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 809 Education2 375...

📈 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 68 117 subscribers, ranking 2 375 in the Education category and 4 809 in the India region.

📊 Audience metrics and dynamics

Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 68 117 subscribers.

According to the latest data from 26 August, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 120 over the last 30 days and by -14 over the last 24 hours, overall reach remains high.

Verification status: Not verified
Engagement rate (ER): The average audience engagement rate is 4.55%. Within the first 24 hours after publication, content typically collects 2.02% reactions from the total number of subscribers.
Post reach: On average, each post receives 3 099 views. Within the first day, a publication typically gains 1 378 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 27 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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68 117

Subscribers

-1424 hours

-667 days

+12030 days

3 099

Post views

~ 1 37824 hours

~ 1 73248 hours

4.55%

Engagement rate

~ 2

Posts per day

Ads index

beta

Posts Archive

68 117

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📚 AI / ML / DL Learning Resources Hub 🤖🧠 A structured, end-to-end roadmap to master AI — from fundamentals to cutting-edge research. 🚀 A carefully curated, all-in-one repository designed to help Computer Science students, AI enthusiasts, and professionals 👩‍💻👨‍💻 who want to build strong foundations and progress confidently from beginner to advanced levels 📈. This hub brings together the high-quality books 📖, courses 🎓, playlists 🎵, research papers 📝, tools 🛠, and learning roadmaps 🗺 covering: Artificial Intelligence, Machine Learning, Deep Learning, Data Science 📊, Transformers, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and MLOps 🔄, all organized in a clear, practical, and industry-relevant manner. The resources are selected to balance theory 🧠, intuition 💡, and real-world application 🌍, allowing learners to follow modules sequentially or in parallel ⏳ based on their goals. ⭐️ Recommended resources highlight high-impact content widely used in academia 🏛, research 🔬, and industry 🏭, ensuring you focus on what truly matters in modern AI. 🆘 Repo: https://github.com/bishwaghimire/ai-learning-roadmaps By: https://t.me/CodeProgrammer

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Machine Learning Specialization — Study Notes & Labs Personal notes and lab notebooks from the Machine Learning Specialization by DeepLearning.AI & Stanford Online (Coursera), instructed by Prof. Andrew Ng. 📂 Repo: https://github.com/TruongDat05/machine-learning-notes-and-code

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🔥 Convolutional Neural Networks: Clearly explained! 🖼 Convolutional Neural Networks (CNNs): CNNs belong to the deep learning methods with layers like convolutional, pooling, and fully-connected layers that transform input images for recognition. ➡️ Feedforward Process: Data flows from input to output layers. Images undergo convolution operations, ReLu activation, and Max-Pooling to reduce size and enhance translation and scaling invariance. Finally, data is classified through a fully connected network. 🔄 Training Process: The training involves batches, backpropagation, and gradient descent to minimize errors. The weights start with random values and are updated through backpropagation. This cycle repeats until accuracy is achieved. 📊 Use Cases: CNNs excel in processing images, videos, and audio for tasks like classification, segmentation, and object detection. ⚠️ Limitations: While CNNs handle translation and scaling well, they struggle with rotation invariance. Want to learn more about CNNs? Then, check out super-detailed article about it. 👇 https://lnkd.in/eyA_DnYj

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Repost from Learn Python Coding

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This Machine Learning Cheat Sheet Saved Me Hours of Revision ⏳ It includes: ✅ Supervised & Unsupervised algorithms ✅ Regression, Classification & Clustering techniques ✅ PCA & Dimensionality Reduction ✅ Neural Networks, CNN, RNN & Transformers ✅ Assumptions, Pros/Cons & Real-world use cases Whether you're: 🔹 Preparing for data science interviews 🔹 Working on ML projects 🔹 Or strengthening your fundamentals this one-page guide is a must-save. ♻️ Repost and share with your ML circle. #MachineLearning #DataScience #AI #MLAlgorithms #InterviewPrep #LearnML

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🧐 Python Cheatsheet — a convenient cheat sheet for Python that really saves time at work! The repository contains a summary of key topics: from basic syntax and data structures to working with files, environments, and OOP with classes and magic methods. Everything is presented compactly, without unnecessary theory, with examples that can be immediately applied in code. Repo: https://github.com/onyxwizard/python-cheatsheet https://t.me/pythonRe 👩‍💻

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