Machine Learning with Python
Kanalga Telegram’da o‘tish
Learn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers. Admin: @HusseinSheikho || @Hussein_Sheikho
Ko'proq ko'rsatish
Tarmoq:📈 Telegram kanali Machine Learning with Python analitikasi
Machine Learning with Python (@codeprogrammer) Ingliz til segmentidagi kanali faol ishtirokchi. Hozirda hamjamiyat 68 137 obunachidan iborat bo'lib, Taʼlim toifasida 2 366-o'rinni va Hindiston mintaqasida 4 740-o'rinni egallagan.
📊 Auditoriya ko‘rsatkichlari va dinamika
невідомо sanasidan buyon loyiha tez o‘sib, 68 137 obunachiga ega bo‘ldi.
30 Avgust, 2026 dagi oxirgi ma’lumotlarga ko‘ra kanal barqaror faollikka ega. Oxirgi 30 kunda obunachilar soni 86 ga, so‘nggi 24 soatda esa 27 ga o‘zgardi va umumiy qamrov yuqori darajada qolmoqda.
- Tasdiqlash holati: Tasdiqlanmagan
- Jalb etish (ER): Auditoriya o‘rtacha 4.07% darajada jalb etiladi. Nashrdan keyingi dastlabki 24 soatda kontent odatda umumiy obunachilar sonining 1.52% ini tashkil etuvchi reaksiyalarni to‘playdi.
- Post qamrovi: Har bir post o‘rtacha 2 775 marta ko‘riladi; birinchi sutkada odatda 1 037 ta ko‘rish yig‘iladi.
- Reaksiyalar va o‘zaro ta’sir: Auditoriya faol: har bir postga o‘rtacha 5 ta reaksiya keladi.
- Tematik yo‘nalishlar: Kontent insidead, learning, degree, evaluation, algorithm kabi asosiy mavzularga jamlangan.
📝 Tavsif va kontent siyosati
Muallif resursni shaxsiy fikrni ifoda etish maydoni sifatida ta’riflaydi:
“Learn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers.
Admin: @HusseinSheikho || @Hussein_Sheikho”
Yuqori yangilanish chastotasi (oxirgi ma’lumot 31 Avgust, 2026 da olingan) sababli kanal doimo dolzarb va katta qamrovli bo‘lib qoladi. Analitika auditoriya kontent bilan faol hamkorlik qilishini, uni Taʼlim toifasidagi muhim ta’sir nuqtasiga aylantirishini ko‘rsatadi.
68 137
Obunachilar
+2724 soatlar
-297 kunlar
+8630 kunlar
Postlar arxiv
Ever wondered how much smarter your workflow could be with AI?
Meet Padma AI — your personal Telegram bot that makes work faster, easier, smarter.
Try out the AI assistant everyone’s talking about now — and see how much more you can do in a day.
Don’t miss your edge — join Padma AI and upgrade your routine!
#ad InsideAds
📺 12 comprehensive playlists to master
⬅️ machine learning, deep learning, and GenAI!
👨🏻💻 Each playlist is designed to be simple and understandable for beginners, and then gradually dive deeper into the topics.
😉 Machine Learning Basics (39 videos)
😉 Python for ML (9 videos)
😉 Optimization for ML (5 videos)
😉 Machine Learning with Practical Exercises (37 videos)
😉 Building Decision Trees from Scratch (13 videos)
😉 Building Neural Networks from Scratch (35 videos)
😉 Graph Neural Networks (6 videos)
😉 Computer Vision from Scratch (19 videos)
😉 Building LLM from Scratch (43 videos)
😉 Reasoning in LLMs from Scratch (22 videos)
😉 Building DeepSeek from Scratch (29 videos)
😉 Machine Learning in Production Environment (6 videos)
🌐 #Data_Science #DataScience
➖➖➖➖➖➖➖➖➖➖➖➖➖
https://t.me/CodeProgrammer ❤️
👨🏻💻 This Python library helps you extract usable data for language models from complex files like tables, images, charts, or multi-page documents.
📝 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.
✅ Works with PDFs, images, and website links.
☑️ Can chunk and process very large documents (up to 1000 pages) by itself.
✔️ Outputs both JSON and Markdown formats.
☑️ Even specifies the exact location of each section on the page.
✔️ Supports parallel and batch processing.
pip install agentic-doc
Ever felt lost between laughter and deep late-night thoughts? 𝑫𝒂𝒓𝒌 𝒍𝒊𝒈𝒉𝒕 🩶 is where you’ll find funny memes, relatable quotes, music to match your mood, and those random thoughts you never share out loud. It’s 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 tip—and watched my gold trade explode. Want to know what happened next? The real results are hidden right here. Don’t let them pass you by.
#ad InsideAds.
Repost from Machine Learning with Python
“Learn AI” is everywhere. But where do the builders actually start?
Here’s the real path, the courses, papers and repos that matter.
