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31 743 技术与应用9 391...

📈 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 13 667 名订阅者，在 技术与应用 类别中位列第 9 391，并在印度地区排名第 31 743 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 13 667 名订阅者。

根据 08 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 4，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 7.97%。内容发布后 24 小时内通常能获得 2.27% 的反应，占订阅者总量。
帖子覆盖： 每篇帖子平均可获得 1 089 次浏览，首日通常累积 310 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 09 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。

13 667

订阅者

+424 小时

+437 天

+15030 天

1 089

帖子浏览量

~ 31024 小时

~ 45848 小时

7.97%

参与率

~ 1

每日帖子数

Ads index

beta

帖子存档

13 667

Kafka 101

13 667

A Stanford CS' Lecture note diving into supervised/unsupervised algorithms, neural networks, SVMs with math proofs and Python pseudocode.

13 667

📚 Data Science Riddle A numeric feature has many repeated exact values with occasional jumps. What type of variable is this?

Anonymous voting

13 667

AI vs Machine Learning vs Deep Learning Vs Generative AI

13 667

4 Pillars of Data Science

13 667

📚 Data Science Riddle You fit a forecasting model and residuals show increasing variance. What is needed?

Anonymous voting

13 667

Repost from Programming, data science, ML - free courses by Big Data Specialist

Notes on HDFS, MapReduce, YARN, Hadoop vs. traditional systems and much more... from Columbia University.

13 667

📚 Data Science Riddle Your spark job fails due to executor memory pressure. Most effective optimization?

Anonymous voting

13 667

📚 Data Science Riddle You're working with highly noisy user text. Which tokenization met6handles misspellings best?

Anonymous voting

13 667

Eigenvalues & Eigenvectors — Why PCA Actually Works You’ve heard of PCA. But what’s really happening underneath? PCA finds the directions (vectors) where your data varies the most. Those directions are eigenvectors of the covariance matrix and the eigenvalues tell you how much variance each captures. You’re basically rotating your data to find its “natural axes.”

PCA isn’t compression — it’s discovering how your data wants to be seen.

13 667

📚 Data Science Riddle You're Processing a dataset with frequent schema evolution. Which format handles it most gracefully?

Anonymous voting

13 667

Covariance vs. Correlation: Same Family, Different Story People use them interchangeably but they measure different things. Covariance tells you the direction of relationship (positive or negative). Correlation goes further; it tells you the strength, normalized between -1 and 1. So while covariance can be 2345.67, correlation says 0.92. clear, interpretable, scale-free.

Covariance shows movement, correlation shows consistency.

13 667

K-Means Clustering

13 667

📚 Data Science Riddle A data engineer complains that your model training job is failing in production due to schema mismatch. What's the root fix?

Anonymous voting

13 667

Top 6 Data Concepts

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📚 Data Science Riddle In a real-world NLP project, your model performs poorly on new slang abbreviations. What's the fix?

Anonymous voting

13 667

Covers basics of Linear Regression for modeling numerical data, including assumptions and applications in genetics, from University of Washington.

13 667

Regression Analysis Cheatsheet

13 667

Repost from Programming, data science, ML - free courses by Big Data Specialist

This is our latest post from Instagram, saved as PDF. It's a comprehensive breakdown(as always) explaining the difference between Relational DB and Graph DB in a fun and easy to grasp way. ⚠️ Spoiler alert: You will love it! Here's our Instagram post: Relational DB Vs Graph DB

13 667

Covers basic numerical and graphical summaries with practical examples, from University of Washington.