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 68 117 subscribers, ranking 2 375 in the Education category and 4 809 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 68 117 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.
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 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.
68 117
Subscribers
-1424 hours
-667 days
+12030 days
Posts Archive
AI is moving fast. Accountability is not.
That is why we built the open source core of Forkit Dev.
Forkit Dev introduces Model Passports and Agent Passports so AI systems can be tracked, verified, and understood across their lifecycle.
Open source repo:
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If you care about trustworthy AI, open source infrastructure, model lineage, or compliance ready deployment, check it out and share your thoughts.
Do you know that Python can shift sequences without slicing and creating new lists? π€
When you need to cyclically shift data, many use slicing:
data = data[-1:] + data[:-1]deque.rotate() does this at the level of the data structure and usually works more efficiently for cyclical operations. π
q.rotate(1)A negative value rotates the queue in the other direction. β¬ οΈ
q.rotate(-2)This is useful for ring buffers, task schedulers, cyclical queues, and round-robin algorithms. π
workers.rotate(-1)π₯
deque.rotate() allows you to implement cyclical data structures without manual index logic and without creating new lists. π‘
π Python Ready | #tip
#Python #Programming #Deque #CodingTips #Tech #DevCommunityDo you know that Python can shift sequences without slicing and creating new lists?
When you need to cyclically shift data, many use slicing:
data = data[-1:] + data[:-1] does this at the level of the data structure and usually works more efficiently for cyclical operations.
``python
q.rotate(1)
A negative value rotates the queue in the other direction.python q.rotate(-2)
This is useful for ring buffers, task schedulers, cyclical queues, and round-robin algorithms.python workers.rotate(-1)
`
π₯ `deque.rotate()` allows you to implement cyclical data structures without manual index logic and without creating new lists.
#Python #deque #rotate #Programming #Coding #DevTipsRepost from Machine Learning
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Inside 100 real questions from interviews in product and infrastructure companies, detailed analyses with code examples and scenarios of tasks that occur in production. π»ποΈ Not "guess the program's output", but the mechanics on which real services are built. π οΈπ
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Repost from Learn Python Coding
Cheat sheet on the basics of Python: ππ
basic syntax and language rules π
scalar types β basic data types (int, float, bool, str, NoneType) π’
datetime β working with date and time π
β°
data structures β Python data structures (list, tuple, dict, set) π
list β mutable lists for storing data collections π
tuple β immutable sequences of values π
dict (hash map) β storing data in a key-value format π
set β unique elements without order π
slicing β obtaining parts of sequences through indices and step βοΈ
module/library β connecting modules and libraries π
help functions β using help() and dir() to explore the Python API π
#Python #Coding #DataScience #Programming #Tech #DevCommunity
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reader3 πβ¨
When you want to connect an AI like Gemini to help you analyze books or content, copying text from a reader usually becomes a hassle. π©π»
Especially if you want to discuss a book by chapters. Highlighting text manually and copying it disrupts the flow and feels like a waste of time. β³π«
Yesterday, Andrzej Karpati, a well-known AI expert, released a new project to the public: reader3, which solves this problem very neatly. ππ οΈ It's a lightweight EPUB reader that allows you to read a book together with AI. π€π
Its interface is as minimalist as possible: only the necessary reading and navigation functions. ππ§ You can also manage your library through folders. πβ¨
The key feature is that it breaks an EPUB into chapters and displays the content one chapter at a time. ππ
This makes it easy to copy the needed part of the book and pass it to a large model for analysis or discussion. ππ It significantly improves the reading experience when paired with AI. ππ§
And it's very easy to get started - just run two commands via uv. β‘π οΈ As a result, it's an excellent tool for those who love reading and want to use AI as a companion for text analysis. ππ€π€
π Language: #Python 61.0%
βοΈ Stars: 1.5k
β‘οΈ Link to GitHub https://github.com/karpathy/reader3
#AI #Python #Reader3 #Tech #BookLovers #Github
https://t.me/CodeProgrammer β
Repost from Machine Learning with Python
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