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 77 284 obunachidan iborat bo'lib, Taʼlim toifasida 1 999-o'rinni va Hindiston mintaqasida 3 968-o'rinni egallagan.
📊 Auditoriya ko‘rsatkichlari va dinamika
невідомо sanasidan buyon loyiha tez o‘sib, 77 284 obunachiga ega bo‘ldi.
29 Avgust, 2026 dagi oxirgi ma’lumotlarga ko‘ra kanal barqaror faollikka ega. Oxirgi 30 kunda obunachilar soni 327 ga, so‘nggi 24 soatda esa 2 ga o‘zgardi va umumiy qamrov yuqori darajada qolmoqda.
- Tasdiqlash holati: Tasdiqlanmagan
- Jalb etish (ER): Auditoriya o‘rtacha 2.71% darajada jalb etiladi. Nashrdan keyingi dastlabki 24 soatda kontent odatda umumiy obunachilar sonining 1.11% ini tashkil etuvchi reaksiyalarni to‘playdi.
- Post qamrovi: Har bir post o‘rtacha 2 091 marta ko‘riladi; birinchi sutkada odatda 857 ta ko‘rish yig‘iladi.
- Reaksiyalar va o‘zaro ta’sir: Auditoriya faol: har bir postga o‘rtacha 4 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 30 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.
77 284
Obunachilar
+224 soatlar
-207 kunlar
+32730 kunlar
Postlar arxiv
What type of problem does Linear Regression solve?
✅ Linear Regression Basics 📈🤖
👉 This is the most important and beginner-friendly algorithm in Machine Learning.
🔹 1. What is Linear Regression?
Linear Regression is used to predict a continuous value.
👉 Example:
✔ Predict salary
✔ Predict house price
✔ Predict sales
🔥 2. Basic Idea
👉 It finds a straight line that best fits the data.
Equation:
y = mx + c
Where:
✔ y → Output (target)
✔ x → Input (feature)
✔ m → Slope
✔ c → Intercept
🔹 3. Example
👉 Predict Salary based on Experience
Experience Salary
1 year 20k
2 years 30k
3 years 40k
👉 Model learns pattern → predicts future salary.
🔹 4. Simple Implementation (Python)
from sklearn.linear_model import LinearRegression
# Sample data
X = [[1], [2], [3]]
y = [20000, 30000, 40000]
model = LinearRegression()
model.fit(X, y)
# Prediction
print(model.predict([[4]]))
👉 Output: ∼50000 (approx)
🔹 5. Important Terms ⭐
✔ Feature (X) → Input
✔ Target (y) → Output
✔ Model → Learns relationship
✔ Prediction → Output from model
🔹 6. Assumptions of Linear Regression
✔ Linear relationship
✔ No extreme outliers
✔ Independent features
🔹 7. Why Linear Regression is Important?
✔ Easy to understand
✔ Used in real-world predictions
✔ Foundation for advanced ML
🎯 Today’s Goal
✔ Understand regression concept
✔ Learn equation (y = mx + c)
✔ Implement simple model
👉 Linear Regression = First step into ML modeling 🚀
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Which of the following is an example of supervised learning?
✅ Machine Learning Basics You Should Know 🤖📊
🔹 1. What is Machine Learning?
Machine Learning = Teaching computers to learn patterns from data without explicit programming
👉 Instead of rules → we give data → model learns patterns.
🔥 2. Types of Machine Learning
✅ 1. Supervised Learning ⭐
👉 Model learns from labeled data
Examples:
✔ Predict house price
✔ Email spam detection
Common Algorithms:
- Linear Regression
- Logistic Regression
- Decision Trees
✅ 2. Unsupervised Learning
👉 Model finds patterns in unlabeled data
Examples:
✔ Customer segmentation
✔ Grouping similar data
Common Algorithms:
- K-Means Clustering
- Hierarchical Clustering
✅ 3. Reinforcement Learning
👉 Model learns through rewards and penalties
Example:
✔ Game playing AI
🔹 3. ML Workflow (Very Important ⭐)
👉 Step-by-step process:
1️⃣ Collect Data
2️⃣ Clean Data
3️⃣ Perform EDA
4️⃣ Split Data (Train/Test)
5️⃣ Train Model
6️⃣ Evaluate Model
7️⃣ Deploy Model
🔹 4. Train-Test Split
from sklearn.model_selection import train_test_split
👉 Used to divide data into:
✔ Training data
✔ Testing data
🔹 5. Example (Simple ML Idea)
👉 Predict Salary based on Experience
Input → Experience
Output → Salary
🔹 6. Why ML is Important?
✔ Automates decision-making
✔ Used in AI, recommendations, predictions
✔ Core of modern tech
🎯 Today’s Goal
✔ Understand ML types
✔ Learn workflow
✔ Understand supervised vs unsupervised
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What is the probability of getting an even number when rolling a dice?
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✅ Probability Basics 🎯📊
👉 Probability is used to predict chances of events happening.
It is the foundation of Machine Learning AI.
🔹 1. What is Probability?
Probability is the chance of an event occurring.
✅ Formula
P(Event) = Favorable Outcomes / Total Outcomes
🔥 2. Basic Example
👉 Toss a coin
• Possible outcomes: {Head, Tail}
• P(Head) = 1/2 = 0.5
• P(Tail) = 1/2 = 0.5
🔹 3. Types of Events
✅ Independent Events
👉 One event does NOT affect another.
Example: Coin toss + Dice roll
✅ Dependent Events
👉 One event affects another.
Example: Picking cards without replacement
🔹 4. Important Probability Rules ⭐
✅ Addition Rule
When events are mutually exclusive:
P(A or B) = P(A) + P(B)
✅ Multiplication Rule
P(A and B) = P(A) × P(B) (for independent events)
🔹 5. Conditional Probability ⭐
👉 Probability of A given B
P(A|B) = P(A∩B)/P(B)
🔹 6. Real-Life Example
👉 Spam detection
• Probability that an email is spam based on words used.
🔹 7. Why Probability is Important?
✔ Used in ML algorithms (Naive Bayes)
✔ Helps in predictions
✔ Used in risk analysis
🎯 Today’s Goal
✔ Understand probability basics
✔ Learn formulas
✔ Solve simple problems
👉 Probability gives decision-making power in data science 🎯
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