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31 693 技术与应用9 381...

📈 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 9 381，并在印度地区排名第 31 693 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

R Cheatsheet - Part 3

13 667

Projects To Learn AI and LLM Engineering

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PCA Dimensionality Reduction Cheatsheet

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R Cheatsheet - Part 2

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The Curse of Dimensionality 🧩 Here’s something that trips up many beginners: More features ≠ always better. When your dataset has too many features (dimensions), weird things happen: ⛔️ Distances between points become meaningless. ⛔️ Models struggle to generalize. ⛔️Training time explodes. 👉 Solution: techniques like PCA, feature selection, or just collecting smarter data instead of more data. Remember: Adding noise isn’t adding information.

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R CHEATSHEET - Part 1

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Data Structure

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SQL for Data Science 📈.pdf2.25 KB

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Overfitting vs Underfitting 🎯 Why do ML models fail? Usually because of one of these two villains: Overfitting: The model memorizes training data but fails on new data. (Like a student who memorizes past exam questions but can’t handle a new one.) Underfitting: The model is too simple to capture patterns. (Like using a straight line to fit a curve.) The sweet spot? A model that generalizes well. Note: Regularization, cross-validation, and more data usually help fight these problems.

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AI vs ML vs Deep Learning 🤖 You’ve probably seen these 3 terms thrown around like they’re the same thing. They’re not. AI (Artificial Intelligence): the big umbrella. Anything that makes machines “smart.” Could be rules, could be learning. ML (Machine Learning): a subset of AI. Machines learn patterns from data instead of being explicitly programmed. Deep Learning: a subset of ML. Uses neural networks with many layers (deep) powering things like ChatGPT, image recognition, etc. Think of it this way: AI = Science ML = A chapter in the science Deep Learning = A paragraph in that chapter.

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Mathematical Foundations For Deep Learning

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Neural Networks and Deep Learning by Michael Nielsen.pdf5.82 MB

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🔥 Building models is fun… but here’s the real test: is your model actually any good, or just pretending? 👀 Topic: Evals in Data Science Evaluations—or evals—are our model’s report card. They tell us: - For a spam filter: Do we catch all spam (recall) without misclassifying grandma’s emails as junk (precision)? - For price prediction: How close are our predictions on average (RMSE)? But evals aren’t just about numbers—they influence trust, fairness, and real-world usefulness of our models. Discussion prompts: - What’s your go-to evaluation metric and why? - Seen a model that looked great on paper but flopped in reality? - Should fairness & usability be considered first-class evaluation metrics alongside accuracy? Free book to dive deeper: - Fairness and Machine Learning — rigorous, practical guide to evaluating models for fairness: https://fairmlbook.org/ Drop your thoughts below ⬇️

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TOP ML Interview Problems

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Machine_Learning_For_Dummies_by_John_Paul_Mueller,_Luca_Massaron.pdf11.81 MB

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Jupyter Notebook Basics.pdf7.43 KB