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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Tarmoq:π Telegram kanali Machine Learning with Python analitikasi
Machine Learning with Python (@codeprogrammer) Ingliz til segmentidagi kanali faol ishtirokchi. Hozirda hamjamiyat 68 471 obunachidan iborat bo'lib, TaΚΌlim toifasida 2 400-o'rinni va Hindiston mintaqasida 4 598-o'rinni egallagan.
π Auditoriya koβrsatkichlari va dinamika
Π½Π΅Π²ΡΠ΄ΠΎΠΌΠΎ sanasidan buyon loyiha tez oβsib, 68 471 obunachiga ega boβldi.
31 Oktabr, 2026 dagi oxirgi maβlumotlarga koβra kanal barqaror faollikka ega. Oxirgi 30 kunda obunachilar soni 12 ga, soβnggi 24 soatda esa -24 ga oβzgardi va umumiy qamrov yuqori darajada qolmoqda.
- Tasdiqlash holati: Tasdiqlanmagan
- Jalb etish (ER): Auditoriya oβrtacha 3.99% darajada jalb etiladi. Nashrdan keyingi dastlabki 24 soatda kontent odatda umumiy obunachilar sonining 1.46% ini tashkil etuvchi reaksiyalarni toβplaydi.
- Post qamrovi: Har bir post oβrtacha 2 735 marta koβriladi; birinchi sutkada odatda 998 ta koβrish yigβiladi.
- Reaksiyalar va oβzaro taβsir: Auditoriya faol: har bir postga oβrtacha 8 ta reaksiya keladi.
- Tematik yoβnalishlar: Kontent insidead, learning, degree, evaluation, algorithm kabi asosiy mavzularga jamlangan.
π Tavsif va kontent siyosati
Muallif resursni shaxsiy fikrni ifoda etish maydoni sifatida taβriflaydi:
βLearn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers.
Admin: @HusseinSheikho || @Hussein_Sheikhoβ
Yuqori yangilanish chastotasi (oxirgi maβlumot 01 Noyabr, 2026 da olingan) sababli kanal doimo dolzarb va katta qamrovli boβlib qoladi. Analitika auditoriya kontent bilan faol hamkorlik qilishini, uni TaΚΌlim toifasidagi muhim taβsir nuqtasiga aylantirishini koβrsatadi.
68 471
Obunachilar
-2424 soatlar
-947 kun
+1230 kun
Postlar arxiv
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πΊ 12 comprehensive playlists to master
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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
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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
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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
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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
