Data Science & Machine Learning
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Join this channel to learn data science, artificial intelligence and machine learning with funny quizzes, interesting projects and amazing resources for free For collaborations: @love_data
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Red:📈 Análisis del canal de Telegram Data Science & Machine Learning
El canal Data Science & Machine Learning (@datasciencefun) en el segmento lingüístico de Inglés es un actor destacado. Actualmente la comunidad reúne a 77 281 suscriptores, ocupando la posición 2 001 en la categoría Educación y el puesto 3 988 en la región India.
📊 Métricas de audiencia y dinámica
Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 77 281 suscriptores.
Según los últimos datos del 28 agosto, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de 347, y en las últimas 24 horas de 6, conservando un alto alcance.
- Estado de verificación: No verificado
- Tasa de interacción (ER): El promedio de interacción de la audiencia es 2.66%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 1.12% de reacciones respecto al total de suscriptores.
- Alcance de las publicaciones: Cada publicación recibe en promedio 2 057 visualizaciones. En el primer día suele acumular 866 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 learning, accuracy, distribution, panda, dataset.
📝 Descripción y política de contenido
El autor describe el recurso como un espacio para expresar opiniones subjetivas:
“Join this channel to learn data science, artificial intelligence and machine learning with funny quizzes, interesting projects and amazing resources for free
For collaborations: @love_data”
Gracias a la alta frecuencia de actualizaciones (últimos datos recibidos el 29 agosto, 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.
77 281
Suscriptores
+624 horas
-107 días
+34730 días
Archivo de publicaciones
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✅ PCA (Principal Component Analysis) Basics 📉🤖
👉 PCA is a Dimensionality Reduction technique used to simplify large datasets while keeping important information.
🔹 1. What is Dimensionality Reduction?
👉 Reducing the number of features columns in data.
Example:
Instead of 100 features → reduce to 10 important features.
✔ Faster training
✔ Better visualization
✔ Reduced complexity
🔥 2. What is PCA?
PCA = Principal Component Analysis
👉 It transforms data into new components called:
✔ Principal Components
These components capture the maximum variance in data.
🔹 3. Why PCA is Important?
✔ Reduces high-dimensional data
✔ Improves model performance
✔ Helps avoid overfitting
✔ Useful for visualization
🔹 4. How PCA Works (Simple Idea)
1️⃣ Find directions with maximum variance
2️⃣ Create principal components
3️⃣ Keep most important components
4️⃣ Remove less useful information
🔹 5. Example
👉 Suppose dataset has:
• Height
• Weight
• BMI
• Body Fat
Many features may contain similar information.
PCA combines them into fewer components.
🔹 6. Important Terms ⭐
✔ Variance → Spread of data
✔ Principal Component → New feature
✔ Explained Variance → Information retained
🔹 7. Implementation (Python)
from sklearn.decomposition import PCA
import numpy as np
X = np.array([
[1,2],
[3,4],
[5,6]
])
pca = PCA(n_components=1)
X_pca = pca.fit_transform(X)
print(X_pca)
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Which of the following is a real-world application of K-Means?
Which method is commonly used to find the best value of K?
K-Means belongs to which type of Machine Learning?
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✅ Clustering with K-Means Algorithm 📊🤖
👉 K-Means is one of the most popular unsupervised learning algorithms. It groups similar data points into clusters.
🔹 1. What is Clustering?
Clustering = Grouping similar data together
👉 No labels are provided. The algorithm finds hidden patterns automatically.
Examples:
✔ Customer segmentation
✔ Grouping similar products
✔ Image compression
🔥 2. What is K-Means?
K-Means divides data into K clusters.
👉 Each cluster has a center called Centroid.
🔹 3. How K-Means Works
Step-by-step:
1️⃣ Choose number of clusters (K)
2️⃣ Select random centroids
3️⃣ Assign points to nearest centroid
4️⃣ Update centroid positions
5️⃣ Repeat until stable
🔹 4. Example
👉 Customer Segmentation
Customers are grouped based on:
✔ Age
✔ Income
✔ Spending habits
🔹 5. Implementation (Python)
from sklearn.cluster import KMeans
# Sample data
X = [[1], [2], [10], [11]]
model = KMeans(n_clusters=2)
model.fit(X)
print(model.labels_)
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Which kernel is commonly used in non-linear SVM?
