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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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๐Ÿ“ˆ 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 75 933 subscribers, ranking 2 103 in the Education category and 4 204 in the India region.

๐Ÿ“Š Audience metrics and dynamics

Since its creation on ะฝะตะฒั–ะดะพะผะพ, the project has demonstrated rapid growth, gathering an audience of 75 933 subscribers.

According to the latest data from 23 June, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 731 over the last 30 days and by 33 over the last 24 hours, overall reach remains high.

  • Verification status: Not verified
  • Engagement rate (ER): The average audience engagement rate is 2.95%. Within the first 24 hours after publication, content typically collects 0.86% reactions from the total number of subscribers.
  • Post reach: On average, each post receives 2 239 views. Within the first day, a publication typically gains 650 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.

๐Ÿ“ Description and content policy

The author describes the resource as a platform for expressing subjective opinions:
โ€œJoin this channel to learn data science, artificial intelligence and machine learning with funny quizzes, interesting projects and amazing resources for free For collaborations: @love_dataโ€

Thanks to the high frequency of updates (latest data received on 24 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.

75 933
Subscribers
+3324 hours
+587 days
+73130 days
Posts Archive
Udacity(udacity.com) courses collections Udacity's Android Basics Nanodegree Download Link- https://mega.nz/folder/nDgXkaob#5LPk0Hpz4HgZ7njcvyNmqw @datasciencefun Udacity's Machine Learning Engineer Nanodegree Download Link- https://mega.nz/folder/qX5BWKDD#s6JadsuGzsyELin6zYfU8Q @datasciencefun Udacity's Blockchain Nanodegree Download Link- https://mega.nz/folder/HD43EKTL#jcAo2OvAjEQmi0SqHELuyA Udacity's Data Analyst Nanodegree Download Link- https://mega.nz/folder/GbgnkCaR#gQodlI6pEkoKGIaqDhuCUg

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If the resources shared in the channel are helpful to you guys, then share this channel link with your friends and WhatsApp groups ๐Ÿ‘‡๐Ÿ‘‡ http://t.me/datasciencefun We will try to come up with more amazing content related to data science and machine Learning

๐Ÿ”ถPython for Data Science and Machine Learning Bootcamp ๐Ÿ”ถ Udemy link: https://www.udemy.com/course/python-for-data-science-and-machine-learning-bootcamp/ ๐ŸŸฅ 3.2 GB Download link: https://drive.google.com/file/d/1vtJoDrZq4rd9ka7DZF20vqg7iB7u8lW4/view

๐Ÿ”ฐPython Cheat Sheet for all Programmers๐Ÿ”ฐ Top 15 Cheat Sheets for Machine Learning, Data Science & Big Data ๐Ÿ–‡Link : https://anonfiles.com/zcLcO0G5oc/Python_Top_15_Cheat_Sheets_for_Machine_Learning_Data_Science_Big_Data_rar Share and support us

Which of the following is an important library or framework for data visualization using PYTHON? [Not Machine learning]
Anonymous voting

Data science Tools
Data science Tools

Building the Machine Learning Model
Building the Machine Learning Model

Which step is done just after collecting data?
Anonymous voting

Do you want more books recommendations?
Anonymous voting

Hello guys, if you are a beginner in data science and want to learn it from scratch. Then, there is a good news for you. Currently Amazon is providing 77% off on this data science book Highly recommend if you are beginner in data science Purchase it before the price increases https://bit.ly/30j72GI Flipkart is selling the same book for rs. 2500 https://bit.ly/39K2pIJ Enjoy learning ๐Ÿ‘

๐Ÿ‘ฉ๐Ÿปโ€๐Ÿ’ป Why should one study Linear Algebra for ML? ๐Ÿ‘‰๐Ÿผ Clearly, to develop a better intuition for machine learning and deep learning algorithms and not treat them as black boxes. This would allow you to choose proper hyper-parameters and develop a better model. You would also be able to code algorithms from scratch and make your own variations to them as well. ๐Ÿ‘‰๐Ÿผ Learn Linear Algebra for Machine Learning with: Khan Academy: https://www.khanacademy.org/math/linear-algebra Udacity: https://www.udacity.com/course/linear-algebra-refresher-course--ud953 Coursera: https://www.coursera.org/learn/linear-algebra-machine-learning Here are some amazing freely available ebooks on the same topic: Mathematics for Machine Learning: https://mml-book.github.io/book/mml-book.pdf An Introduction to Statistical Learning: https://faculty.marshall.usc.edu/gareth-james/ISL/ Happy machine learning! ๐ŸŽ‰

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