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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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๐Ÿ“ˆ 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 67 819 subscribers, ranking 2 404 in the Education category and 5 049 in the India region.

๐Ÿ“Š Audience metrics and dynamics

Since its creation on ะฝะตะฒั–ะดะพะผะพ, the project has demonstrated rapid growth, gathering an audience of 67 819 subscribers.

According to the latest data from 05 June, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 77 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 2.60%. Within the first 24 hours after publication, content typically collects 2.50% reactions from the total number of subscribers.
  • Post reach: On average, each post receives 1 767 views. Within the first day, a publication typically gains 1 695 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.

๐Ÿ“ Description and content policy

The author describes the resource as a platform for expressing subjective opinions:
โ€œLearn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers. Admin: @HusseinSheikho || @Hussein_Sheikhoโ€

Thanks to the high frequency of updates (latest data received on 06 June, 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.

67 819
Subscribers
+924 hours
+587 days
+7730 days
Posts Archive
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Data scientists are in high demand right now: there's just too much data to analyze. In this course, Tatev and Vae teach #Python for #DataScience. You'll be doing projects and exploring EDA, A/B testing, BI, and more. https://t.me/Python53 ๐ŸŒŸ

๐Ÿ”– An excellent resource for learning about neural networks We're sharing a cool resource for learning about neural networks, offering clear, step-by-step instruction with dynamic visualizations and easy-to-understand explanations. In addition, you'll find many other useful materials on machine learning on the site. Find and use it โ€” https://mlu-explain.github.io/neural-networks/ tags: #AI #ML #PYTHON โžก @CODEPROGRAMMER

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๐Ÿ‘ A fresh deep learning course from MIT is now available publicly A full-fledged educational course has been published on th
๐Ÿ‘ A fresh deep learning course from MIT is now available publicly A full-fledged educational course has been published on the university's website: 24 lectures, practical tasks, homework assignments, and a collection of materials for self-study. The program includes modern neural network architectures, generative models, transformers, inference, and other key topics. A great opportunity to study deep learning based on the structure of a top university, free of charge and without simplifications โ€” let's learn here. https://ocw.mit.edu/courses/6-7960-deep-learning-fall-2024/resources/lecture-videos/ tags: #python #deeplearning โžก @codeprogrammer

Repost from Machine Learning
๐Ÿ“Œ Your First 90 Days as a Data Scientist ๐Ÿ—‚ Category: DATA SCIENCE ๐Ÿ•’ Date: 2026-02-14 | โฑ๏ธ Read time: 8 min read A practica
๐Ÿ“Œ Your First 90 Days as a Data Scientist ๐Ÿ—‚ Category: DATA SCIENCE ๐Ÿ•’ Date: 2026-02-14 | โฑ๏ธ Read time: 8 min read A practical onboarding checklist for building trust, business fluency, and data intuition #DataScience #AI #Python

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Design patterns are proven solutions to common problems in development. If you've ever found yourself constantly writing the
Design patterns are proven solutions to common problems in development. If you've ever found yourself constantly writing the same thing when creating objects or struggling with managing different types of objects, then the factory pattern might be exactly what you need. In this tutorial: https://www.freecodecamp.org/news/how-to-use-the-factory-pattern-in-python-a-practical-guide/ you'll learn what a factory is, why it's useful, and how to implement it in #Python. We'll gather practical examples that will show when and how to apply this pattern in real tasks. The code can be found on #GitHub https://github.com/balapriyac/python-basics/tree/main/design-patterns/factory https://t.me/CodeProgrammer

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Repost from Data Analytics
These 9 lectures from Stanford are a pure goldmine for anyone wanting to learn and understand LLMs in depth Lecture 1 - Trans
These 9 lectures from Stanford are a pure goldmine for anyone wanting to learn and understand LLMs in depth Lecture 1 - Transformer: https://lnkd.in/dGnQW39t Lecture 2 - Transformer-Based Models & Tricks: https://lnkd.in/dT_VEpVH Lecture 3 - Tranformers & Large Language Models: https://lnkd.in/dwjjpjaP Lecture 4 - LLM Training: https://lnkd.in/dSi_xCEN Lecture 5 - LLM tuning: https://lnkd.in/dUK5djpB Lecture 6 - LLM Reasoning: https://lnkd.in/dAGQTNAM Lecture 7 - Agentic LLMs: https://lnkd.in/dWD4j7vm Lecture 8 - LLM Evaluation: https://lnkd.in/ddxE5zvb Lecture 9 - Recap & Current Trends: https://lnkd.in/dGsTd8jN Start understanding #LLMs in depth from the experts. Go through each step-by-step video. https://t.me/DataAnalyticsX ๐Ÿ”—

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