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:Machine Learning with Python Suriya227 Texnologiyalar & Aralashmalar3 343...

📈 Telegram kanali Machine Learning analitikasi

Machine Learning (@machinelearning9) Ingliz til segmentidagi kanali faol ishtirokchi. Hozirda hamjamiyat 40 265 obunachidan iborat bo'lib, Texnologiyalar & Aralashmalar toifasida 3 343-o'rinni va Suriya mintaqasida 227-o'rinni egallagan.

📊 Auditoriya ko‘rsatkichlari va dinamika

невідомо sanasidan buyon loyiha tez o‘sib, 40 265 obunachiga ega bo‘ldi.

06 Iyul, 2026 dagi oxirgi ma’lumotlarga ko‘ra kanal barqaror faollikka ega. Oxirgi 30 kunda obunachilar soni 336 ga, so‘nggi 24 soatda esa -4 ga o‘zgardi va umumiy qamrov yuqori darajada qolmoqda.

Tasdiqlash holati: Tasdiqlanmagan
Jalb etish (ER): Auditoriya o‘rtacha 2.25% darajada jalb etiladi. Nashrdan keyingi dastlabki 24 soatda kontent odatda umumiy obunachilar sonining 1.88% ini tashkil etuvchi reaksiyalarni to‘playdi.
Post qamrovi: Har bir post o‘rtacha 906 marta ko‘riladi; birinchi sutkada odatda 758 ta ko‘rish yig‘iladi.
Reaksiyalar va o‘zaro ta’sir: Auditoriya faol: har bir postga o‘rtacha 3 ta reaksiya keladi.
Tematik yo‘nalishlar: Kontent distance, insidead, gpu, learning, degree kabi asosiy mavzularga jamlangan.

📝 Tavsif va kontent siyosati

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 07 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.

40 265

Obunachilar

-424 soatlar

+917 kunlar

+33630 kunlar

906

Post ko'rishlar

~ 75824 soatlar

~ 1 03748 soatlar

2.25%

Muloqot nisbati

~ 3

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40 265

📌 Introducing n-Step Temporal-Difference Methods 🗂 Category: MACHINE LEARNING 🕒 Date: 2024-12-29 | ⏱️ Read time: 10 min read Dissecting “Reinforcement Learning” by Richard S. Sutton with custom Python implementations, Episode V

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📌 I Combined the Blockchain and AI to Generate Art. Here’s What Happened Next. 🗂 Category: BLOCKCHAIN 🕒 Date: 2024-12-30 | ⏱️ Read time: 8 min read Using LLMs to create artistic representations of data

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📌 How to Build a Graph RAG App 🗂 Category: 🕒 Date: 2024-12-30 | ⏱️ Read time: 30 min read The accompanying code for the app and notebook are here. Knowledge graphs (KGs) and Large Language…

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📌 How to Build a Resume Optimizer with AI 🗂 Category: 🕒 Date: 2024-12-30 | ⏱️ Read time: 7 min read Step-by-step guide with example Python code

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📌 Mastering Model Uncertainty: Thresholding Techniques in Deep Learning 🗂 Category: DATA SCIENCE 🕒 Date: 2024-12-30 | ⏱️ Read time: 7 min read A few words on thresholding, the softmax activation function, introducing an extra label, and considerations…

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📌 From Default Python Line Chart to Journal-Quality Infographics 🗂 Category: ANALYTICS 🕒 Date: 2024-12-30 | ⏱️ Read time: 3 min read Transform boring default Matplotlib line charts into stunning, customized visualizations

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📌 The Key to Smarter Models: Tracking Feature Histories 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-12-31 | ⏱️ Read time: 10 min read Capture context and improve predictions with historical data

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📌 The Math Behind In-Context Learning 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2024-12-31 | ⏱️ Read time: 6 min read From attention to gradient descent: unraveling how transformers learn from examples

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📌 Creating SMOTE Oversampling from Scratch 🗂 Category: DATA SCIENCE 🕒 Date: 2024-12-31 | ⏱️ Read time: 8 min read A Python tutorial on how to implement oversampling and how to make custom variations

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📌 Top 12 Skills Data Scientists Need to Succeed in 2025 🗂 Category: CAREER ADVICE 🕒 Date: 2024-12-31 | ⏱️ Read time: 27 min read It’s (not) all about LLMs and AI tools

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📌 Multi-Agentic RAG with Hugging Face Code Agents 🗂 Category: 🕒 Date: 2024-12-31 | ⏱️ Read time: 80 min read Using Qwen2.5-7B-Instruct powered code agents to create a local, open source, multi-agentic RAG system

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📌 Chi-Squared Test: Comparing Variations Through Soccer 🗂 Category: DATA SCIENCE 🕒 Date: 2025-01-01 | ⏱️ Read time: 13 min read Understanding Different Types of Chi-Squared Tests: A/B Testing for Data Science Series (8)

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📌 Transforming Data into Solutions: Building a Smart App with Python and AI 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2025-01-01 | ⏱️ Read time: 13 min read Some financial analysts worry that artificial intelligence may not justify the massive investments being made…

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📌 Mastering Sensor Fusion: Color Image Obstacle Detection with KITTI Data – Part 2 🗂 Category: DEEP LEARNING 🕒 Date: 2025-01-01 | ⏱️ Read time: 26 min read How to use Color Image data for object detection in the context of obstacle detection

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📌 Scaling Statistics: Incremental Standard Deviation in SQL with dbt 🗂 Category: DATA SCIENCE 🕒 Date: 2025-01-01 | ⏱️ Read time: 7 min read Why scan yesterday’s data when you can increment today’s?

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📌 AI-Powered Information Extraction and Matchmaking 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2025-01-01 | ⏱️ Read time: 34 min read Developing an application for extracting key profile information from CVs and recommending jobs aligned with…

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📌 Mastering the Basics: How Linear Regression Unlocks the Secrets of Complex Models 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2025-01-01 | ⏱️ Read time: 12 min read Full explanation on Linear Regression and how it learns

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📌 5 Simple Projects to Start Today: A Learning Roadmap for Data Engineering 🗂 Category: DATA ENGINEERING 🕒 Date: 2025-01-02 | ⏱️ Read time: 11 min read Start with 5 practical projects to lay the foundation for your data engineering roadmap.

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📌 How to Process 10k Images in Seconds 🗂 Category: DATA SCIENCE 🕒 Date: 2025-01-02 | ⏱️ Read time: 7 min read Efficient image operations with multiprocessing in Python