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:📈 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 13 907 名订阅者，在 技术与应用 类别中位列第 8 911，并在 印度 地区排名第 28 819 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 13 907 名订阅者。
根据 30 八月, 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 90，过去 24 小时变化为 7，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 7.35%。内容发布后 24 小时内通常能获得 2.05% 的反应，占订阅者总量。
- 帖子覆盖： 每篇帖子平均可获得 1 022 次浏览，首日通常累积 285 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 31 八月, 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。
13 907
订阅者
+724 小时
+17 天
+9030 天
帖子存档
13 906
Python Machine Learning (3rd Ed.) Code Repository
Paperback: 770 pages
Publisher: Packt Publishing
Language: English
https://github.com/rasbt/python-machine-learning-book-3rd-edition
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13 906
Sorry I haven't forwarded it earlier, this post belongs to this channel as well. 👆
13 906
Data Analysis free courses
The Analytics Edge (Spring 2017)
by MIT
🎬 193 video lessons
⏰ 16 hours worth of material
🔗 Courses link
Statistics and data literacy for non-statisticians
Rating ⭐️: 4.7 out of 5
Students 👨🎓: 13,320
Duration ⏰: 1h 36min
Teacher: Mike X Cohen
🔗 Courses link
Data Analysis with Python courses
by freeCodeCamp
[
Data Analysis with Python
🎬 28 video lessons
Numpy
🎬 9 video lessons
Data Analysis with Python Projects
🔖 5 projects
🔗 Courses link
]
Data Analysis w/ Python 3 and Pandas
by sentdex
🎬 6 video lessons
⏰ 2-3 hours worth of material
🔗 Course link
Master Data Analysis with Python - Intro to Pandas 2022
Rating ⭐️: 4.6 out of 5
Students 👨🎓: 3,828
Duration ⏰: 1hr 49min
Teacher: Ted Petrou
🔗 Courses link
Learn to code for data analysis
by OpenLearn
⏳ 8 weeks
🔗 Course link
Lecture notes from Statistical Thinking and Data Analysis
by MIT
🔗 Notes link
Python for Data Analysis
Rating ⭐️: 4.2 out of 5
Students 👨🎓: 14,168
Duration ⏰: 1h 10min
Teacher: Bob Wakefield
🔗 Courses link
Prepare data for analysis
by Microsoft
📁2 modules
Get data in Power BI - 12 Units
Clean, transform, and load data in Power BI - 10 Units
Duration ⏰: 3 hr 26 min
🔗 Course link
NOC:Data Analysis and Decision Making - I, IIT Kanpur
NOC:Data Analysis & Decision Making - II, IIT Kanpur
NOC:Data Analysis & Decision Making - III, IIT Kanpur
👨🏫 Prof. Raghunandan Sengupta
Each of 3 parts lasts ⏳12 weeks!
#datanalysis #dataanalysis #datascience #powerbi #dataanalytics
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13 906
When to Choose CatBoost Over XGBoost or LightGBM [Practical Guide]
Boosting algorithms have become one of the most powerful algorithms for training on structural (tabular) data.
I have been working with these 3 for years, even my bachelor thesis was comparison of these 3 algorithms alongside AdaBoost. This article explains when to use CatBoost over other ones.
https://neptune.ai/blog/when-to-choose-catboost-over-xgboost-or-lightgbm
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13 906
Repost from Cool GitHub repositories
data-science
This is a path for those of you who want to complete the Data Science undergraduate curriculum on your own time, for free, with courses from the best universities in the World.
Creator: ossu
Stars ⭐️: 14.5k
Forked By: 2.6k
GithubRepo:https://github.com/ossu/data-science
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13 906
Knowledge Graphs Course
Data Models, Knowledge Acquisition, Inference and Applications
Department of Computer Science, Stanford University, Spring 2021
⏳10 weeks, each week has slides and video lessons 📽
https://web.stanford.edu/class/cs520/
#datascience #machinelearning #tensorflow #scikitlearn #keras
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13 906
Repost from Free programming books
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 2nd Edition
by Aurélien Géron
📑 510 pages
🔗 Book link
#datascience #machinelearning #tensorflow #scikitlearn #keras
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13 906
The R Programming For Data Science A-Z Complete Diploma 2022
Rating ⭐️: 4.5 out of 5
Students 👨🎓: 38,584
Duration ⏰: 5h 6min
🔗 Course link
13 906
Reproducible Data Science with Docker
https://archive.org/details/pyconza2018-Reproducible_Data_Science_with_Docker
13 906
Intro to Machine Learning
by Kaggle
Learn the core ideas in machine learning, and build your first models.
1 How Models Work
The first step if you're new to machine learning.
2 Basic Data Exploration
Load and understand your data.
3 Your First Machine Learning Model
Building your first model. Hurray!
#machinelearning #ml
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4 Model Validation
Measure the performance of your model, so you can test and compare alternatives.
5 Underfitting and Overfitting
Fine-tune your model for better performance.
6 Random Forests
Using a more sophisticated machine learning algorithm.
7 Machine Learning Competitions
Enter the world of machine learning competitions to keep improving and see your progress.
🔗 Course link
13 906
FOUNDATIONS OF MACHINE LEARNING
by Bloomberg
Understand the Concepts, Techniques and Mathematical Frameworks Used by Experts in Machine Learning
🎬 30 video lessons with slides
⏰ 28 hours
https://bloomberg.github.io/foml/#home
#machinelearning #ml
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13 906
The Incredible PyTorch
A curated list of tutorials, papers, projects, communities and more relating to PyTorch.
https://www.ritchieng.com/the-incredible-pytorch/
#pytorch
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