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Data science/ML/AI

Data science/ML/AI

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Data science and machine learning hub Python, SQL, stats, ML, deep learning, projects, PDFs, roadmaps and AI resources. For beginners, data scientists and ML engineers πŸ‘‰ https://rebrand.ly/bigdatachannels DMCA: @disclosure_bds Contact: @mldatascientist

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Programming, data science, ML - free courses by Big Data SpecialistNetwork:Programming, data science, ML - free courses by Big Data SpecialistIndia31 551Technologies & Applications9 384

πŸ“ˆ Analytical overview of Telegram channel Data science/ML/AI

Channel Data science/ML/AI (@datascience_bds) in the English language segment is an active participant. Currently, the community unites 13 684 subscribers, ranking 9 384 in the Technologies & Applications category and 31 551 in the India region.

πŸ“Š Audience metrics and dynamics

Since its creation on Π½Π΅Π²Ρ–Π΄ΠΎΠΌΠΎ, the project has demonstrated rapid growth, gathering an audience of 13 684 subscribers.

According to the latest data from 11 June, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 150 over the last 30 days and by 11 over the last 24 hours, overall reach remains high.

  • Verification status: Not verified
  • Engagement rate (ER): The average audience engagement rate is 8.13%. Within the first 24 hours after publication, content typically collects 2.20% reactions from the total number of subscribers.
  • Post reach: On average, each post receives 1 112 views. Within the first day, a publication typically gains 301 views.
  • Reactions and interaction: The audience actively supports content: the average number of reactions per post is 5.
  • Thematic interests: Content is focused on key topics such as panda, learning, row, api, ethic.

πŸ“ Description and content policy

The author describes the resource as a platform for expressing subjective opinions:
β€œData science and machine learning hub Python, SQL, stats, ML, deep learning, projects, PDFs, roadmaps and AI resources. For beginners, data scientists and ML engineers πŸ‘‰ https://rebrand.ly/bigdatachannels DMCA: @disclosure_bds Contact: @mldatasci...”

Thanks to the high frequency of updates (latest data received on 12 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 Technologies & Applications category.

13 684
Subscribers
+1124 hours
+227 days
+15030 days
Posts Archive
Data science/ML/AI
Data science/ML/AI
13 690
When to Choose CatBoost Over XGBoost or LightGBM [Practical Guide] Boosting algorithms have become one of the most powerful algorithms for training on structural (tabular) data. I have been working with these 3 for years, even my bachelor thesis was comparison of these 3 algorithms alongside AdaBoost. This article explains when to use CatBoost over other ones. https://neptune.ai/blog/when-to-choose-catboost-over-xgboost-or-lightgbm βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž– Join @datascience_bds for more cool data science materials. *This channel belongs to @bigdataspecialist group

Data science/ML/AI
Data science/ML/AI
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Repost from Cool GitHub repositories
data-science This is a path for those of you who want to complete the Data Science undergraduate curriculum on your own time, for free, with courses from the best universities in the World. Creator: ossu Stars ⭐️: 14.5k Forked By: 2.6k GithubRepo:https://github.com/ossu/data-science βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž– Join @github_repositories_bds for more cool repositories. *This channel belongs to @bigdataspecialist group

Data science/ML/AI
Data science/ML/AI
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photo content

Data science/ML/AI
Data science/ML/AI
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Knowledge Graphs Course Data Models, Knowledge Acquisition, Inference and Applications Department of Computer Science, Stanford University, Spring 2021 ⏳10 weeks, each week has slides and video lessons πŸ“½ https://web.stanford.edu/class/cs520/ #datascience #machinelearning #tensorflow #scikitlearn #keras βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž– Join @programming_books_bds for more

Data science/ML/AI
Data science/ML/AI
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ML_cheatsheets.pdf7.63 MB

Data science/ML/AI
Data science/ML/AI
13 690
Another data science channel you might like: https://t.me/Artificial_Intelligence_DS

Data science/ML/AI
Data science/ML/AI
13 690
Repost from Free programming books
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 2nd Edition by AurΓ©lien GΓ©ron πŸ“‘ 510 pages πŸ”— Book link #
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 2nd Edition by AurΓ©lien GΓ©ron πŸ“‘ 510 pages πŸ”— Book link #datascience #machinelearning #tensorflow #scikitlearn #keras βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž– Join @programming_books_bds for more

Data science/ML/AI
Data science/ML/AI
13 690
The R Programming For Data Science A-Z Complete Diploma 2022 Rating ⭐️: 4.5 out of 5 Students πŸ‘¨β€πŸŽ“: 38,584 Duration ⏰: 5h 6min πŸ”— Course link

Data science/ML/AI
Data science/ML/AI
13 690
ML Q&A.pdf2.14 KB

Data science/ML/AI
Data science/ML/AI
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Data science/ML/AI
Data science/ML/AI
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photo content

Data science/ML/AI
Data science/ML/AI
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Intro to Machine Learning by Kaggle Learn the core ideas in machine learning, and build your first models. 1 How Models Work The first step if you're new to machine learning. 2 Basic Data Exploration Load and understand your data. 3 Your First Machine Learning Model Building your first model. Hurray! #machinelearning #ml βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž– Join @datascience_bds for more cool data science materials. *This channel belongs to @bigdataspecialist group 4 Model Validation Measure the performance of your model, so you can test and compare alternatives. 5 Underfitting and Overfitting Fine-tune your model for better performance. 6 Random Forests Using a more sophisticated machine learning algorithm. 7 Machine Learning Competitions Enter the world of machine learning competitions to keep improving and see your progress. πŸ”— Course link

Data science/ML/AI
Data science/ML/AI
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Data Cleaning Guide.pdf2.11 MB

Data science/ML/AI
Data science/ML/AI
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photo content

Data science/ML/AI
Data science/ML/AI
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FOUNDATIONS OF MACHINE LEARNING by Bloomberg Understand the Concepts, Techniques and Mathematical Frameworks Used by Experts in Machine Learning 🎬 30 video lessons with slides ⏰ 28 hours https://bloomberg.github.io/foml/#home #machinelearning #ml βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž– Join @datascience_bds for more cool data science materials. *This channel belongs to @bigdataspecialist group

Data science/ML/AI
Data science/ML/AI
13 690
The Incredible PyTorch A curated list of tutorials, papers, projects, communities and more relating to PyTorch. https://www.ritchieng.com/the-incredible-pytorch/ #pytorch βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž– Join @datascience_bds for more cool data science materials. *This channel belongs to @bigdataspecialist group

Data science/ML/AI
Data science/ML/AI
13 690
Lectures for UC Berkeley CS 182: Deep Learning Spring 2021 🎬 66 videos ⏰ 26 hours https://www.youtube.com/playlist?list=PL_iWQOsE6TfVmKkQHucjPAoRtIJYt8a5A βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž– Join @datascience_bds for more cool data science materials. *This channel belongs to @bigdataspecialist group

Data science/ML/AI
Data science/ML/AI
13 690
cheatsheet-supervised-learning.pdf6.41 KB

Data science/ML/AI
Data science/ML/AI
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photo content

Data science/ML/AI
Data science/ML/AI
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photo content