Data Science & Machine Learning
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📈 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 77 621 مشترک است و جایگاه 2 017 را در دسته آموزش و رتبه 3 821 را در منطقه الهند دارد.
📊 شاخص‌های مخاطب و پویایی
از زمان ایجاد در невідомо، پروژه رشد سریعی داشته و 77 621 مشترک جذب کرده است.
بر اساس آخرین داده‌ها در تاریخ 13 نوامبر, 2026، کانال فعالیت پایداری دارد. در ۳۰ روز گذشته تغییر اعضا برابر -142 و در ۲۴ ساعت گذشته برابر -20 بوده و همچنان دسترسی گسترده‌ای حفظ شده است.
- وضعیت تأیید: تأیید نشده
- نرخ تعامل (ER): میانگین تعامل مخاطب 2.37% است و در ۲۴ ساعت نخست پس از انتشار، محتوا معمولاً 1.01% واکنش نسبت به کل مشترکان کسب می‌کند.
- دسترسی پست‌ها: هر پست به طور میانگین 1 843 بازدید دریافت می‌کند. در اولین روز معمولاً 781 بازدید جمع‌آوری می‌شود.
- واکنش‌ها و تعامل: مخاطبان به‌طور فعال حمایت می‌کنند؛ میانگین واکنش به هر پست 3 است.
- علایق موضوعی: محتوا بر موضوعات کلیدی مانند learning, accuracy, distribution, panda, dataset تمرکز دارد.
📝 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 14 نوامبر, 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
Quiz Explaination
Supervised Learning: All data is labeled and the algorithms learn to predict the output from the
input data
Unsupervised Learning: All data is unlabeled and the algorithms learn to inherent structure from
the input data.
Semi-supervised Learning: Some data is labeled but most of it is unlabeled and a mixture of
supervised and unsupervised techniques can be used to solve problem.
Unsupervised learning problems can be further grouped into clustering and association problems.
Clustering: A clustering problem is where you want to discover the inherent groupings
in the data, such as grouping customers by purchasing behavior.
Association: An association rule learning problem is where you want to discover rules
that describe large portions of your data, such as people that buy A also tend to buy B.
Which of the following is not a type of unsupervised Learning?
In which technique, data is unlabeled and the algorithms learn to inherent structure from the input data?
Master_Machine_Learning_Algorithms_Discover_how_they_work_by_Jason.pdf1.09 MB
What are the main parameters of the random forest model?
max_depth: Longest Path between root node and the leaf
min_sample_split: The minimum number of observations needed to split a given node
max_leaf_nodes: Conditions the splitting of the tree and hence, limits the growth of the trees
min_samples_leaf: minimum number of samples in the leaf node
n_estimators: Number of trees
max_sample: Fraction of original dataset given to any individual tree in the given model
max_features: Limits the maximum number of features provided to trees in random forest model
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What are the main parameters in the gradient boosting model?
There are many parameters, but below are a few key defaults.
learning_rate=0.1 (shrinkage).
n_estimators=100 (number of trees).
max_depth=3.
min_samples_split=2.
min_samples_leaf=1.
subsample=1.0.
Which of the following Python Library can be exclusively used to plot graphs?
How does L2 regularization look like in a linear model?
L2 regularization adds a penalty term to our cost function which is equal to the sum of squares of models coefficients multiplied by a lambda hyperparameter.
This technique makes sure that the coefficients are close to zero and is widely used in cases when we have a lot of features that might correlate with each other.
Which regularization techniques do you know?
There are mainly two types of regularization,
L1 Regularization (Lasso regularization) - Adds the sum of absolute values of the coefficients to the cost function.
L2 Regularization (Ridge regularization) - Adds the sum of squares of coefficients to the cost function
Here, Lambda determines the amount of regularization.
Machine Learning for Everyone in some words
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Everything you need to know about TensorFlow 2.0
Keras-APIs, SavedModels, TensorBoard, Keras-Tuner and more.
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JULY75What happens to our linear regression model if we have three columns in our data: x, y, z — and z is a sum of x and y?
We would not be able to perform the regression. Because z is linearly dependent on x and y so when performing the regression would be a singular (not invertible) matrix.
What’s the difference between random forest and gradient boosting?
Random Forests builds each tree independently while Gradient Boosting builds one tree at a time.
Random Forests combine results at the end of the process (by averaging or "majority rules") while Gradient Boosting combines results along the way.
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What do we do with categorical variables?
Categorical variables must be encoded before they can be used as features to train a machine learning model. There are various encoding techniques, including:
One-hot encoding
Label encoding
Ordinal encoding
Target encoding