✅ Videos:
Everything here ⇒ https://lnkd.in/ePfB8_rk
➡️ LLM Introduction → https://lnkd.in/ernZFpvB
➡️ LLMs from Scratch - Stanford CS229 → https://lnkd.in/etUh6_mn
➡️ Agentic AI Overview →https://lnkd.in/ecpmzAyq
➡️ Building and Evaluating Agents → https://lnkd.in/e5KFeZGW
➡️ Building Effective Agents → https://lnkd.in/eqxvBg79
➡️ Building Agents with MCP → https://lnkd.in/eZd2ym2K
➡️ Building an Agent from Scratch → https://lnkd.in/eiZahJGn
✅ Courses:
All Courses here ⇒ https://lnkd.in/eKKs9ves
➡️ HuggingFace's Agent Course → https://lnkd.in/e7dUTYuE
➡️ MCP with Anthropic → https://lnkd.in/eMEnkCPP
➡️ Building Vector DB with Pinecone → https://lnkd.in/eP2tMGVs
➡️ Vector DB from Embeddings to Apps → https://lnkd.in/eP2tMGVs
➡️ Agent Memory → https://lnkd.in/egC8h9_Z
➡️ Building and Evaluating RAG apps → https://lnkd.in/ewy3sApa
➡️ Building Browser Agents → https://lnkd.in/ewy3sApa
➡️ LLMOps → https://lnkd.in/ex4xnE8t
➡️ Evaluating AI Agents → https://lnkd.in/eBkTNTGW
➡️ Computer Use with Anthropic → https://lnkd.in/ebHUc-ZU
➡️ Multi-Agent Use → https://lnkd.in/e4f4HtkR
➡️ Improving LLM Accuracy → https://lnkd.in/eVUXGT4M
➡️ Agent Design Patterns → https://lnkd.in/euhUq3W9
➡️ Multi Agent Systems → https://lnkd.in/evBnavk9
✅ Guides:
Access all ⇒ https://lnkd.in/e-GA-HRh
➡️ Google's Agent → https://lnkd.in/encAzwKf
➡️ Google's Agent Companion → https://lnkd.in/e3-XtYKg
➡️ Building Effective Agents by Anthropic → https://lnkd.in/egifJ_wJ
➡️ Claude Code Best practices → https://lnkd.in/eJnqfQju
➡️ OpenAI's Practical Guide to Building Agents → https://lnkd.in/e-GA-HRh
✅ Repos:
➡️ GenAI Agents → https://lnkd.in/eAscvs_i
➡️ Microsoft's AI Agents for Beginners → https://lnkd.in/d59MVgic
➡️ Prompt Engineering Guide → https://lnkd.in/ewsbFwrP
➡️ AI Agent Papers → https://lnkd.in/esMHrxJX
✅ Papers:
🟡 ReAct → https://lnkd.in/eZ-Z-WFb
🟡 Generative Agents → https://lnkd.in/eDAeSEAq
🟡 Toolformer → https://lnkd.in/e_Vcz5K9
🟡 Chain-of-Thought Prompting → https://lnkd.in/eRCT_Xwq
🟡 Tree of Thoughts → https://lnkd.in/eiadYm8S
🟡 Reflexion → https://lnkd.in/eggND2rZ
🟡 Retrieval-Augmented Generation Survey → https://lnkd.in/eARbqdYE
Access all ⇒ https://lnkd.in/e-GA-HRh
By: https://t.me/CodeProgrammer 🟡
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 Web3’s play-to-earn ecosystem. Boost your mana, invite friends, and turn your time into real rewards — 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)
📖 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
📌 Missing Value Imputation, Explained: A Visual Guide with Code Examples for Beginners
🗂 Category: MACHINE LEARNING
🕒 Date: 2024-08-27 | ⏱️ Read time: 13 min read
One (tiny) dataset, six imputation methods?
Repost from Data Analytics
🖥 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.
▪️ GitHub
https://t.me/addlist/8_rRW2scgfRhOTc0 ⚡️
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
👉 @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/
👉 @DataScienceM
Repost from Machine Learning
📌 Extracting Structured Vehicle Data from Images
🗂 Category:
🕒 Date: 2025-01-27 | ⏱️ Read time: 10 min read
Build an Automated Vehicle Documentation System that Extracts Structured Information from Images, using OpenAI API,…
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/
👉 @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/
👉 @DataScienceM
I spent years chasing success until I found the 7 daily habits no one talks about—now everything’s changed for me. Most people miss the real secret. See what you’ve been overlooking: Success Tips 🔥 | InsideAds
Repost from Machine Learning
📌 How to Build a Genetic Algorithm from Scratch in Python
🗂 Category: DATA SCIENCE
🕒 Date: 2024-08-30 | ⏱️ Read time: 16 min read
A complete walkthrough on how one can build a Genetic Algorithm from scratch in Python,…
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]
}
}
}