Machine Learning with Python

前往频道在 Telegram

Learn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers. Admin: @HusseinSheikho || @Hussein_Sheikho

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网络:Machine Learning with Python 印度5 038 教育2 416...

📈 Telegram 频道 Machine Learning with Python 的分析概览

频道 Machine Learning with Python (@codeprogrammer) 英语语言赛道中的是活跃参与者。目前社区聚集了 67 813 名订阅者，在教育类别中位列第 2 416，并在印度地区排名第 5 038 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 67 813 名订阅者。

根据 09 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 70，过去 24 小时变化为 10，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 2.94%。内容发布后 24 小时内通常能获得 2.44% 的反应，占订阅者总量。
帖子覆盖： 每篇帖子平均可获得 1 997 次浏览，首日通常累积 1 652 次浏览。
互动与反馈： 受众积极参与，单帖平均反应数为 7。
主题关注点： 内容集中在 insidead, learning, degree, evaluation, algorithm 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：
“Learn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers. Admin: @HusseinSheikho || @Hussein_Sheikho”

凭借高频更新（最新数据采集于 10 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。

67 813

订阅者

+1024 小时

+127 天

+7030 天

1 997

帖子浏览量

~ 1 65224 小时

~ 2 22648 小时

2.94%

参与率

~ 3

每日帖子数

Ads index

beta

帖子存档

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"Machine Learning & LLMs for Beginners" Don't miss these 2 books of 100-pages. Both are #FREE to read. 🌟 The Hundred-Page Machine Learning Book: themlbook.com/wiki/doku.php 🌟 The Hundred-Page Language Model Book: thelmbook.com

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"Data Structures & Algorithms using Python" This book of 222 pages implements all types of #DATASTRUCTURES and #ALGORITHMS. And it's 💯 #FREE. Download Free: https://donsheehy.github.io/datastructures/fullbook.pdf By: https://t.me/DataScience4

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Stanford’s Machine Learning - by Andrew Ng A complete lecture notes of 227 pages. Available Free. Download the notes: cs229.stanford.edu/main_notes.pdf

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🔗 Machine Learning from Scratch by Danny Friedman

https://dafriedman97.github.io/mlbook/content/introduction.html

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Introduction to Machine Learning Class Notes by Huy Nguyen https://www.cs.cmu.edu/~hn1/documents/machine-learning/notes.pdf

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Explore the world of Data Science through Jupyter Notebooks—insights, tutorials, and tools to boost your data journey. Code, analyze, and visualize smarter with every post. https://t.me/DataScienceN

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We have launched a support and donation campaign for our channel in order to improve the content and enrich it with more resources. You can donate to our channel using stars through this post. Thanks to everyone who contributes to improving our content, the benefit will be extended to all members

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Repost from Machine Learning

#MachineLearning Systems — Principles and Practices of Engineering Artificially Intelligent Systems: https://mlsysbook.ai/ open-source textbook focuses on how to design and implement AI systems effectively

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Linear Algebra The 2nd best book on linear algebra with ~1000 practice problems. A MUST for AI & Machine Learning. Completely FREE. Download it: https://www.cs.ox.ac.uk/files/12921/book.pdf

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Exercises in Machine Learning This book contains 75+ exercises Download, read, and practice: arxiv.org/pdf/2206.13446 GitHub Repo: https://github.com/michaelgutmann/ml-pen-and-paper-exercises

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Repost from Machine Learning with Python

This channels is for Programmers, Coders, Software Engineers. 0️⃣ Python 1️⃣ Data Science 2️⃣ Machine Learning 3️⃣ Data Visualization 4️⃣ Artificial Intelligence 5️⃣ Data Analysis 6️⃣ Statistics 7️⃣ Deep Learning 8️⃣ programming Languages ✅ https://t.me/addlist/8_rRW2scgfRhOTc0 ✅ https://t.me/Codeprogrammer

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📄 "Pandas Introduction to Advanced" booklet 👨🏻‍💻 You can't attend a #datascience interview and not be asked about Pandas! But you don't have to memorize all its methods and functions! With this booklet, you'll learn everything you need. ✔️ One of the most useful and interesting combinations is using #Pandas with #AWS Lambda, which can be very useful in real projects.

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👨🏻‍💻 One of the most popular GitHub repositories for "learning and using algorithms in Python" is The Algorithms - Python repo with 196K stars. ✏️ It has a lot of organized and categorized code that you can use to find, read, and run different algorithms. Everything you can think of is here; from simple algorithms like sorting to advanced algorithms for machine learning, artificial intelligence, neural networks, and more. ✅ Why should we use it? 🔢 For learning: If you're looking to learn algorithms in action, this is great. 🔢 For practice: You can take the codes, run them, and modify them to better understand. 🔢 For projects : You can even use the codes here in real-life or academic projects. 🔢 For interviews: If you're preparing for data science interviews, this is full of practical algorithms. ┌ 🏳️‍🌈 The Algorithms - Python └ 🐱 GitHub-Repos

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course lecture on building Transformers from first principles: https://www.dropbox.com/scl/fi/jhfgy8dnnvy5qq385tnms/lectureattentionneuralnetworks.pdf?rlkey=fddnkonsez76mf8bzider3hrv&dl=0 The #PyTorch notebooks also demonstrate how to implement #Transformers from scratch: https://github.com/xbresson/CS52422025/tree/main/labslecture07

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