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 68 471 subscribers, ranking 2 400 in the Education category and 4 598 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 68 471 subscribers.
According to the latest data from 31 October, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 12 over the last 30 days and by -24 over the last 24 hours, overall reach remains high.
- Verification status: Not verified
- Engagement rate (ER): The average audience engagement rate is 3.99%. Within the first 24 hours after publication, content typically collects 1.46% reactions from the total number of subscribers.
- Post reach: On average, each post receives 2 735 views. Within the first day, a publication typically gains 998 views.
- Reactions and interaction: The audience actively supports content: the average number of reactions per post is 8.
- 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 01 November, 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.
68 471
Subscribers
-2424 hours
-947 days
+1230 days
Posts Archive
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πΊ 12 comprehensive playlists to master
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π¨π»βπ» Each playlist is designed to be simple and understandable for beginners, and then gradually dive deeper into the topics.
π Machine Learning Basics (39 videos)
π Python for ML (9 videos)
π Optimization for ML (5 videos)
π Machine Learning with Practical Exercises (37 videos)
π Building Decision Trees from Scratch (13 videos)
π Building Neural Networks from Scratch (35 videos)
π Graph Neural Networks (6 videos)
π Computer Vision from Scratch (19 videos)
π Building LLM from Scratch (43 videos)
π Reasoning in LLMs from Scratch (22 videos)
π Building DeepSeek from Scratch (29 videos)
π Machine Learning in Production Environment (6 videos)
π #Data_Science #DataScience
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https://t.me/CodeProgrammer β€οΈ
π¨π»βπ» This Python library helps you extract usable data for language models from complex files like tables, images, charts, or multi-page documents.
π The idea of Agentic Document Extraction is that unlike common methods like OCR that only read text, it can also understand the structure and relationships between different parts of the document. For example, it understands which title belongs to which table or image.
β
Works with PDFs, images, and website links.
βοΈ Can chunk and process very large documents (up to 1000 pages) by itself.
βοΈ Outputs both JSON and Markdown formats.
βοΈ Even specifies the exact location of each section on the page.
βοΈ Supports parallel and batch processing.
pip install agentic-doc
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Repost from Machine Learning with Python
βLearn AIβ is everywhere. But where do the builders actually start?
Hereβs the real path, the courses, papers and repos that matter.
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Videos:
Everything here β https://lnkd.in/ePfB8_rk
β‘οΈ LLM Introduction β https://lnkd.in/ernZFpvB
β‘οΈ LLMs from Scratch - Stanford CS229 β https://lnkd.in/etUh6_mn
β‘οΈ Agentic AI Overview βhttps://lnkd.in/ecpmzAyq
β‘οΈ Building and Evaluating Agents β https://lnkd.in/e5KFeZGW
β‘οΈ Building Effective Agents β https://lnkd.in/eqxvBg79
β‘οΈ Building Agents with MCP β https://lnkd.in/eZd2ym2K
β‘οΈ Building an Agent from Scratch β https://lnkd.in/eiZahJGn
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Courses:
All Courses here β https://lnkd.in/eKKs9ves
β‘οΈ HuggingFace's Agent Course β https://lnkd.in/e7dUTYuE
β‘οΈ MCP with Anthropic β https://lnkd.in/eMEnkCPP
β‘οΈ Building Vector DB with Pinecone β https://lnkd.in/eP2tMGVs
β‘οΈ Vector DB from Embeddings to Apps β https://lnkd.in/eP2tMGVs
β‘οΈ Agent Memory β https://lnkd.in/egC8h9_Z
β‘οΈ Building and Evaluating RAG apps β https://lnkd.in/ewy3sApa
β‘οΈ Building Browser Agents β https://lnkd.in/ewy3sApa
β‘οΈ LLMOps β https://lnkd.in/ex4xnE8t
β‘οΈ Evaluating AI Agents β https://lnkd.in/eBkTNTGW
β‘οΈ Computer Use with Anthropic β https://lnkd.in/ebHUc-ZU
β‘οΈ Multi-Agent Use β https://lnkd.in/e4f4HtkR
β‘οΈ Improving LLM Accuracy β https://lnkd.in/eVUXGT4M
β‘οΈ Agent Design Patterns β https://lnkd.in/euhUq3W9
β‘οΈ Multi Agent Systems β https://lnkd.in/evBnavk9
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Guides:
Access all β https://lnkd.in/e-GA-HRh
β‘οΈ Google's Agent β https://lnkd.in/encAzwKf
β‘οΈ Google's Agent Companion β https://lnkd.in/e3-XtYKg
β‘οΈ Building Effective Agents by Anthropic β https://lnkd.in/egifJ_wJ
β‘οΈ Claude Code Best practices β https://lnkd.in/eJnqfQju
β‘οΈ OpenAI's Practical Guide to Building Agents β https://lnkd.in/e-GA-HRh
β
Repos:
β‘οΈ GenAI Agents β https://lnkd.in/eAscvs_i
β‘οΈ Microsoft's AI Agents for Beginners β https://lnkd.in/d59MVgic
β‘οΈ Prompt Engineering Guide β https://lnkd.in/ewsbFwrP
β‘οΈ AI Agent Papers β https://lnkd.in/esMHrxJX
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Papers:
π‘ ReAct β https://lnkd.in/eZ-Z-WFb
π‘ Generative Agents β https://lnkd.in/eDAeSEAq
π‘ Toolformer β https://lnkd.in/e_Vcz5K9
π‘ Chain-of-Thought Prompting β https://lnkd.in/eRCT_Xwq
π‘ Tree of Thoughts β https://lnkd.in/eiadYm8S
π‘ Reflexion β https://lnkd.in/eggND2rZ
π‘ Retrieval-Augmented Generation Survey β https://lnkd.in/eARbqdYE
Access all β https://lnkd.in/e-GA-HRh
By: https://t.me/CodeProgrammer π‘
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Python Cheat Sheet (very very important)
π Compact Python cheat sheet covering setup, syntax, data types, variables, strings, control flow, functions, classes, errors, and I/O.
Link: https://discord.com/channels/942740928706281524/1423994784720359567/1424711790947864669
Big surprise in our channels on Discord
https://discord.gg/PGZku7DrSz
Repost from Machine Learning
π Missing Value Imputation, Explained: A Visual Guide with Code Examples for Beginners
π Category: MACHINE LEARNING
π Date: 2024-08-27 | β±οΈ Read time: 13 min read
One (tiny) dataset, six imputation methods?
Repost from Data Analytics
π₯ Extremely useful collection of 800+ SQL questions frequently asked in interviews.
It also includes tasks for self-study and many examples.
The collection is perfect for those who want to improve their SQL skills, refresh their knowledge, and test themselves.
βͺοΈ GitHub
https://t.me/addlist/8_rRW2scgfRhOTc0 β‘οΈ
Great find for developers: free cheat sheets on Deep Learning and PyTorch
A detailed guide to creating and training neural networks - link
Basic principles and practice of working with PyTorch - link
π @CODEPROGRAMMER
