متاح الآن! بحث تيليغرام 2025 — أهم رؤى العام 
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
إظهار المزيد📈 نظرة تحليلية على قناة تيليجرام Data Science & Machine Learning
تُعد قناة Data Science & Machine Learning (@datasciencefun) في القطاع اللغوي الإنكليزية لاعباً نشطاً. يضم المجتمع حالياً 75 860 مشتركاً، محتلاً المرتبة 2 107 في فئة التعليم والمرتبة 4 219 في منطقة الهند.
📊 مؤشرات الجمهور والحراك
منذ تأسيسه في невідомо، حقق المشروع نمواً سريعاً وجمع 75 860 مشتركاً.
بحسب آخر البيانات بتاريخ 22 يونيو, 2026، تحافظ القناة على نشاط مستقر. خلال آخر 30 يوماً تغيّر عدد الأعضاء بمقدار 728، وفي آخر 24 ساعة بمقدار -2، مع بقاء الوصول العام مرتفعاً.
- حالة التحقق: غير موثّقة
- معدل التفاعل (ER): يبلغ متوسط تفاعل الجمهور 3.00%. وخلال أول 24 ساعة من النشر يحصد المحتوى عادةً 1.05% من ردود الفعل نسبةً إلى إجمالي المشتركين.
- وصول المنشورات: يحصل كل منشور على متوسط 2 278 مشاهدة. وخلال اليوم الأول يجمع عادةً 794 مشاهدة.
- التفاعلات والاستجابة: يتفاعل الجمهور بانتظام؛ متوسط التفاعلات لكل منشور يبلغ 3.
- الاهتمامات الموضوعية: يركز المحتوى على مواضيع رئيسية مثل learning, accuracy, distribution, panda, dataset.
📝 الوصف وسياسة المحتوى
يصف المؤلف القناة بأنها مساحة للتعبير عن الآراء الذاتية:
“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”
بفضل وتيرة التحديث المرتفعة (أحدث البيانات بتاريخ 23 يونيو, 2026) تحافظ القناة على حداثتها ومستوى وصول مرتفع. وتُظهر التحليلات تفاعلاً نشطاً من الجمهور، ما يجعلها نقطة تأثير مهمة ضمن فئة التعليم.
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Is accuracy always a good metric?
Accuracy is not a good performance metric when there is imbalance in the dataset. For example, in binary classification with 95% of A class and 5% of B class, a constant prediction of A class would have an accuracy of 95%. In case of imbalance dataset, we need to choose Precision, recall, or F1 Score depending on the problem we are trying to solve.
What are precision, recall, and F1-score?
Precision and recall are classification evaluation metrics:
P = TP / (TP + FP) and R = TP / (TP + FN).
Where TP is true positives, FP is false positives and FN is false negatives
In both cases the score of 1 is the best: we get no false positives or false negatives and only true positives.
F1 is a combination of both precision and recall in one score (harmonic mean):
F1 = 2 * PR / (P + R).
Max F score is 1 and min is 0, with 1 being the best.
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How do we evaluate classification models?
Depending on the classification problem, we can use the following evaluation metrics:
Accuracy
Precision
Recall
F1 Score
Logistic loss (also known as Cross-entropy loss)
Jaccard similarity coefficient score
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What is sigmoid? What does it do?
A sigmoid function is a type of activation function, and more specifically defined as a squashing function. Squashing functions limit the output to a range between 0 and 1, making these functions useful in the prediction of probabilities.
Sigmod(x) = 1/(1+e^{-x})
What is overfitting?
When your model perform very well on your training set but can't generalize the test set, because it adjusted a lot to the training set.
What is the bias-variance trade-off?
• Bias is the error introduced by approximating the true underlying function, which can be quite complex, by a simpler model. Variance is a model sensitivity to changes in the training dataset.
• Bias-variance trade-off is a relationship between the expected test error and the variance and the bias - both contribute to the level of the test error and ideally should be as small as possible:
ExpectedTestError = Variance + Bias² + IrreducibleError
• But as a model complexity increases, the bias decreases and the variance increases which leads to overfitting. And vice versa, model simplification helps to decrease the variance but it increases the bias which leads to underfitting.
Can you explain how cross-validation works?
Cross-validation is the process to separate your total training set into two subsets: training and validation set, and evaluate your model to choose the hyperparameters. But you do this process iteratively, selecting differents training and validation set, in order to reduce the bias that you would have by selecting only one validation set
What is K-fold cross-validation?
K fold cross validation is a method of cross validation where we select a hyperparameter k. The dataset is now divided into k parts. Now, we take the 1st part as validation set and remaining k-1 as training set. Then we take the 2nd part as validation set and remaining k-1 parts as training set. Like this, each part is used as validation set once and the remaining k-1 parts are taken together and used as training set. It should not be used in a time series data.
How to validate your models?
One of the most common approaches is splitting data into train, validation and test parts.
Models are trained on train data, hyperparameters (for example early stopping) are selected based on the validation data, the final measurement is done on test dataset.
Another approach is cross-validation: split dataset into K folds and each time train models on training folds and measure the performance on the validation folds.
Also you could combine these approaches: make a test/holdout dataset and do cross-validation on the rest of the data. The final quality is measured on test dataset.
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SAVE75 to get instant 75% discount in all the above courses before 8 JuneWhy is it require to split our data into three parts: train, validation, and test?
• The training set is used to fit the model, i.e. to train the model with the data.
• The validation set is then used to provide an unbiased evaluation of a model while fine-tuning hyperparameters. This improves the generalization of the model.
• Finally, a test data set which the model has never "seen" before should be used for the final evaluation of the model. This allows for an unbiased evaluation of the model. The evaluation should never be performed on the same data that is used for training. Otherwise the model performance would not be representative.
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