Data science/ML/AI

前往频道在 Telegram

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 👉 https://rebrand.ly/bigdatachannels DMCA: @disclosure_bds Contact: @mldatascientist

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网络:Programming, data science, ML - free courses by Big Data Specialist 印度31 551 技术与应用9 384...

📈 Telegram 频道 Data science/ML/AI 的分析概览

频道 Data science/ML/AI (@datascience_bds) 英语语言赛道中的是活跃参与者。目前社区聚集了 13 690 名订阅者，在 技术与应用 类别中位列第 9 384，并在印度地区排名第 31 551 位。

📊 受众指标与增长动态

自 невідомо 创建以来，项目保持高速增长，吸引了 13 690 名订阅者。

根据 11 六月, 2026 的最新数据，频道保持稳定运转。过去 30 天订阅人数变化为 150，过去 24 小时变化为 11，整体触达仍然可观。

认证状态： 未认证
互动率 (ER)： 平均受众互动率为 8.13%。内容发布后 24 小时内通常能获得 2.20% 的反应，占订阅者总量。
帖子覆盖： 每篇帖子平均可获得 1 112 次浏览，首日通常累积 301 次浏览。
互动与反馈： 受众积极参与，单帖平均反应数为 5。
主题关注点： 内容集中在 panda, learning, row, api, ethic 等核心主题上。

📝 描述与内容策略

作者将该频道定位为表达主观观点的平台：
“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 👉 https://rebrand.ly/bigdatachannels DMCA: @disclosure_bds Contact: @mldatasci...”

凭借高频更新（最新数据采集于 12 六月, 2026），频道始终保持新鲜度与高覆盖。分析显示受众积极互动，使其成为 技术与应用 类别中的关键影响点。

13 690

订阅者

+1124 小时

+227 天

+15030 天

1 112

帖子浏览量

~ 30124 小时

~ 43548 小时

8.13%

参与率

~ 1

每日帖子数

Ads index

beta

帖子存档

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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 ➖➖➖➖➖➖➖➖➖➖➖➖➖➖ Join @datascience_bds for more cool data science materials. *This channel belongs to @bigdataspecialist group

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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 ➖➖➖➖➖➖➖➖➖➖➖➖➖➖ Join @github_repositories_bds for more cool repositories. *This channel belongs to @bigdataspecialist group

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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 📽 https://web.stanford.edu/class/cs520/ #datascience #machinelearning #tensorflow #scikitlearn #keras ➖➖➖➖➖➖➖➖➖➖➖➖➖ Join @programming_books_bds for more

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ML_cheatsheets.pdf7.63 MB

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Another data science channel you might like: https://t.me/Artificial_Intelligence_DS

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Repost from Free programming books

Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 2nd Edition by Aurélien Géron 📑 510 pages 🔗 Book link #datascience #machinelearning #tensorflow #scikitlearn #keras ➖➖➖➖➖➖➖➖➖➖➖➖➖ Join @programming_books_bds for more

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The R Programming For Data Science A-Z Complete Diploma 2022 Rating ⭐️: 4.5 out of 5 Students 👨‍🎓: 38,584 Duration ⏰: 5h 6min 🔗 Course link

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ML Q&A.pdf2.14 KB

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Reproducible Data Science with Docker https://archive.org/details/pyconza2018-Reproducible_Data_Science_with_Docker

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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 ➖➖➖➖➖➖➖➖➖➖➖➖➖➖ 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. 🔗 Course link

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Data Cleaning Guide.pdf2.11 MB

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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 ➖➖➖➖➖➖➖➖➖➖➖➖➖➖ Join @datascience_bds for more cool data science materials. *This channel belongs to @bigdataspecialist group

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

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

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cheatsheet-supervised-learning.pdf6.41 KB

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