Machine Learning with Python
Open in Telegram
Learn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers. Admin: @HusseinSheikho || @Hussein_Sheikho
Show more
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 68 137 subscribers, ranking 2 366 in the Education category and 4 740 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 68 137 subscribers.
According to the latest data from 30 August, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 86 over the last 30 days and by 27 over the last 24 hours, overall reach remains high.
- Verification status: Not verified
- Engagement rate (ER): The average audience engagement rate is 4.07%. Within the first 24 hours after publication, content typically collects 1.52% reactions from the total number of subscribers.
- Post reach: On average, each post receives 2 775 views. Within the first day, a publication typically gains 1 037 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 31 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.
68 137
Subscribers
+2724 hours
-297 days
+8630 days
Posts Archive
Ever wondered how much smarter your workflow could be with AI?
Meet Padma AI β your personal Telegram bot that makes work faster, easier, smarter.
Try out the AI assistant everyoneβs talking about now β and see how much more you can do in a day.
Donβt miss your edge β join Padma AI and upgrade your routine!
#ad InsideAds
πΊ 12 comprehensive playlists to master
β¬
οΈ machine learning, deep learning, and GenAI!
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
βββββββββββββ
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
Ever felt lost between laughter and deep late-night thoughts? π«πππ πππππ π©Ά is where youβll find funny memes, relatable quotes, music to match your mood, and those random thoughts you never share out loud. Itβs the corner of Telegram where real feels meet good vibes. Curious yet? Join us here to take a break for yourself.
#ad InsideAds
I never thought 2 simple signals a day could change how I trade. Yesterday, I followed one tipβand watched my gold trade explode. Want to know what happened next? The real results are hidden right here. Donβt let them pass you by.
#ad InsideAds.
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.
β
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
β
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
β
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
β
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‘
Start small and build steady income: learn the basics inside the app, earn your first tokens, and unlock higher rewards as you progress. Bring friends later to multiply results without extra risk.
Start now!
#ad InsideAds
Think crypto mining is just for whales? Discover how anyone can earn tokens and unlock upgrades and artifacts with Padma Web3βs play-to-earn ecosystem. Boost your mana, invite friends, and turn your time into real rewards β no special equipment needed. Curious about the next big thing? See what everyone is mining right now.
Start now!
#ad InsideAds
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
Awesome interactive textbook on probability theory and statistics
Inside are clear visualizations, interactive elements, and minimal dry theory. You can tweak distributions, sample datasets, play with confidence intervals, and clearly see how it all works
Get it here, I recommend opening it on a desktop
https://seeing-theory.brown.edu/
π @DataScienceM
Repost from Machine Learning
π Extracting Structured Vehicle Data from Images
π Category:
π Date: 2025-01-27 | β±οΈ Read time: 10 min read
Build an Automated Vehicle Documentation System that Extracts Structured Information from Images, using OpenAI API,β¦
Awesome interactive textbook on probability theory and statistics
Inside are clear visualizations, interactive elements, and minimal dry theory. You can tweak distributions, sample datasets, play with confidence intervals, and clearly see how it all works
Get it here, I recommend opening it on a desktop
https://seeing-theory.brown.edu/
π @DataScienceM
Repost from Machine Learning
Awesome interactive textbook on probability theory and statistics
Inside are clear visualizations, interactive elements, and minimal dry theory. You can tweak distributions, sample datasets, play with confidence intervals, and clearly see how it all works
Get it here, I recommend opening it on a desktop
https://seeing-theory.brown.edu/
π @DataScienceM
I spent years chasing success until I found the 7 daily habits no one talks aboutβnow everythingβs changed for me. Most people miss the real secret. See what youβve been overlooking: Success Tips π₯ | InsideAds
Repost from Machine Learning
π How to Build a Genetic Algorithm from Scratch in Python
π Category: DATA SCIENCE
π Date: 2024-08-30 | β±οΈ Read time: 16 min read
A complete walkthrough on how one can build a Genetic Algorithm from scratch in Python,β¦
Repost from Github Top Repositories
Python library RetinaFace for face detection and working with key points (eyes, nose, mouth)
Supports face alignment, easily installed via
pip install retina-face, and works based on deep models from the insightface project.
An excellent tool for tasks in computer vision and face recognition.
Usage examples:
from retinaface import RetinaFace
resp = RetinaFace.detect_faces("img1.jpg")
print(resp)
{
"face_1": {
"score": 0.9993440508842468,
"facial_area": [155, 81, 434, 443],
"landmarks": {
"right_eye": [257.82974, 209.64787],
"left_eye": [374.93427, 251.78687],
"nose": [303.4773, 299.91144],
"mouth_right": [228.37329, 338.73193],
"mouth_left": [320.21982, 374.58798]
}
}
}