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Machine Learning with Python

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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📈 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 68 579 subscribers, ranking 2 404 in the Education category and 4 688 in the India region.

📊 Audience metrics and dynamics

Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 68 579 subscribers.

According to the latest data from 25 October, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 227 over the last 30 days and by -9 over the last 24 hours, overall reach remains high.

  • Verification status: Not verified
  • Engagement rate (ER): The average audience engagement rate is 3.45%. 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 2 369 views. Within the first day, a publication typically gains 1 042 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 26 October, 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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68 579
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Posts Archive
Repost from Machine Learning
🔖 A Comprehensive Resource for Machine Learning Practice The platform offers: 🫡 1200+ machine learning tasks; 🫡 Mathematic
🔖 A Comprehensive Resource for Machine Learning Practice The platform offers: 🫡 1200+ machine learning tasks; 🫡 Mathematics, from linear algebra to PCA; 🫡 Deep Learning, NLP, and Computer Vision; 🫡 Labs with real datasets; 🫡 Learning paths and interview preparation; 🫡 Projects using GPT, RL, and CUDA. Almost everything can be accessed directly in your browser, and basic tasks are available for free. ⛓️ Link to the platform https://enghusseinsheikho.substack.com/p/a-comprehensive-resource-for-machine?r=3hwnr8&utm_campaign=post&utm_medium=web&showWelcomeOnShare=true #MachineLearning #DeepLearning #DataScience #AI #Coding #Tech ✨ Join Best TG Channels https://t.me/addlist/0f6vfFbEMdAwODBk ⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A

Repost from Machine Learning
🔖 A Comprehensive Resource for Machine Learning Practice The platform offers: 🫡 1200+ machine learning tasks; 🫡 Mathematic
🔖 A Comprehensive Resource for Machine Learning Practice The platform offers: 🫡 1200+ machine learning tasks; 🫡 Mathematics, from linear algebra to PCA; 🫡 Deep Learning, NLP, and Computer Vision; 🫡 Labs with real datasets; 🫡 Learning paths and interview preparation; 🫡 Projects using GPT, RL, and CUDA. Almost everything can be accessed directly in your browser, and basic tasks are available for free. ⛓️ Link to the platform https://enghusseinsheikho.substack.com/p/a-comprehensive-resource-for-machine?r=3hwnr8&utm_campaign=post&utm_medium=web&showWelcomeOnShare=true #MachineLearning #DeepLearning #DataScience #AI #Coding #Tech ✨ Join Best TG Channels https://t.me/addlist/0f6vfFbEMdAwODBk ⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A

Repost from Machine Learning
🔖 A Comprehensive Resource for Machine Learning Practice The platform offers: 🫡 1200+ machine learning tasks; 🫡 Mathematic
🔖 A Comprehensive Resource for Machine Learning Practice The platform offers: 🫡 1200+ machine learning tasks; 🫡 Mathematics, from linear algebra to PCA; 🫡 Deep Learning, NLP, and Computer Vision; 🫡 Labs with real datasets; 🫡 Learning paths and interview preparation; 🫡 Projects using GPT, RL, and CUDA. Almost everything can be accessed directly in your browser, and basic tasks are available for free. ⛓ Link to the platform https://www.deep-ml.com/ #MachineLearning #DeepLearning #DataScience #AI #Coding #Tech ✨ Join Best TG Channels https://t.me/addlist/0f6vfFbEMdAwODBk ⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A

