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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
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Data Science & Machine Learning (@datasciencefun) Ingliz til segmentidagi kanali faol ishtirokchi. Hozirda hamjamiyat 75 860 obunachidan iborat bo'lib, Taʼlim toifasida 2 107-o'rinni va Hindiston mintaqasida 4 219-o'rinni egallagan.
📊 Auditoriya ko‘rsatkichlari va dinamika
невідомо sanasidan buyon loyiha tez o‘sib, 75 860 obunachiga ega bo‘ldi.
22 Iyun, 2026 dagi oxirgi ma’lumotlarga ko‘ra kanal barqaror faollikka ega. Oxirgi 30 kunda obunachilar soni 728 ga, so‘nggi 24 soatda esa -2 ga o‘zgardi va umumiy qamrov yuqori darajada qolmoqda.
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- Jalb etish (ER): Auditoriya o‘rtacha 3.00% darajada jalb etiladi. Nashrdan keyingi dastlabki 24 soatda kontent odatda umumiy obunachilar sonining 1.05% ini tashkil etuvchi reaksiyalarni to‘playdi.
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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 23 Iyun, 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.
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Postlar arxiv
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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
What is the area under the PR curve? Is it a useful metric?
The Precision-Recall AUC is just like the ROC AUC, in that it summarizes the curve with a range of threshold values as a single score.
A high area under the curve represents both high recall and high precision, where high precision relates to a low false positive rate, and high recall relates to a low false negative rate.
What is the PR (precision-recall) curve?
A precision-recall curve (or PR Curve) is a plot of the precision (y-axis) and the recall (x-axis) for different probability thresholds. Precision-recall curves (PR curves) are recommended for highly skewed domains where ROC curves may provide an excessively optimistic view of the performance.
What is AUC (AU ROC)? When to use it?
AUC stands for Area Under the ROC Curve. ROC is a probability curve and AUC represents degree or measure of separability. It's used when we need to value how much model is capable of distinguishing between classes. The value is between 0 and 1, the higher the better.
What kind of problems neural nets can solve?
Neural nets are good at solving non-linear problems. Some good examples are problems that are relatively easy for humans (because of experience, intuition, understanding, etc), but difficult for traditional regression models: speech recognition, handwriting recognition, image identification, etc.
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Enroll as soon as possible because Udacity is giving huge discount this time and coupon code is valid for limited time only.What’s the interpretation of the bias term in linear models?
Bias is simply, a difference between predicted value and actual/true value. It can be interpreted as the distance from the average prediction and true value i.e. true value minus mean(predictions). But dont get confused between accuracy and bias.
What is the ROC curve? When to use it?
ROC stands for Receiver Operating Characteristics. The diagrammatic representation that shows the contrast between true positive rate vs false positive rate.
It is used when we need to predict the probability of the binary outcome.
What are the main assumptions of linear regression?
There are several assumptions of linear regression. If any of them is violated, model predictions and interpretation may be worthless or misleading.
1) Linear relationship between features and target variable.
2) Additivity means that the effect of changes in one of the features on the target variable does not depend on values of other features. For example, a model for predicting revenue of a company have of two features - the number of items a sold and the number of items b sold. When company sells more items a the revenue increases and this is independent of the number of items b sold. But, if customers who buy a stop buying b, the additivity assumption is violated.
3) Features are not correlated (no collinearity) since it can be difficult to separate out the individual effects of collinear features on the target variable.
4) Errors are independently and identically normally distributed (yi = B0 + B1*x1i + ... + errori):
i) No correlation between errors (consecutive errors in the case of time series data).
ii) Constant variance of errors - homoscedasticity. For example, in case of time series, seasonal patterns can increase errors in seasons with higher activity.
iii) Errors are normaly distributed, otherwise some features will have more influence on the target variable than to others. If the error distribution is significantly non-normal, confidence intervals may be too wide or too narrow.
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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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