اکنون در دسترس! پژوهش تلگرام ۲۰۲۵ — مهمترین بینشهای سال 
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
نمایش بیشتر📈 تحلیل کانال تلگرام Data Science & Machine Learning
کانال Data Science & Machine Learning (@datasciencefun) در بخش زبانی انگلیسی بازیگری فعال است. در حال حاضر جامعه شامل 75 624 مشترک است و جایگاه 2 119 را در دسته آموزش و رتبه 4 357 را در منطقه الهند دارد.
📊 شاخصهای مخاطب و پویایی
از زمان ایجاد در невідомо، پروژه رشد سریعی داشته و 75 624 مشترک جذب کرده است.
بر اساس آخرین دادهها در تاریخ 10 ژوئن, 2026، کانال فعالیت پایداری دارد. در ۳۰ روز گذشته تغییر اعضا برابر 922 و در ۲۴ ساعت گذشته برابر 33 بوده و همچنان دسترسی گستردهای حفظ شده است.
- وضعیت تأیید: تأیید نشده
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📝 توضیح و سیاست محتوایی
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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”
به لطف بهروزرسانیهای پرتکرار (آخرین داده در تاریخ 11 ژوئن, 2026)، کانال همواره بهروز و دارای دسترسی بالاست. تحلیلها نشان میدهد مخاطبان بهطور فعال با محتوا تعامل دارند و آن را به نقطه اثرگذاری مهم در دسته آموزش تبدیل کردهاند.
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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
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✔ 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 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
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What type of distribution is symmetric and bell-shaped?
