现已上线！2025 年 Telegram 研究 — 年度关键洞察 
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
显示更多📈 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 75 837 名订阅者，在 教育 类别中位列第 2 107，并在 印度 地区排名第 4 219 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 75 837 名订阅者。
根据 22 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 728，过去 24 小时变化为 -2，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 3.00%。内容发布后 24 小时内通常能获得 1.05% 的反应，占订阅者总量。
- 帖子覆盖： 每篇帖子平均可获得 2 278 次浏览，首日通常累积 794 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 23 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。
75 837
订阅者
-224 小时
+637 天
+72830 天
帖子存档
👉A handy notebook on handling missing values
Link : 👇👇
https://www.kaggle.com/parulpandey/a-guide-to-handling-missing-values-in-python
A list of NLP Tutorials
Link : 👇👇
https://github.com/lyeoni/nlp-tutorial
“An Implementation and Explanation of the Random Forest in Python” by Will Koehrsen 👇👇
https://link.medium.com/GCWFv81v95
“How to analyse 100s of GBs of data on your laptop with Python” by Jovan Veljanoski 👇👇
https://link.medium.com/V8xS82Cax6
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 🤑
Recall how many of the true positives were recalled (found), i.e. how many of the correct hits were also found. Its formula would be
Precision is one indicator of a machine learning model's performance – the quality of a positive prediction made by the model. Its formula would be?
Seeing Theory : A visual introduction to probability and statistics
Link :👇👇
https://seeing-theory.brown.edu/
“The Projects You Should Do to Get a Data Science Job” by Ken Jee
👇👇
https://link.medium.com/Q2DnxSGRO6
👉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!!
🤑Referral Roll — Invite Friends & Team Up for 1 BTC, 22,000 USDT in Prizes & More 🤑
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"✅
