframes interactive…
40 193
📌 Leveraging Gemini-1.5-Pro-Latest for Smarter Eating
🗂 Category: ARTIFICIAL INTELLIGENCE
🕒 Date: 2024-08-21 | ⏱️ Read time: 9 min read
Learn how to use Google’s Gemini-1.5-pro-latest model to develop a generative AI app for calorie…
40 193
📌 The Forgotten Guiding Role of Data Modelling
🗂 Category: DATA ENGINEERING
🕒 Date: 2024-08-21 | ⏱️ Read time: 12 min read
Getting to the bottom of what structuring your data responsibly really means
40 193
📌 Linear Programming: The Stock Cutting Problem
🗂 Category: DATA SCIENCE
🕒 Date: 2024-08-22 | ⏱️ Read time: 13 min read
Part 2 – Linear Programming Example Deep Dive
40 193
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40 193
📌 Learning to Unlearn: Why Data Scientists and AI Practitioners Should Understand Machine Unlearning
🗂 Category: MACHINE LEARNING
🕒 Date: 2024-08-22 | ⏱️ Read time: 24 min read
Explore the intersections between privacy and AI with a guide to removing the impact of…
40 193
📌 SQL User Defined Functions (UDFs)
🗂 Category: DATA SCIENCE
🕒 Date: 2024-08-22 | ⏱️ Read time: 11 min read
A tutorial on mastering SQL UDFs: categories, use cases, and difference from stored procedures
