Machine Learning & Artificial Intelligence | Data Science Free Courses
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 66 732 suscriptores, ocupando la posición 2 450 en la categoría Educación y el puesto 436 en la región Malasia.
📊 Métricas de audiencia y dinámica
Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 66 732 suscriptores.
Según los últimos datos del 24 junio, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de 534, y en las últimas 24 horas de 42, conservando un alto alcance.
- Estado de verificación: No verificado
- Tasa de interacción (ER): El promedio de interacción de la audiencia es 0.75%. 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 502 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 3.
- 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 25 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.
Carga de datos en curso...
| 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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| 17 | ✅ Data Science Mistakes Beginners Should Avoid ⚠️📉
1️⃣ Skipping the Basics
• Jumping into ML without Python, Stats, or Pandas
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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
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