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 078 Education2 407...

📈 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 67 827 subscribers, ranking 2 407 in the Education category and 5 078 in the India region.

📊 Audience metrics and dynamics

Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 67 827 subscribers.

According to the latest data from 04 June, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 75 over the last 30 days and by 11 over the last 24 hours, overall reach remains high.

Verification status: Not verified
Engagement rate (ER): The average audience engagement rate is 2.53%. Within the first 24 hours after publication, content typically collects 1.84% reactions from the total number of subscribers.
Post reach: On average, each post receives 1 717 views. Within the first day, a publication typically gains 1 249 views.
Reactions and interaction: The audience actively supports content: the average number of reactions per post is 6.
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 05 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.

67 827

Subscribers

+1124 hours

+587 days

+7530 days

1 717

Post views

~ 1 24924 hours

~ 1 70648 hours

2.53%

Engagement rate

~ 3

Posts per day

Ads index

beta

Posts Archive

67 829

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67 829

Repost from Machine Learning

🔥 Awesome open-source project to learn more about Transformer Models! 🤖✨ We found this interactive website that shows you visually how transformer models work. 🌐📊 Transformer Explainer: https://poloclub.github.io/transformer-explainer/ #TransformerModels #OpenSource #AI #MachineLearning #DataScience #Tech

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"Dive into Deep Learning" 📘🤖 is an open-source book that forms the mathematical foundation for large language models. 🧠📐 It covers linear algebra, mathematical analysis, probability theory, optimization methods, backpropagation, attention mechanisms, and transformer architectures. 🧮📉🔄 The book progressively moves from classical neural networks and convolutional neural networks to modern transformers and practical techniques used in large language models. 🚀🔗🧠 It contains over 1,000 pages 📖 and provides clear explanations, practical examples, and exercises. ✅📝 Making it one of the most comprehensive free resources for understanding the mathematical structure of modern artificial intelligence systems and language models. 🌐🔍🤖 arxiv.org/pdf/2106.11342 🔗 #DeepLearning #AI #MachineLearning #NeuralNetworks #Transformers #OpenSource

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Repost from Machine Learning

🚀 Master Binary Classification with Neural Networks! 🧠✨ Ever wondered how to build a neural network from scratch in Python using NumPy? 🐍📊 Binary classification is at the heart of many machine learning applications. 🎯🤖 Our super-detailed guide walks you through the entire process step by step. 📝📚 💡 Dive in and start building your own neural network today! 🏗🔥 https://tinztwinshub.com/data-science/a-beginners-guide-to-developing-an-artificial-neural-network-from-zero/ #MachineLearning #NeuralNetworks #Python #DataScience #AI #Tech

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AI is moving fast. Accountability is not. That is why we built the open source core of Forkit Dev. Forkit Dev introduces Model Passports and Agent Passports so AI systems can be tracked, verified, and understood across their lifecycle. Open source repo: https://github.com/arpitasarker01/Forkit_Dev If you care about trustworthy AI, open source infrastructure, model lineage, or compliance ready deployment, check it out and share your thoughts.

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Do you know that Python can shift sequences without slicing and creating new lists? 🤔 When you need to cyclically shift data, many use slicing:

data = data[-1:] + data[:-1]

But deque.rotate() does this at the level of the data structure and usually works more efficiently for cyclical operations. 🚀

q.rotate(1)

A negative value rotates the queue in the other direction. ⬅️

q.rotate(-2)

This is useful for ring buffers, task schedulers, cyclical queues, and round-robin algorithms. 🔄

workers.rotate(-1)

🔥 deque.rotate() allows you to implement cyclical data structures without manual index logic and without creating new lists. 💡 👉 Python Ready | #tip #Python #Programming #Deque #CodingTips #Tech #DevCommunity

67 829

Do you know that Python can shift sequences without slicing and creating new lists? When you need to cyclically shift data, many use slicing:

data = data[-1:] + data[:-1]

But `deque.rotate()

 does this at the level of the data structure and usually works more efficiently for cyclical operations.

``

python q.rotate(1)

A negative value rotates the queue in the other direction.

python q.rotate(-2)

This is useful for ring buffers, task schedulers, cyclical queues, and round-robin algorithms.

python workers.rotate(-1) ` 🔥 `deque.rotate()` allows you to implement cyclical data structures without manual index logic and without creating new lists. #Python #deque #rotate #Programming #Coding #DevTips

67 829

Repost from Machine Learning

👣 Rust Interview Deep Dive 🦀🔍 A repository for systematic preparation for Rust interviews at the middle, senior, and staff levels. 💼📚 Inside 100 real questions from interviews in product and infrastructure companies, detailed analyses with code examples and scenarios of tasks that occur in production. 💻🏗️ Not "guess the program's output", but the mechanics on which real services are built. 🛠️🚀 Here are lock-free structures, self-referential types in async, FFI with tensor libraries, correct Send on guards via await, memory ordering under loom, soundness of custom collections. 🔒⚡ And it all starts with the basics. Ownership, borrowing, lifetimes. 🧱🔄 Those who want can start from scratch or at the staff level. 🚶‍♂️👨‍💻 https://github.com/Develp10/rustinterviewquiestions 🔗 #Rust #Programming #InterviewPrep #SoftwareEngineering #SystemsProgramming #CareerGrowth

67 829

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67 829

Repost from Learn Python Coding

Cheat sheet on the basics of Python: 🐍📚 basic syntax and language rules 📝 scalar types — basic data types (int, float, bool, str, NoneType) 🔢 datetime — working with date and time 📅⏰ data structures — Python data structures (list, tuple, dict, set) 🗄 list — mutable lists for storing data collections 📋 tuple — immutable sequences of values 🔒 dict (hash map) — storing data in a key-value format 🗝 set — unique elements without order 🔘 slicing — obtaining parts of sequences through indices and step ✂️ module/library — connecting modules and libraries 🔌 help functions — using help() and dir() to explore the Python API 🛠 #Python #Coding #DataScience #Programming #Tech #DevCommunity

67 829

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reader3 📚✨ When you want to connect an AI like Gemini to help you analyze books or content, copying text from a reader usually becomes a hassle. 😩💻 Especially if you want to discuss a book by chapters. Highlighting text manually and copying it disrupts the flow and feels like a waste of time. ⏳🚫 Yesterday, Andrzej Karpati, a well-known AI expert, released a new project to the public: reader3, which solves this problem very neatly. 🎉🛠️ It's a lightweight EPUB reader that allows you to read a book together with AI. 🤖📖 Its interface is as minimalist as possible: only the necessary reading and navigation functions. 📉🧭 You can also manage your library through folders. 📁✨ The key feature is that it breaks an EPUB into chapters and displays the content one chapter at a time. 🔓📄 This makes it easy to copy the needed part of the book and pass it to a large model for analysis or discussion. 📋🔄 It significantly improves the reading experience when paired with AI. 🚀🧠 And it's very easy to get started - just run two commands via uv. ⚡🛠️ As a result, it's an excellent tool for those who love reading and want to use AI as a companion for text analysis. 📚🤝🤖 📁 Language: #Python 61.0% ⭐️ Stars: 1.5k ➡️ Link to GitHub https://github.com/karpathy/reader3 #AI #Python #Reader3 #Tech #BookLovers #Github https://t.me/CodeProgrammer ✅

67 829

Repost from Machine Learning with Python

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