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Data Science & Machine Learning

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

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📈 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 77 616 subscribers, ranking 2 020 in the Education category and 3 811 in the India region.

📊 Audience metrics and dynamics

Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 77 616 subscribers.

According to the latest data from 14 November, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by -147 over the last 30 days and by -20 over the last 24 hours, overall reach remains high.

  • Verification status: Not verified
  • Engagement rate (ER): The average audience engagement rate is 2.41%. Within the first 24 hours after publication, content typically collects 0.97% reactions from the total number of subscribers.
  • Post reach: On average, each post receives 1 872 views. Within the first day, a publication typically gains 755 views.
  • Reactions and interaction: The audience actively supports content: the average number of reactions per post is 2.
  • Thematic interests: Content is focused on key topics such as learning, accuracy, distribution, panda, dataset.

📝 Description and content policy

The author describes the resource as a platform for expressing subjective opinions:
“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”

Thanks to the high frequency of updates (latest data received on 15 November, 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.

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77 616
Subscribers
-2024 hours
-947 days
-14730 days
Posts Archive
Which function is used to randomly reorder a Series or rows in Dataframe?
Anonymous voting

Which of the following is correct code to generate 20 normally distributed random numbers? import numpy as np
Anonymous voting

Which type of data is separated into bins for analysis
Anonymous voting

Just a correction here "np.nan" instead of "na.nan" Hope you all are aware that mostly we import numpy as np

Which of the following is incorrect code to replace na values with 0 in data?
Anonymous voting

Majority is correct Well done guys 👍👍 Scikit learn can be specially used to solve classification, regression, clustering and dimensionality reduction problems

Which Scipy package can be used to solve differential equations?
Anonymous voting

Which Scipy package can be used for standard continuous and discrete probability distributions?
Anonymous voting

Keyboard shortcuts for data scientists

Which of the following is not a function of scikit learn?
Anonymous voting

An Artificial Neuron Network (ANN), popularly known as Neural Network is a computational model based on the structure and functions of biological neural networks. It is like an artificial human nervous system for receiving, processing, and transmitting information in terms of Computer Science. Basically, there are 3 different layers in a neural network : Input Layer (All the inputs are fed in the model through this layer) Hidden Layers (There can be more than one hidden layers which are used for processing the inputs received from the input layers) Output Layer (The data after processing is made available at the output layer) Graph data can be used with a lot of learning tasks contain a lot rich relation data among elements. For example, modeling physics system, predicting protein interface, and classifying diseases require that a model learns from graph inputs. Graph reasoning models can also be used for learning from non-structural data like texts and images and reasoning on extracted structures.

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Top Bayesian Algorithms and Methods: - Naive Bayes. - Averages one-dependence estimators. - Bayesian belief networks. - Gaussian naive Bayes. - Multinomial naive Bayes. - Bayesian networks.

SQL-cheat-sheet.pdf2.25 KB

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