Data Science & Machine Learning
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Tarmoq:📈 Telegram kanali Data Science & Machine Learning analitikasi
Data Science & Machine Learning (@datasciencefun) Ingliz til segmentidagi kanali faol ishtirokchi. Hozirda hamjamiyat 77 281 obunachidan iborat bo'lib, Taʼlim toifasida 2 001-o'rinni va Hindiston mintaqasida 3 988-o'rinni egallagan.
📊 Auditoriya ko‘rsatkichlari va dinamika
невідомо sanasidan buyon loyiha tez o‘sib, 77 281 obunachiga ega bo‘ldi.
28 Avgust, 2026 dagi oxirgi ma’lumotlarga ko‘ra kanal barqaror faollikka ega. Oxirgi 30 kunda obunachilar soni 347 ga, so‘nggi 24 soatda esa 6 ga o‘zgardi va umumiy qamrov yuqori darajada qolmoqda.
- Tasdiqlash holati: Tasdiqlanmagan
- Jalb etish (ER): Auditoriya o‘rtacha 2.66% darajada jalb etiladi. Nashrdan keyingi dastlabki 24 soatda kontent odatda umumiy obunachilar sonining 1.12% ini tashkil etuvchi reaksiyalarni to‘playdi.
- Post qamrovi: Har bir post o‘rtacha 2 057 marta ko‘riladi; birinchi sutkada odatda 866 ta ko‘rish yig‘iladi.
- Reaksiyalar va o‘zaro ta’sir: Auditoriya faol: har bir postga o‘rtacha 3 ta reaksiya keladi.
- Tematik yo‘nalishlar: Kontent learning, accuracy, distribution, panda, dataset kabi asosiy mavzularga jamlangan.
📝 Tavsif va kontent siyosati
Muallif resursni shaxsiy fikrni ifoda etish maydoni sifatida ta’riflaydi:
“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”
Yuqori yangilanish chastotasi (oxirgi ma’lumot 29 Avgust, 2026 da olingan) sababli kanal doimo dolzarb va katta qamrovli bo‘lib qoladi. Analitika auditoriya kontent bilan faol hamkorlik qilishini, uni Taʼlim toifasidagi muhim ta’sir nuqtasiga aylantirishini ko‘rsatadi.
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Obunachilar
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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?