🚀 RESEARCH & DIGITAL SOLUTIONS — YOUR PARTNER IN SUCCESS! Are you a researcher, student, business owner, or developer lookin
🚀 RESEARCH & DIGITAL SOLUTIONS — YOUR PARTNER IN SUCCESS! Are you a researcher, student, business owner, or developer looking for professional support? We are here to help you turn your ideas into real results! 💡 🔬 1. Scientific Research & Academic Publishing Professional review and evaluation of scientific manuscripts before submission. Guidance on journal selection and publication strategies. Expert advice to improve research quality and presentation. Support for researchers throughout the academic publishing process. 💻 2. Website & Software Development Professional website design and development for businesses and individuals. Custom electronic systems tailored to your needs. Technical assistance for students and academic projects. ⚙️ 3. API & User Interface (UI) Services API development and integration. Modern, responsive, and user-friendly interface design. Seamless integration between applications and systems. 📊 4. Real-World & Synthetic Datasets Providing real-world datasets for research and analysis. Creating customized synthetic datasets according to your requirements. Data support for scientific research, machine learning, and AI projects. 🤝 5. FREE Discussion & Consultation Discuss your research ideas and technical projects with us. Get initial guidance and recommendations. Explore the best solutions for your academic or professional needs. 🌟 WHY CHOOSE US? ✅ Research and technology expertise in one place. ✅ Tailored solutions based on your needs. ✅ Professional guidance and practical support. ✅ Free initial discussion and consultation. 📩 CONTACT US ON TELEGRAM 👨‍💻 Team Admin & Support: @Omidyzd62 💬 Feel free to contact us to discuss your ideas, ask questions, or explore how we can help! Your Research. Your Ideas. Our Expertise. Your Success. 🎯 #ResearchSupport #AcademicPublishing #ScientificResearch #WebDevelopment #SoftwareDevelopment #API #UIUX #Datasets #MachineLearning #ArtificialIntelligence #StudentSupport

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📚 Download 10 Python books for FREE: 1. Think Python — O’Reilly https://t.co/X7y3pX68IW 2. The Big Book of Small Python Projects https://inventwithpython.com/bigbookpython/ 3. Data Structures https://opendatastructures.org/ods-python.pdf 4. Data Science Handbook https://t.co/9aOLdiAbim 5. Data Analysis https://wesmckinney.com/book/ 6. Data Science https://t.co/TusyBWGfrx 7. Machine Learning https://t.co/7veOwESe8q 8. Statistics https://t.co/waWDQylQpY 9. Think Bayes https://allendowney.github.io/ThinkBayes2/ 10. Clean Code in Python — Packt https://t.co/OHhVgF1L65 #Python #FreeBooks #Programming #DataScience #MachineLearning #Coding ✨ Join Best TG Channels https://t.me/addlist/0f6vfFbEMdAwODBk ⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A

Repost from Data Analytics
One of the key moments when I truly understood how transformers work: 🧠✨ "Stop thinking of a transformer as a conveyor belt,
One of the key moments when I truly understood how transformers work: 🧠✨ "Stop thinking of a transformer as a conveyor belt, where each layer transforms the output of the previous one." "Start thinking of it as a residual flow." 🌊 Each block in a transformer has a residual connection that adds the input of the block to its output:
x' = f(x) + x
Because of this addition, the attention mechanism or MLP within the layer – the function f in the formula above – actually does NOT transform the input. Instead, it calculates the information that needs to be ADDED to the input before passing it to the next block! ➕ Imagine the main part of the transformer as a shared whiteboard. 📝 Each block reads what it needs from it and adds its own notes. All changes are additions. Furthermore, layers can exchange information over distances. A block in the first layer can write information, and a block in the fifth layer can read it, even though there is no direct connection between them. 🔗 I owe these ideas to an older article by Anthropic about the architecture of transformers: transformer-circuits.pub/2021/framework #Transformers #DeepLearning #AI #MachineLearning #NeuralNetworks #Tech ✨ Join Best TG Channels https://t.me/addlist/0f6vfFbEMdAwODBk ⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A

Repost from Machine Learning
🔖 Official PyTorch Tutorials This collection includes: 🫡 PyTorch fundamentals and torch.nn; 🫡 Computer Vision and transfer
🔖 Official PyTorch Tutorials This collection includes: 🫡 PyTorch fundamentals and torch.nn; 🫡 Computer Vision and transfer learning; 🫡 NLP and RNNs; 🫡 Reinforcement Learning; 🫡
torch.compile
and ONNX; 🫡 Distributed Training, FSDP, and Tensor Parallel; 🫡 Profiling and memory optimization; 🫡 Model serving and Ray. A useful resource for both beginners and those already working with PyTorch in production. ⛓https://docs.pytorch.org/tutorials/

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