Machine Learning with Python
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Learn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers. Admin: @HusseinSheikho || @Hussein_Sheikho
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📈 Análisis del canal de Telegram Machine Learning with Python
El canal Machine Learning with Python (@codeprogrammer) en el segmento lingüístico de Inglés es un actor destacado. Actualmente la comunidad reúne a 68 475 suscriptores, ocupando la posición 2 406 en la categoría Educación y el puesto 4 586 en la región India.
📊 Métricas de audiencia y dinámica
Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 68 475 suscriptores.
Según los últimos datos del 03 noviembre, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de -74, y en las últimas 24 horas de 12, conservando un alto alcance.
- Estado de verificación: No verificado
- Tasa de interacción (ER): El promedio de interacción de la audiencia es 4.07%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 1.55% de reacciones respecto al total de suscriptores.
- Alcance de las publicaciones: Cada publicación recibe en promedio 2 789 visualizaciones. En el primer día suele acumular 1 062 visualizaciones.
- Reacciones e interacción: La audiencia responde de forma activa: el promedio de reacciones por publicación es 8.
- Intereses temáticos: El contenido se centra en temas clave como insidead, learning, degree, evaluation, algorithm.
📝 Descripción y política de contenido
El autor describe el recurso como un espacio para expresar opiniones subjetivas:
“Learn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers.
Admin: @HusseinSheikho || @Hussein_Sheikho”
Gracias a la alta frecuencia de actualizaciones (últimos datos recibidos el 04 noviembre, 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 Educación.
68 475
Suscriptores
+1224 horas
-827 días
-7430 días
Archivo de publicaciones
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☄️ JupyterLab Templates plugin
👨🏻💻 If you use JupyterLab a lot, you probably create a new notebook from scratch every time you start a data analysis project. But with the JupyterLab Templates plugin , there is no need to do this anymore. This tool allows you to create different templates and use them whenever you need.
💰 For example, suppose you have a similar structure for each data analysis or EDA project. Using JupyterLab Templates, you can create this structure once and then use that template every time you want to start a new project, without having to start all over again!
⏪ Installing and using this plugin is very simple.👇
pip install jupyterlab_templates
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✅ 5 of the best Kaggle datasets
💸 For data science projects (in finance)
👨🏻💻 If you are looking for datasets to do financial projects, the datasets presented on the Kaggle site can be a great option.
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1️⃣ S&P 500 stock dataset (daily update)
📎 Link: S&P 500 Stocks
2️⃣ Database of loans and debts
📎 Link: Loans & Liability
3️⃣ Dataset of frequent use of credit card
📎 Link: Credit Card Spending Habits
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📎 Link: Company Bankruptcy Prediction
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📎 Link: Credit score classification
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