Machine Learning
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Real Machine Learning — simple, practical, and built on experience. Learn step by step with clear explanations and working code. Admin: @HusseinSheikho || @Hussein_Sheikho
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Tarmoq:📈 Telegram kanali Machine Learning analitikasi
Machine Learning (@machinelearning9) Ingliz til segmentidagi kanali faol ishtirokchi. Hozirda hamjamiyat 40 715 obunachidan iborat bo'lib, Texnologiyalar & Aralashmalar toifasida 3 246-o'rinni va Suriya mintaqasida 218-o'rinni egallagan.
📊 Auditoriya ko‘rsatkichlari va dinamika
невідомо sanasidan buyon loyiha tez o‘sib, 40 715 obunachiga ega bo‘ldi.
30 Iyul, 2026 dagi oxirgi ma’lumotlarga ko‘ra kanal barqaror faollikka ega. Oxirgi 30 kunda obunachilar soni 523 ga, so‘nggi 24 soatda esa 22 ga o‘zgardi va umumiy qamrov yuqori darajada qolmoqda.
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- Jalb etish (ER): Auditoriya o‘rtacha 4.66% darajada jalb etiladi. Nashrdan keyingi dastlabki 24 soatda kontent odatda umumiy obunachilar sonining 1.80% ini tashkil etuvchi reaksiyalarni to‘playdi.
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Muallif resursni shaxsiy fikrni ifoda etish maydoni sifatida ta’riflaydi:
“Real Machine Learning — simple, practical, and built on experience.
Learn step by step with clear explanations and working code.
Admin: @HusseinSheikho || @Hussein_Sheikho”
Yuqori yangilanish chastotasi (oxirgi ma’lumot 31 Iyul, 2026 da olingan) sababli kanal doimo dolzarb va katta qamrovli bo‘lib qoladi. Analitika auditoriya kontent bilan faol hamkorlik qilishini, uni Texnologiyalar & Aralashmalar toifasidagi muhim ta’sir nuqtasiga aylantirishini ko‘rsatadi.
40 715
Obunachilar
+2224 soatlar
+1437 kunlar
+52330 kunlar
Postlar arxiv
40 715
📌 Context Engineering as Your Competitive Edge
🗂 Category: LARGE LANGUAGE MODELS
🕒 Date: 2026-03-01 | ⏱️ Read time: 13 min read
If you have both unique domain expertise and know how to make it usable to…
#DataScience #AI #Python
40 715
📌 Zero-Waste Agentic RAG: Designing Caching Architectures to Minimize Latency and LLM Costs at Scale
🗂 Category: LARGE LANGUAGE MODELS
🕒 Date: 2026-03-01 | ⏱️ Read time: 19 min read
Reducing LLM costs by 30% with validation-aware, multi-tier caching
#DataScience #AI #Python
40 715
📌 Scaling ML Inference on Databricks: Liquid or Partitioned? Salted or Not?
🗂 Category: DATA ENGINEERING
🕒 Date: 2026-02-28 | ⏱️ Read time: 11 min read
A case study on techniques to maximize your clusters
#DataScience #AI #Python
40 715
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40 715
📌 Claude Skills and Subagents: Escaping the Prompt Engineering Hamster Wheel
🗂 Category: AGENTIC AI
🕒 Date: 2026-02-28 | ⏱️ Read time: 17 min read
How reusable, lazy-loaded instructions solve the context bloat problem in AI-assisted development.
#DataScience #AI #Python
40 715
📌 The Gap Between Junior and Senior Data Scientists Isn’t Code
🗂 Category: DATA SCIENCE
🕒 Date: 2026-02-27 | ⏱️ Read time: 6 min read
Why my obsession with complex algorithms was actually holding my career back.
#DataScience #AI #Python
40 715
📌 Generative AI, Discriminative Human
🗂 Category: ARTIFICIAL INTELLIGENCE
🕒 Date: 2026-02-27 | ⏱️ Read time: 14 min read
How to think critically about AI in an ocean of hype
#DataScience #AI #Python
40 715
📌 Stop Asking if a Model Is Interpretable
🗂 Category: ARTIFICIAL INTELLIGENCE
🕒 Date: 2026-02-27 | ⏱️ Read time: 6 min read
Start asking what question the explanation should answer.
#DataScience #AI #Python
40 715
📌 Coding the Pong Game from Scratch in Python
🗂 Category: PROGRAMMING
🕒 Date: 2026-02-27 | ⏱️ Read time: 18 min read
Implementing the classic Pong game in Python using OOP and Turtle
#DataScience #AI #Python
40 715
📌 Take a Deep Dive into Filtering in DAX
🗂 Category: DATA ANALYSIS
🕒 Date: 2026-02-26 | ⏱️ Read time: 13 min read
Have you ever wondered what happens when you apply a filter in a DAX expression?…
#DataScience #AI #Python
40 715
📌 Designing Data and AI Systems That Hold Up in Production
🗂 Category: AUTHOR SPOTLIGHTS
🕒 Date: 2026-02-26 | ⏱️ Read time: 6 min read
A system-level perspective on architecture, agents, and responsible scale
#DataScience #AI #Python
40 715
Repost from Machine Learning with Python
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40 715
📌 Detecting and Editing Visual Objects with Gemini
🗂 Category: LLM APPLICATIONS
🕒 Date: 2026-02-26 | ⏱️ Read time: 34 min read
A practical guide to identifying, restoring, and transforming elements within your images
#DataScience #AI #Python
40 715
📌 A Generalizable MARL-LP Approach for Scheduling in Logistics
🗂 Category: MACHINE LEARNING
🕒 Date: 2026-02-26 | ⏱️ Read time: 17 min read
Part 1. Hybrid Solution for Dynamic Vehicle Routing — Context and Architecture
#DataScience #AI #Python
40 715
📌 Breaking the Host Memory Bottleneck: How Peer Direct Transformed Gaudi’s Cloud Performance
🗂 Category: ARTIFICIAL INTELLIGENCE
🕒 Date: 2026-02-25 | ⏱️ Read time: 9 min read
Engineering RDMA-like performance over cloud host NICs using libfabric, DMA-BUF, and HCCL to restore distributed…
#DataScience #AI #Python
40 715
📌 Scaling Feature Engineering Pipelines with Feast and Ray
🗂 Category: MACHINE LEARNING
🕒 Date: 2026-02-25 | ⏱️ Read time: 11 min read
Utilizing feature stores like Feast and distributed compute frameworks like Ray in production machine learning systems
#DataScience #AI #Python
40 715
📌 How to Define the Modeling Scope of an Internal Credit Risk Model
🗂 Category: DATA SCIENCE
🕒 Date: 2026-02-25 | ⏱️ Read time: 9 min read
Dataset construction for Internal Ratings-Based (IRB) Probability of Default (PD) models
#DataScience #AI #Python
40 715
📌 Aliasing in Audio, Easily Explained: From Wagon Wheels to Waveforms
🗂 Category: MACHINE LEARNING
🕒 Date: 2026-02-25 | ⏱️ Read time: 21 min read
Understanding the foundational distortion of digital audio from first principles, with worked examples and visual…
#DataScience #AI #Python
40 715
📌 Decisioning at the Edge: Policy Matching at Scale
🗂 Category: DATA SCIENCE
🕒 Date: 2026-02-24 | ⏱️ Read time: 13 min read
Policy-to-Agency Optimization with PuLP
#DataScience #AI #Python
40 715
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