Machine Learning with Python

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

Network:Machine Learning with Python India5 033 Education2 417...

📈 Analytical overview of Telegram channel Machine Learning with Python

Channel Machine Learning with Python (@codeprogrammer) in the English language segment is an active participant. Currently, the community unites 67 816 subscribers, ranking 2 417 in the Education category and 5 033 in the India region.

📊 Audience metrics and dynamics

Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 67 816 subscribers.

According to the latest data from 10 June, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 49 over the last 30 days and by -10 over the last 24 hours, overall reach remains high.

Verification status: Not verified
Engagement rate (ER): The average audience engagement rate is 2.61%. Within the first 24 hours after publication, content typically collects 2.40% reactions from the total number of subscribers.
Post reach: On average, each post receives 1 770 views. Within the first day, a publication typically gains 1 628 views.
Reactions and interaction: The audience actively supports content: the average number of reactions per post is 7.
Thematic interests: Content is focused on key topics such as insidead, learning, degree, evaluation, algorithm.

📝 Description and content policy

The author describes the resource as a platform for expressing subjective opinions:
“Learn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers. Admin: @HusseinSheikho || @Hussein_Sheikho”

Thanks to the high frequency of updates (latest data received on 11 June, 2026), the channel maintains relevance and a high level of publication reach. Analytics show that the audience actively interacts with content, making it an important point of influence in the Education category.

67 816

Subscribers

-1024 hours

-17 days

+4930 days

1 770

Post views

~ 1 62824 hours

~ 2 19748 hours

2.61%

Engagement rate

~ 3

Posts per day

Ads index

beta

Posts Archive

67 816

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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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Find these FREE AI Courses here 👇 https://www.mltut.com/best-resources-to-learn-artificial-intelligence/

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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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