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
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Network:📈 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 68 473 subscribers, ranking 2 398 in the Education category and 4 562 in the India region.
📊 Audience metrics and dynamics
Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 68 473 subscribers.
According to the latest data from 01 November, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by -24 over the last 30 days and by -28 over the last 24 hours, overall reach remains high.
- Verification status: Not verified
- Engagement rate (ER): The average audience engagement rate is 3.65%. Within the first 24 hours after publication, content typically collects 1.44% reactions from the total number of subscribers.
- Post reach: On average, each post receives 2 501 views. Within the first day, a publication typically gains 987 views.
- Reactions and interaction: The audience actively supports content: the average number of reactions per post is 8.
- 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 02 November, 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.
68 473
Subscribers
-2824 hours
-1137 days
-2430 days
Posts Archive
🚀 Comprehensive Tutorial: Build a Folder Monitoring & Intruder Detection System in Python
Read and get code: https://hackmd.io/@husseinsheikho/Build-a-Folder-Monitoring
#Python #Security #FolderMonitoring #IntruderDetection #OpenCV #FaceCapture #Automation #Windows #TaskScheduler #ComputerVision
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🖼️ Upload any image
🎯 Select the object you want to remove
🌟 The model automatically erases the object and intelligently reconstructs the background
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— Uses Segment Anything (SAM) by Meta for object segmentation
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— Works in your browser with an intuitive Gradio UI
✔️ Fully open-source and can be run locally.
📎 GitHub: https://github.com/zjx0101/ObjectClear
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Transformer Lesson - Part 1/7: Introduction and Architecture
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📚 JaidedAI/EasyOCR — an open-source Python library for Optical Character Recognition (OCR) that's easy to use and supports over 80 languages out of the box.
### 🔍 Key Features:
🔸 Extracts text from images and scanned documents — including handwritten notes and unusual fonts
🔸 Supports a wide range of languages like English, Russian, Chinese, Arabic, and more
🔸 Built on PyTorch — uses modern deep learning models (not the old-school Tesseract)
🔸 Simple to integrate into your Python projects
### ✅ Example Usage:
import easyocr
reader = easyocr.Reader(['en', 'ru']) # Choose supported languages
result = reader.readtext('image.png')
30 NumPy MCQs with solutions
Are you ready??
Let's start: https://codeprogrammer.notion.site/30-NumPy-MCQs-with-solutions-23ccd3a4dba9803e8fafe39a110a3f9e?source=copy_link
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