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Machine Learning & Artificial Intelligence | Data Science Free Courses

Machine Learning & Artificial Intelligence | Data Science Free Courses

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Perfect channel to learn Data Analytics, Data Sciene, Machine Learning & Artificial Intelligence Admin: @coderfun

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📈 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 66 723 suscriptores, ocupando la posición 2 466 en la categoría Educación y el puesto 435 en la región Malasia.

📊 Métricas de audiencia y dinámica

Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 66 723 suscriptores.

Según los últimos datos del 23 junio, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de 495, y en las últimas 24 horas de 27, conservando un alto alcance.

  • Estado de verificación: No verificado
  • Tasa de interacción (ER): El promedio de interacción de la audiencia es 0.86%. 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 571 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 24 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.

66 723
Suscriptores
+2724 horas
+207 días
+49530 días
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junio '26
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+702
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+5 260
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noviembre '23
+370
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+302
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+528
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agosto '23
+900
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+846
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junio '23
+21
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mayo '23
+27
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+28
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+19
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+24
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enero '23
+30
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diciembre '22
+44
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noviembre '22
+123
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octubre '22
+68
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septiembre '22
+121
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agosto '22
+42
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julio '22
+75
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junio '22
+40
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mayo '22
+224
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abril '22
+182
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marzo '22
+94
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febrero '22
+285
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enero '22
+42
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diciembre '21
+11
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noviembre '21
+11
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octubre '21
+174
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septiembre '21
+398
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Fecha
Crecimiento de Suscriptores
Menciones
Canales
24 junio+50
23 junio+37
22 junio+11
21 junio+16
20 junio+17
19 junio+10
18 junio+22
17 junio+9
16 junio+26
15 junio+34
14 junio+55
13 junio+52
12 junio+34
11 junio+20
10 junio+28
09 junio+25
08 junio+23
07 junio+29
06 junio+25
05 junio+26
04 junio+36
03 junio+37
02 junio+37
01 junio+29
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✅ Data Science Mistakes Beginners Should Avoid ⚠️📉 1️⃣ Skipping the Basics • Jumping into ML without Python, Stats, or Pandas ✅ Build strong foundations in math, programming & EDA first 2️⃣ Not Understanding the Problem • Applying models blindly • Irrelevant features and metrics ✅ Always clarify business goals before coding 3️⃣ Treating Data Cleaning as Optional • Training on dirty/incomplete data ✅ Spend time on preprocessing — it’s 70% of real work 4️⃣ Using Complex Models Too Early • Overfitting small datasets • Ignoring simpler, interpretable models ✅ Start with baseline models (Logistic Regression, Decision Trees) 5️⃣ No Evaluation Strategy • Relying only on accuracy ✅ Use proper metrics (F1, AUC, MAE) based on problem type 6️⃣ Not Visualizing Data • Missed outliers and patterns ✅ Use Seaborn, Matplotlib, Plotly for EDA 7️⃣ Poor Feature Engineering • Feeding raw data into models ✅ Create meaningful features that boost performance 8️⃣ Ignoring Domain Knowledge • Features don’t align with real-world logic ✅ Talk to stakeholders or do research before modeling 9️⃣ No Practice with Real Datasets • Kaggle-only learning ✅ Work with messy, real-world data (open data portals, APIs) 🔟 Not Documenting or Sharing Work • No GitHub, no portfolio ✅ Document notebooks, write blogs, push projects online 💬 Tap ❤️ for more!
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