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Data Science & Machine Learning

Data Science & Machine Learning

前往频道在 Telegram

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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Free Courses with Certificate - Python Programming, Data Science, Java Coding, SQL, Web Development, AI, ML, ChatGPT Expert网络:Free Courses with Certificate - Python Programming, Data Science, Java Coding, SQL, Web Development, AI, ML, ChatGPT Expert印度4 234教育2 106

📈 Telegram 频道 Data Science & Machine Learning 的分析概览

频道 Data Science & Machine Learning (@datasciencefun) 英语 语言赛道中的 是活跃参与者。目前社区聚集了 75 831 名订阅者，在 教育 类别中位列第 2 106，并在 印度 地区排名第 4 234 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 75 831 名订阅者。

根据 21 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 770，过去 24 小时变化为 8，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 3.15%。内容发布后 24 小时内通常能获得 1.09% 的反应，占订阅者总量。
  • 帖子覆盖： 每篇帖子平均可获得 2 385 次浏览，首日通常累积 827 次浏览。
  • 互动与反馈： 受众积极参与，单帖平均反应数为 3。
  • 主题关注点： 内容集中在 learning, accuracy, distribution, panda, dataset 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：
“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”

凭借高频更新（最新数据采集于 22 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 教育 类别中的关键影响点。

75 831
订阅者
+824 小时
+717 天
+77030 天
帖子存档
Data Science & Machine Learning
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Introduction to Machine Learning.pdf6.12 MB

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SecretNFT is the next phase in DAO Web3.0's evolution; it combines a unique and intriguing #MetaSpace with #NFT collecting, a
SecretNFT is the next phase in DAO Web3.0's evolution; it combines a unique and intriguing #MetaSpace with #NFT collecting, as well as competitive #playtoearn features for any NFT Collectors and Digital Artists on its roster. 🎁 Get SecretNFT Airdrop - https://t.me/SecretNft_bot?start=1619607198 #rarenft #nftdrop #nftcommunity #foundation #opensea #openseanft #nftcollection #NFTGiveAway #secretNFT

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The Data Science Design Manual.pdf17.72 MB

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Top free Data Science resources @datasciencefun 1. CS109 Data Science http://cs109.github.io/2015/pages/videos.html 2. Data Science Essentials https://www.edx.org/course/data-science-essentials 3. Learning From Data from California Institute of Technology http://work.caltech.edu/telecourse 4. Mathematics for Machine Learning by University of California, Berkeley https://gwthomas.github.io/docs/math4ml.pdf?fbclid=IwAR2UsBgZW9MRgS3nEo8Zh_ukUFnwtFeQS8Ek3OjGxZtDa7UxTYgIs_9pzSI 5. Foundations of Data Science by Avrim Blum, John Hopcroft, and Ravindran Kannan https://www.cs.cornell.edu/jeh/book.pdf?fbclid=IwAR19tDrnNh8OxAU1S-tPklL1mqj-51J1EJUHmcHIu2y6yEv5ugrWmySI2WY 6. Python Data Science Handbook https://jakevdp.github.io/PythonDataScienceHandbook/?fbclid=IwAR34IRk2_zZ0ht7-8w5rz13N6RP54PqjarQw1PTpbMqKnewcwRy0oJ-Q4aM 7. CS 221 ― Artificial Intelligence https://stanford.edu/~shervine/teaching/cs-221/ 8. Ten Lectures and Forty-Two Open Problems in the Mathematics of Data Science https://ocw.mit.edu/courses/mathematics/18-s096-topics-in-mathematics-of-data-science-fall-2015/lecture-notes/MIT18_S096F15_TenLec.pdf 9. Python for Data Analysis by Boston University https://www.bu.edu/tech/files/2017/09/Python-for-Data-Analysis.pptx 10. Data Mining bu University of Buffalo https://cedar.buffalo.edu/~srihari/CSE626/index.html?fbclid=IwAR3XZ50uSZAb3u5BP1Qz68x13_xNEH8EdEBQC9tmGEp1BoxLNpZuBCtfMSE Share the channel link with friends http://t.me/datasciencefun

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+3
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9 Best Machine Learning Use cases in our Daily Lives 🚀 👓 Youtube Recommendation 👓 Voice Assistants 👓 arrow Smartphone Cam
9 Best Machine Learning Use cases in our Daily Lives 🚀 👓 Youtube Recommendation 👓 Voice Assistants 👓 arrow Smartphone Camera 👓 Google Maps routes 👓 Email Filtering 👓 Search 👓 Translation 👓 Chatbots 👓 Fraud Protection

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Data Science Interview Questions.pdf3.82 KB

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😉5 Machine Learning Algorithms with Project Ideas 📉Linear Regression -> House Price Prediction 📈Logistic Regression -> Loan Default Prediction 🗞️ SVM -> News Classification 🏛️ KNN -> Breast Cancer Classification 🧮 Naive Bayes -> Text Classification

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Decision trees and Random forests? Decision tree is a type of supervised learning algorithm (having a pre-defined target variable) that is mostly used in classification problems. It works for both categorical and continuous input and output variables. In this technique, we split the population or sample into two or more homogeneous sets (or sub-populations) based on most significant splitter / differentiator in input variables. Random Forest is a versatile machine learning method capable of performing both regression and classification tasks. It also undertakes dimensional reduction methods, treats missing values, outlier values and other essential steps of data exploration, and does a fairly good job. It is a type of ensemble learning method, where a group of weak models combine to form a powerful model.

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Join Now Get access of 5000 books of python for free Join Below https://t.me/PythonEbookz

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Some interview questions related to Data science 1- what is difference between structured data and unstructured data. 2- what is multicollinearity.and how to remove them 3- which algorithms you use to find the most correlated features in the datasets. 4- define entropy 5- what is the workflow of principal component analysis 6- what are the applications of principal component analysis not with respect to dimensionality reduction 7- what is the Convolutional neural network. Explain me its working

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