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
Lynda.com - Data Science Foundations - Fundamentals.zip665.81 MB
Free ML webinar to learn how Swiggy uses Data Science!
Link: https://bit.ly/3gNRBy0 ✅
Only for Indian users
Would you prefer gradient boosting trees model or logistic regression when doing text classification with bag of words?
Usually logistic regression is better because bag of words creates a matrix with large number of columns. For a huge number of columns logistic regression is usually faster than gradient boosting trees.
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Python Programming. Python Programming for Beginners, Python Programming for Intermediates
LinkedIn - Python for Data Science Essential Training Part 2.zip390.08 MB
🌐 Join the researchers and programmers channel (Courses, Books, Papere and Codes).
t.me/DataScience_Books
Introduction to Data Science - A Python Approach to Concepts, Techniques and Applications - Laura Igual, Santi Segui (Springer, 2017)
What is unsupervised learning?
Unsupervised learning aims to detect patterns in the data where no labels are given.
What are precision, recall, and F1-score?
Precision and recall are classification evaluation metrics:
P = TP / (TP + FP) and R = TP / (TP + FN).
Where TP is true positives, FP is false positives and FN is false negatives
In both cases the score of 1 is the best: we get no false positives or false negatives and only true positives.
F1 is a combination of both precision and recall in one score (harmonic mean):
F1 = 2 * PR / (P + R).
Max F score is 1 and min is 0, with 1 being the best.
How NASA Auto Colourise Images with Deep Learning?
Free Live Sessions on Aug 19th,20th @7.00pm IST
Register here :
https://bit.ly/2SLpFlw
Machine Learning God by Stefan Stavrev (version 2.0).pdf1.57 MB
Some useful Machine Learning projects for practice
https://elitedatascience.com/machine-learning-projects-for-beginners
https://hackernoon.com/top-5-machine-learning-projects-for-beginners-47b184e7837f
https://www.springboard.com/blog/machine-learning-projects
https://www.ubuntupit.com/top-20-best-machine-learning-projects-for-beginner-to-professional
