اکنون در دسترس! پژوهش تلگرام ۲۰۲۵ — مهمترین بینشهای سال 
Data Science & Machine Learning
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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 75 504 مشترک است و جایگاه 2 124 را در دسته آموزش و رتبه 4 396 را در منطقه الهند دارد.
📊 شاخصهای مخاطب و پویایی
از زمان ایجاد در невідомо، پروژه رشد سریعی داشته و 75 504 مشترک جذب کرده است.
بر اساس آخرین دادهها در تاریخ 05 ژوئن, 2026، کانال فعالیت پایداری دارد. در ۳۰ روز گذشته تغییر اعضا برابر 982 و در ۲۴ ساعت گذشته برابر 36 بوده و همچنان دسترسی گستردهای حفظ شده است.
- وضعیت تأیید: تأیید نشده
- نرخ تعامل (ER): میانگین تعامل مخاطب 3.44% است و در ۲۴ ساعت نخست پس از انتشار، محتوا معمولاً 1.40% واکنش نسبت به کل مشترکان کسب میکند.
- دسترسی پستها: هر پست به طور میانگین 2 593 بازدید دریافت میکند. در اولین روز معمولاً 1 057 بازدید جمعآوری میشود.
- واکنشها و تعامل: مخاطبان بهطور فعال حمایت میکنند؛ میانگین واکنش به هر پست 4 است.
- علایق موضوعی: محتوا بر موضوعات کلیدی مانند learning, accuracy, distribution, panda, dataset تمرکز دارد.
📝 توضیح و سیاست محتوایی
نویسنده این فضا را محل بیان دیدگاههای شخصی توصیف میکند:
“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 07 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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آرشیو پست ها
Data Analyst vs Data Scientist vs Business Analyst vs ML Engineer vs Gen AI Engineer
Which of the following is a hyperparameter in KNN?
Which method is commonly used for Hyperparameter Tuning?
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✅ Cross Validation & Hyperparameter Tuning 🤖⚙️
👉 Building a model is not enough.
We must also make sure it performs well on unseen data.
This is done using:
✔ Cross Validation
✔ Hyperparameter Tuning
🔹 1. What is Cross Validation?
Cross Validation checks how well a model generalizes to new data.
👉 Instead of using only one train-test split, data is divided multiple times.
🔥 2. K-Fold Cross Validation ⭐
How it Works:
1️⃣ Split data into K parts (folds)
2️⃣ Use one fold for testing
3️⃣ Use remaining folds for training
4️⃣ Repeat until every fold is tested
✅ Example
If K = 5:
• 4 folds → Training
• 1 fold → Testing
Repeated 5 times.
🔹 3. Why Cross Validation is Important?
✔ Better model evaluation
✔ Reduces overfitting risk
✔ More reliable accuracy
🔹 4. Implementation (Python)
from sklearn.model_selection import cross_val_score
from sklearn.linear_model import LogisticRegression
model = LogisticRegression()
scores = cross_val_score(model, X, y, cv=5)
print(scores)
from sklearn.model_selection import GridSearchCV
params = {
"n_neighbors": [3,5,7]
}
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Which of the following can help reduce overfitting?
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✅ Overfitting vs Underfitting 🤖📉
👉 One of the most important concepts in Machine Learning.
A model should not:
❌ Learn too little
❌ Learn too much
It should learn just right ✅
🔹 1. What is Underfitting?
👉 Underfitting happens when the model is too simple and cannot learn patterns properly.
Characteristics:
❌ Poor performance on training data
❌ Poor performance on testing data
✅ Example
Trying to fit a straight line to highly complex data.
🔥 2. What is Overfitting?
👉 Overfitting happens when the model memorizes training data instead of learning general patterns.
Characteristics:
✔ Very high training accuracy
❌ Poor testing accuracy
✅ Example
A student memorizes answers instead of understanding concepts.
🔹 3. Ideal Model (Best Case) ⭐
👉 Performs well on:
✔ Training data
✔ Testing data
This is called: ✅ Good Generalization
🔹 4. Visual Understanding
📉 Underfitting → Too simple
📈 Overfitting → Too complex
✅ Balanced model → Best fit
🔹 5. Causes of Overfitting
✔ Too much model complexity
✔ Small dataset
✔ Too many features
🔹 6. How to Reduce Overfitting ⭐
✔ More training data
✔ Feature selection
✔ Cross-validation
✔ Regularization
✔ Simpler model
🔹 7. How to Reduce Underfitting
✔ Use better features
✔ Increase model complexity
✔ Train longer
🔹 8. Why This is Important?
✔ Critical interview topic
✔ Improves model performance
✔ Core ML concept
🎯 Today’s Goal
✔ Understand overfitting
✔ Understand underfitting
✔ Learn solutions
💬 Tap ❤️ for more!
In a good regression model, the R² score should be:
