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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📈 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 77 633 名订阅者，在 教育 类别中位列第 2 017，并在 印度 地区排名第 3 821 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 77 633 名订阅者。
根据 13 十一月, 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 -142，过去 24 小时变化为 -20，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 2.37%。内容发布后 24 小时内通常能获得 1.01% 的反应，占订阅者总量。
- 帖子覆盖： 每篇帖子平均可获得 1 843 次浏览，首日通常累积 781 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 14 十一月, 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。
77 633
订阅者
-2024 小时
-957 天
-14230 天
帖子存档
👉The Ultimate Guide to the Pandas Library for Data Science in Python
👇👇
https://www.freecodecamp.org/news/the-ultimate-guide-to-the-pandas-library-for-data-science-in-python/amp/
A Visual Intro to NumPy and Data Representation
.
Link : 👇👇
https://jalammar.github.io/visual-numpy/
Matplotlib Cheatsheet 👇👇
https://github.com/rougier/matplotlib-cheatsheet
SQL Cheatsheet 👇👇
https://websitesetup.org/sql-cheat-sheet/
Join our group 👉Bybit Global Activities 👈 to get 20 USDT maximum!!💵
See more details in the group! complete registration ONLY will just take 5 mins to get USDT🥳🤑
Bybit：Established in March 2018, Bybit is one of the fastest growing cryptocurrency exchanges, with more than 3 million registered users. Welcome to join us!!
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Start working on any project if you are a beginner and want to grow your career as a data scientist
You will learn much more as you practice and work on projects from yourself
You can find dataset in this channel or go to kaggle to find any random dataset and just work on it
Learning concepts is fine but most of the learnings come from projects
I know that might feel boring at first time but as you move forward, it become interesting
K-means vs DBScan ML Algorithm
DBScan is more robust to noise.
DBScan is better when the amount of clusters is difficult to guess.
K-means has a lower complexity, i.e. it will be much faster, especially with a larger amount of points.
What is the curse of dimensionality? Why do we care about it?
Data in only one dimension is relatively tightly packed. Adding a dimension stretches the points across that dimension, pushing them further apart. Additional dimensions spread the data even further making high dimensional data extremely sparse. We care about it, because it is difficult to use machine learning in sparse spaces.
Dimensionality reduction techniques
Singular Value Decomposition (SVD)
Principal Component Analysis (PCA)
Linear Discriminant Analysis (LDA)
T-distributed Stochastic Neighbor Embedding (t-SNE)
Autoencoders
Fourier and Wavelet Transforms
Join our group 👉Bybit Global Activities 👈 to get 20 USDT maximum!!💵
See more details in the group! complete registration ONLY will just take 5 mins to get USDT🥳🤑
Bybit：Established in March 2018, Bybit is one of the fastest growing cryptocurrency exchanges, with more than 3 million registered users. Welcome to join us!!
🤑Referral Roll — Invite Friends & Team Up for 1 BTC, 22,000 USDT in Prizes & More 🤑
Chatbot project using ML
Before using this you have to install
Tensorflow, keras , pickle, nltk by using pip install in command prompt
Fake news Detection Machine Learning Project with 92%Accuracy
it contain compressed file in which "jupyter notebook file and dataset"✅
Adaptive_Computation_and_Machine_Learning_Ralf_Herbrich_Learning.pdf2.69 MB
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
👋 Welcome to @datastructuresandalgoofficial 👋
To get job in top tech companies like 𝙂𝙤𝙤𝙜𝙡𝙚, 𝘼𝙢𝙖𝙯𝙤𝙣, 𝙈𝙞𝙘𝙧𝙤𝙨𝙤𝙛𝙩, 𝙁𝙖𝙘𝙚𝙗𝙤𝙤𝙠, 𝙚𝙩𝙘 you must know 𝘿𝙖𝙩𝙖 𝙎𝙩𝙧𝙪𝙘𝙩𝙪𝙧𝙚𝙨 𝙖𝙣𝙙 𝘼𝙡𝙜𝙤𝙧𝙞𝙩𝙝𝙢𝙨. And for excelling in tech field you should know 𝘿𝙖𝙩𝙖 𝙎𝙩𝙧𝙪𝙘𝙩𝙪𝙧𝙚𝙨 𝙖𝙣𝙙 𝘼𝙡𝙜𝙤𝙧𝙞𝙩𝙝𝙢𝙨.
In this Channel you will get all materials related Data Structures and Algorithms(DSA).
𝙁𝙤𝙧 𝙡𝙚𝙖𝙧𝙣𝙞𝙣𝙜 𝘿𝙖𝙩𝙖 𝙎𝙩𝙧𝙪𝙘𝙩𝙪𝙧𝙚𝙨 𝙖𝙣𝙙 𝘼𝙡𝙜𝙤𝙧𝙞𝙩𝙝𝙢𝙨(𝘿𝙎𝘼) 𝙛𝙤𝙧 𝙛𝙧𝙚𝙚 𝙟𝙤𝙞𝙣 𝙤𝙪𝙧 𝙩𝙚𝙡𝙚𝙜𝙧𝙖𝙢 𝙘𝙝𝙖𝙣𝙣𝙚𝙡:
https://t.me/datastructuresandalgoofficial
So what are you waiting for?
Join right now👍
https://t.me/datastructuresandalgoofficial
👋 Welcome to @datastructuresandalgoofficial 👋
To get job in top tech companies like 𝙂𝙤𝙤𝙜𝙡𝙚, 𝘼𝙢𝙖𝙯𝙤𝙣, 𝙈𝙞𝙘𝙧𝙤𝙨𝙤𝙛𝙩, 𝙁𝙖𝙘𝙚𝙗𝙤𝙤𝙠, 𝙚𝙩𝙘 you must know 𝘿𝙖𝙩𝙖 𝙎𝙩𝙧𝙪𝙘𝙩𝙪𝙧𝙚𝙨 𝙖𝙣𝙙 𝘼𝙡𝙜𝙤𝙧𝙞𝙩𝙝𝙢𝙨. And for excelling in tech field you should know 𝘿𝙖𝙩𝙖 𝙎𝙩𝙧𝙪𝙘𝙩𝙪𝙧𝙚𝙨 𝙖𝙣𝙙 𝘼𝙡𝙜𝙤𝙧𝙞𝙩𝙝𝙢𝙨.
In this Channel you will get all materials related Data Structures and Algorithms(DSA).
𝙁𝙤𝙧 𝙡𝙚𝙖𝙧𝙣𝙞𝙣𝙜 𝘿𝙖𝙩𝙖 𝙎𝙩𝙧𝙪𝙘𝙩𝙪𝙧𝙚𝙨 𝙖𝙣𝙙 𝘼𝙡𝙜𝙤𝙧𝙞𝙩𝙝𝙢𝙨(𝘿𝙎𝘼) 𝙛𝙤𝙧 𝙛𝙧𝙚𝙚 𝙟𝙤𝙞𝙣 𝙤𝙪𝙧 𝙩𝙚𝙡𝙚𝙜𝙧𝙖𝙢 𝙘𝙝𝙖𝙣𝙣𝙚𝙡:
https://t.me/datastructuresandalgoofficial
So what are you waiting for?
Join right now👍
https://t.me/datastructuresandalgoofficial
Free Data Science courses from Udemy and Udacity
👇👇
Intro to Data Science
https://imp.i115008.net/rn2beD
Data Analysis and Visualization
https://imp.i115008.net/JrBjZR
Data Analysis with R by Facebook
https://imp.i115008.net/gbJr5r
Introduction to Data Science using Python
https://ern.li/OP/1qvkxbfaxqj
Intro to Data for Data Science
https://ern.li/OP/1qvkxbfbmf8
Data Science with Analogies, Algorithms and Solved Problems
https://ern.li/OP/1qvkxbfcehz
Introduction to Data Science for Complete Beginners
https://bit.ly/3sh4oPO
ENJOY LEARNING 👍👍
Source codes for data science projects 👇👇
1. Build chatbots:
https://dzone.com/articles/python-chatbot-project-build-your-first-python-pro
2. Credit card fraud detection:
https://www.kaggle.com/renjithmadhavan/credit-card-fraud-detection-using-python
3. Fake news detection
https://data-flair.training/blogs/advanced-python-project-detecting-fake-news/
4.Driver Drowsiness Detection
https://data-flair.training/blogs/python-project-driver-drowsiness-detection-system/
5. Recommender Systems (Movie Recommendation)
https://data-flair.training/blogs/data-science-r-movie-recommendation/
6. Sentiment Analysis
https://data-flair.training/blogs/data-science-r-sentiment-analysis-project/
7. Gender Detection & Age Prediction
https://www.pyimagesearch.com/2020/04/13/opencv-age-detection-with-deep-learning/
𝗘𝗡𝗝𝗢𝗬 𝗟𝗘𝗔𝗥𝗡𝗜𝗡𝗚👍👍
