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Perfect channel to learn Data Analytics, Data Sciene, Machine Learning & Artificial Intelligence Admin: @coderfun
Mostrar más📈 Análisis del canal de Telegram Machine Learning & Artificial Intelligence | Data Science Free Courses
El canal Machine Learning & Artificial Intelligence | Data Science Free Courses (@datasciencefree) en el segmento lingüístico de Inglés es un actor destacado. Actualmente la comunidad reúne a 66 652 suscriptores, ocupando la posición 2 465 en la categoría Educación y el puesto 432 en la región Malasia.
📊 Métricas de audiencia y dinámica
Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 66 652 suscriptores.
Según los últimos datos del 21 junio, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de 571, y en las últimas 24 horas de 2, conservando un alto alcance.
- Estado de verificación: No verificado
- Tasa de interacción (ER): El promedio de interacción de la audiencia es 0.92%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 0.79% de reacciones respecto al total de suscriptores.
- Alcance de las publicaciones: Cada publicación recibe en promedio 612 visualizaciones. En el primer día suele acumular 524 visualizaciones.
- Reacciones e interacción: La audiencia responde de forma activa: el promedio de reacciones por publicación es 4.
- Intereses temáticos: El contenido se centra en temas clave como sellerflash, waybienad, pricing, buybox, buyer.
📝 Descripción y política de contenido
El autor describe el recurso como un espacio para expresar opiniones subjetivas:
“Perfect channel to learn Data Analytics, Data Sciene, Machine Learning & Artificial Intelligence
Admin: @coderfun”
Gracias a la alta frecuencia de actualizaciones (últimos datos recibidos el 22 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 Educación.
66 652
Suscriptores
+224 horas
+417 días
+57130 días
Archivo de publicaciones
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4. What is overfitting and how can you prevent it?
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6. What is cross-validation, and why is it important in machine learning?
7. What are some feature selection techniques you are familiar with?
8.What are the assumptions of linear regression?
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10. Explain the difference between classification and regression.
11. What are some common algorithms used for dimensionality reduction?
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13. What are ensemble methods, and why are they useful?
14. How do you handle missing or corrupted data in a dataset?
15. What are the different kernels used in Support Vector Machines (SVM)?
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An high level overview for becoming a machine learning engineer
