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:π 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 68 118 subscribers, ranking 2 372 in the Education category and 4 808 in the India region.
π Audience metrics and dynamics
Since its creation on Π½Π΅Π²ΡΠ΄ΠΎΠΌΠΎ, the project has demonstrated rapid growth, gathering an audience of 68 118 subscribers.
According to the latest data from 27 August, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 112 over the last 30 days and by 8 over the last 24 hours, overall reach remains high.
- Verification status: Not verified
- Engagement rate (ER): The average audience engagement rate is 4.52%. Within the first 24 hours after publication, content typically collects 1.90% reactions from the total number of subscribers.
- Post reach: On average, each post receives 3 077 views. Within the first day, a publication typically gains 1 291 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.
π Description and content policy
The author describes the resource as a platform for expressing subjective opinions:
βLearn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers.
Admin: @HusseinSheikho || @Hussein_Sheikhoβ
Thanks to the high frequency of updates (latest data received on 28 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.
68 118
Subscribers
+824 hours
-727 days
+11230 days
Posts Archive
π New Edge for Polymarket Traders: Oracle Lag Sniper
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NumPy, SciPy, Matplotlib & Pandas A-Z: Machine Learning
NumPy | SciPy | Matplotlib | Pandas | Machine Learning | Data Science | Deep Learning | Pre-Machine Learning Analysis...
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πΎ TensorTonic β a platform with 200+ algorithms and tasks for ML developers
What's inside:
βΆοΈ Analysis of research and step-by-step reproduction of model architectures;
βΆοΈ Explanation of topics and concepts with interactive visualizations;
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A great option to hone your ML skills in the evening β‘
https://www.tensortonic.com/
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Python Machine Learning - Practice Questions 2026
Python Machine Learning 120 unique high-quality test questions with detailed explanations!...
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π Language: English (US)
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Python File Handling - Practice Questions 2026
Python File Handling 120 unique high-quality test questions with detailed explanations!...
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Classical filters & convolution: The heart of computer vision
Before Deep Learning exploded onto the scene, traditional computer vision centered on filters. Filters were small, hand-engineered matrices that you convolved with an image to detect specific features like edges, corners, or textures. In this article, we will dive into the details of classical filters and convolution operation - how they work, why they matter, and how to implement them.
More: https://www.vizuaranewsletter.com/p/classical-filters-and-convolution
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Python File Handling - Practice Questions 2026
Python File Handling 120 unique high-quality test questions with detailed explanations!...
π· Category: it-and-software
π Language: English (US)
π₯ Students: 543 students
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π± Python enthusiasts, this is for you β 15 BEST REPOSITORIES on GitHub for learning Python
βΆοΈ Awesome Python β https://github.com/vinta/awesome-python
β the largest and most authoritative collection of frameworks, libraries, and resources for Python β a must-save
βΆοΈ TheAlgorithms/Python β https://github.com/TheAlgorithms/Python
β a huge collection of algorithms and data structures written in Python
βΆοΈ Project-Based-Learning β https://github.com/practical-tutorials/project-based-learning
β learning Python (and not only) through real projects
βΆοΈ Real Python Guide β https://github.com/realpython/python-guide
β a high-quality guide to the Python ecosystem, tools, and best practices
βΆοΈ Materials from Real Python β https://github.com/realpython/materials
β a collection of code and projects for Real Python articles and courses
βΆοΈ Learn Python β https://github.com/trekhleb/learn-python
β a reference with explanations, examples, and exercises
βΆοΈ Learn Python 3 β https://github.com/jerry-git/learn-python3
β a convenient guide to modern Python 3 with tasks
βΆοΈ Python Reference β https://github.com/rasbt/python_reference
β cheat sheets, scripts, and useful tips from one of the most respected Python authors
βΆοΈ 30-Days-Of-Python β https://github.com/Asabeneh/30-Days-Of-Python
β a 30-day challenge: from syntax to more complex topics
βΆοΈ Python Programming Exercises β https://github.com/zhiwehu/Python-programming-exercises
β 100+ Python tasks with answers
βΆοΈ Coding Problems β https://github.com/MTrajK/coding-problems
β tasks on algorithms and data structures, including for preparation for interviews
βΆοΈ Projects β https://github.com/karan/Projects
β a list of ideas for pet projects (not just Python). Great for practice
βΆοΈ 100-Days-Of-ML-Code β https://github.com/Avik-Jain/100-Days-Of-ML-Code
β machine learning in Python in the format of a challenge
βΆοΈ 30-Seconds-of-Python β https://github.com/30-seconds/30-seconds-of-python
β useful snippets and tricks for everyday tasks
βΆοΈ Geekcomputers/Python β https://github.com/geekcomputers/Python
β various scripts: from working with the network to automation tasks
React β₯οΈ for more posts like this π
π Harvard has released a textbook on ML systems β from autograd to production
Not just another "what is a neural network" course β this is about how to build combat-ready ML systems around models.
What's inside:
βΆοΈ Building autograd, optimizers, attention, and mini-PyTorch from scratch;
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