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Show moreπ Analytical overview of Telegram channel Data Science & Machine Learning
Channel Data Science & Machine Learning (@datasciencefun) in the English language segment is an active participant. Currently, the community unites 75 822 subscribers, ranking 2 109 in the Education category and 4 254 in the India region.
π Audience metrics and dynamics
Since its creation on Π½Π΅Π²ΡΠ΄ΠΎΠΌΠΎ, the project has demonstrated rapid growth, gathering an audience of 75 822 subscribers.
According to the latest data from 20 June, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 833 over the last 30 days and by 1 over the last 24 hours, overall reach remains high.
- Verification status: Not verified
- Engagement rate (ER): The average audience engagement rate is 3.15%. Within the first 24 hours after publication, content typically collects 1.15% reactions from the total number of subscribers.
- Post reach: On average, each post receives 2 391 views. Within the first day, a publication typically gains 875 views.
- Reactions and interaction: The audience actively supports content: the average number of reactions per post is 3.
- Thematic interests: Content is focused on key topics such as learning, accuracy, distribution, panda, dataset.
π Description and content policy
The author describes the resource as a platform for expressing subjective opinions:
βJoin this channel to learn data science, artificial intelligence and machine learning with funny quizzes, interesting projects and amazing resources for free
For collaborations: @love_dataβ
Thanks to the high frequency of updates (latest data received on 21 June, 2026), the channel maintains relevance and a high level of publication reach. Analytics show that the audience actively interacts with content, making it an important point of influence in the Education category.
75 822
Subscribers
+124 hours
+1047 days
+83330 days
Posts Archive
1. What is the Impact of Outliers on Logistic Regression?
The estimates of the Logistic Regression are sensitive to unusual observations such as outliers, high leverage, and influential observations. Therefore, to solve the problem of outliers, a sigmoid function is used in Logistic Regression.
2. What is the difference between vanilla RNNs and LSTMs?
The main difference between vanilla RNNs and LSTMs is that LSTMs are able to better remember long-term dependencies, while vanilla RNNs tend to forget them. This is due to the fact that LSTMs have a special type of memory cell that can retain information for longer periods of time, while vanilla RNNs only have a single layer of memory cells.
3. What is Masked Language Model in NLP?
Masked language models help learners to understand deep representations in downstream tasks by taking an output from the corrupt input. This model is often used to predict the words to be used in a sentence.
4. Why is the KNN Algorithm known as Lazy Learner?
When the KNN algorithm gets the training data, it does not learn and make a model, it just stores the data. Instead of finding any discriminative function with the help of the training data, it follows instance-based learning and also uses the training data when it actually needs to do some prediction on the unseen datasets. As a result, KNN does not immediately learn a model rather delays the learning thereby being referred to as Lazy Learner.
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Applied Geospatial Data Science with Python
David S. Jordan, 2023
Three different learning styles in machine learning algorithms:
1. Supervised Learning
Input data is called training data and has a known label or result such as spam/not-spam or a stock price at a time.
A model is prepared through a training process in which it is required to make predictions and is corrected when those predictions are wrong. The training process continues until the model achieves a desired level of accuracy on the training data.
Example problems are classification and regression.
Example algorithms include: Logistic Regression and the Back Propagation Neural Network.
2. Unsupervised Learning
Input data is not labeled and does not have a known result.
A model is prepared by deducing structures present in the input data. This may be to extract general rules. It may be through a mathematical process to systematically reduce redundancy, or it may be toΒ organizeΒ data by similarity.
Example problems are clustering, dimensionality reduction and association rule learning.
Example algorithms include: the Apriori algorithm and K-Means.
3. Semi-Supervised Learning
Input data is a mixture of labeled and unlabelled examples.
There is a desired prediction problem but the model must learn the structures to organize the data as well as make predictions.
Example problems are classification and regression.
Example algorithms are extensions to other flexible methods that make assumptions about how to model the unlabeled data.
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1. Explain the difference between L1 and L2 regularization.
Answer: L2 regularization tends to spread error among all the terms, while L1 is more binary/sparse, with many variables either being assigned a 1 or 0 in weighting. L1 corresponds to setting a Laplacean prior on the terms, while L2 corresponds to a Gaussian prior.
2. What is deep learning, and how does it contrast with other machine learning algorithms?
Answer: Deep learning is a subset of machine learning that is concerned with neural networks: how to use backpropagation and certain principles from neuroscience to more accurately model large sets of unlabelled or semi-structured data. In that sense, deep learning represents an unsupervised learning algorithm that learns representations of data through the use of neural nets.
3. Name an example where ensemble techniques might be useful.
Answer: Ensemble techniques use a combination of learning algorithms to optimize better predictive performance. They typically reduce overfitting in models and make the model more robust (unlikely to be influenced by small changes in the training data).
You could list some examples of ensemble methods (bagging, boosting, the βbucket of modelsβ method) and demonstrate how they could increase predictive power.
4. Whatβs the βkernel trickβ and how is it useful?
Answer: The Kernel trick involves kernel functions that can enable in higher-dimension spaces without explicitly calculating the coordinates of points within that dimension: instead, kernel functions compute the inner products between the images of all pairs of data in a feature space. This allows them the very useful attribute of calculating the coordinates of higher dimensions while being computationally cheaper than the explicit calculation of said coordinates.
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What Business Leaders Need to Know about Web3 (+ Metaverse).zip263.59 MB
1. How does a Decision Tree handle continuous(numerical) features?
Ans.
Decision Trees handle continuous features by converting these continuous features to a threshold-based boolean feature.
To decide The threshold value, we use the concept of Information Gain, choosing that threshold that maximizes the information gain.
2. What are Loss Function and Cost Functions?
Ans. the loss function is to capture the difference between the actual and predicted values for a single record whereas cost functions aggregate the difference for the entire training dataset.
The Most commonly used loss functions are Mean-squared error and Hinge loss.
3. What is the difference between Python Arrays and lists?
Ans. Arrays in python can only contain elements of same data types i.e., data type of array should be homogeneous. It is a thin wrapper around C language arrays and consumes far less memory than lists.
Lists in python can contain elements of different data types i.e., data type of lists can be heterogeneous. It has the disadvantage of consuming large memory.
4. What is root cause analysis? What is a causation vs. a correlation?
Ans. Root cause analysis: a method of problem-solving used for identifying the root cause(s) of a problem [5]
Correlation measures the relationship between two variables, range from -1 to 1. Causation is when a first event appears to have caused a second event. Causation essentially looks at direct relationships while correlation can look at both direct and indirect relationships.
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Applications of Machine Learning and Data Analytics Models in Maritime Transportation
Ran Yan, 2023
