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 68 536 subscribers, ranking 2 402 in the Education category and 4 600 in the India region.
📊 Audience metrics and dynamics
Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 68 536 subscribers.
According to the latest data from 29 October, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 74 over the last 30 days and by -5 over the last 24 hours, overall reach remains high.
- Verification status: Not verified
- Engagement rate (ER): The average audience engagement rate is 3.84%. Within the first 24 hours after publication, content typically collects 1.47% reactions from the total number of subscribers.
- Post reach: On average, each post receives 2 629 views. Within the first day, a publication typically gains 1 007 views.
- Reactions and interaction: The audience actively supports content: the average number of reactions per post is 9.
- 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 30 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.
68 536
Subscribers
-524 hours
-847 days
+7430 days
Posts Archive
😎 Machine Learning Cheatsheet — a structured ML guide!
There are no courses here, no unnecessary theory or long lectures, but there are clear formulas, algorithms, the logic of ML pipelines, and a neatly structured knowledge base. It's perfect for quickly refreshing your understanding of algorithms or having it handy as an ML cheat sheet during work.
📌 Here's the link: ml-cheatsheet.readthedocs.io
🚪 @codeprogrammer | #resource
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Repost from ADMINOTEKA
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Подписан — значит вооружен!
Best GitHub repositories to learn AI from scratch in 2026:1. Andrej Karpathy https://github.com/karpathy/nn-zero-to-hero 2. Hugging Face Transformers https://github.com/huggingface/transformers 3. FastAI/fastbook https://github.com/fastai/fastbook 4. Made-With-ML https://github.com/GokuMohandas/Made-With-ML 5. ML System Design https://github.com/chiphuyen/machine-learning-systems-design 6. Awesome Generative AI guide https://github.com/aishwaryanr/awesome-generative-ai-guide 7. Dive into Deep Learning https://github.com/d2l-ai/d2l-en 👉 @codeprogrammer
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Collection of books on machine learning and artificial intelligence in PDF format
Repo: https://github.com/Ramakm/AI-ML-Book-References
#MACHINELEARNING #PYTHON #DATASCIENCE #DATAANALYSIS #DeepLearning
👉 @codeprogrammer
Repost from Machine Learning with Python
🤖 Machine Learning Tutorials Repository
1. Python
2. Computer Vision: Techniques, algorithms
3. NLP
4. Matplotlib
5. NumPy
6. Pandas
7. MLOps
8. LLMs
9. PyTorch/TensorFlow
git clone https://github.com/patchy631/machine-learning
🔗 GitHub: https://github.com/patchy631/machine-learning/tree/main
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YOLO Training Template
Manual data labeling has become significantly more convenient. Now the process looks like in the usual labeling systems - you just outline the object with a frame and a bounding box is immediately created.
The platform allows:
• to upload your own dataset
• to label manually or auto-label via DINOv3
• to enrich the data if desired
• to train a #YOLO model on your own data
• to run inference immediately
• to export to ONNX or NCNN, which ensures compatibility with edge hardware and smartphones
All of this is available for free and can already be tested on #GitHub.
Repo:
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This channels is for Programmers, Coders, Software Engineers.
0️⃣ Python
1️⃣ Data Science
2️⃣ Machine Learning
3️⃣ Data Visualization
4️⃣ Artificial Intelligence
5️⃣ Data Analysis
6️⃣ Statistics
7️⃣ Deep Learning
8️⃣ programming Languages
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+7Machine Learning Roadmap 2026
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