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 India4 740 Education2 366...

📈 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 68 138 subscribers, ranking 2 366 in the Education category and 4 740 in the India region.

📊 Audience metrics and dynamics

Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 68 138 subscribers.

According to the latest data from 30 August, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 86 over the last 30 days and by 27 over the last 24 hours, overall reach remains high.

Verification status: Not verified
Engagement rate (ER): The average audience engagement rate is 4.07%. Within the first 24 hours after publication, content typically collects 1.52% reactions from the total number of subscribers.
Post reach: On average, each post receives 2 775 views. Within the first day, a publication typically gains 1 037 views.
Reactions and interaction: The audience actively supports content: the average number of reactions per post is 5.
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 31 August, 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.

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68 138

Subscribers

+2724 hours

-297 days

+8630 days

2 775

Post views

~ 1 03724 hours

~ 1 11248 hours

4.07%

Engagement rate

~ 2

Posts per day

Ads index

beta

Posts Archive

68 140

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68 140

Repost from Machine Learning

📌 PyTorch Explained: From Automatic Differentiation to Training Custom Neural Networks 🗂 Category: DEEP LEARNING 🕒 Date: 2025-09-24 | ⏱️ Read time: 15 min read Deep learning is shaping our world as we speak. In fact, it has been slowly…

68 140

Most of the time, you regret helping others in general, because not everyone appreciates your hard work or the effort it takes you to spread a piece of information, even if it's quoted

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68 140

Repost from Machine Learning

📌 Paper Walkthrough: Attention Is All You Need 🗂 Category: DEEP LEARNING 🕒 Date: 2024-11-03 | ⏱️ Read time: 46 min read The complete guide to implementing a Transformer from scratch

68 140

Repost from Machine Learning

📌 Building a Convolutional Neural Network (CNNs) from Scratch 🗂 Category: 🕒 Date: 2024-11-05 | ⏱️ Read time: 15 min read Line-by-Line, Let’s Build a ResNet Classifier on the MNIST-Fashion Dataset

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68 140

Jeden Tag verpassen, wie andere Top-Deals abstauben? Warum noch länger zu viel bezahlen? Spare jetzt bis zu 80 % bei Angeboten von Amazon, eBay u.v.m.! Aber: Viele Deals sind nur für kurze Zeit verfügbar! Entdecke die heißesten Schnäppchen zuerst und sicher dir deinen Vorteil – bevor sie weg sind! #ad InsideAds

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💯 Use Kaggle like a pro with this method! 👨🏻‍💻 Never underestimate Kaggle! One of the best ways to start learning data science and ML is Kaggle. A place where theory turns into practice, beginners become professionals, and skills turn into value. 🎯 This roadmap is the key to practical use of this amazing platform:👇 ⬅️ Step one: Strengthen your basic skills! ✏️ Start with Kaggle's short and free courses. Practical, focused, and suitable for beginners. ✅ Python ⬅️ Link ☑️ Introduction to Machine Learning ⬅️ Link ✔️ Introduction to Deep Learning ⬅️ Link ✔️ Introduction to SQL ⬅️ Link ✔️ Introduction to Game AI and RL ⬅️ Link 📝 Complete list of courses ⬅️Link ➖➖➖➖➖➖ ⬅️ Step two: Apply what you’ve learned. ✏️ Learning alone is not enough; you have to solve problems! Kaggle competitions are the best place for this. ✅ Classification problem for beginners ☑️ Regression-based challenge ✔️ Fake news detection with NLP ✔️ Deep learning on image data with TPU 📝 Complete list of competitions ⬅️Link https://t.me/CodeProgrammer 🌟

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Want to create SQL databases visually? 🔥 Try this online tool that allows you to design and model databases using a convenient drag-and-drop interface. It helps reinforce SQL knowledge, better understand relationships between tables, and work without installing software or registering. The tool is completely free and open source, and also supports importing and exporting SQL code. website: https://www.drawdb.app/ 👉 https://t.me/CodeProgrammer

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Gradient Boosting for Regression Notes.pdf6.45 MB

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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