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

Ko'proq ko'rsatish

Tarmoq:Machine Learning with Python Suriya227 Texnologiyalar & Aralashmalar3 365...

📈 Telegram kanali Machine Learning analitikasi

Machine Learning (@machinelearning9) Ingliz til segmentidagi kanali faol ishtirokchi. Hozirda hamjamiyat 40 202 obunachidan iborat bo'lib, Texnologiyalar & Aralashmalar toifasida 3 365-o'rinni va Suriya mintaqasida 227-o'rinni egallagan.

📊 Auditoriya ko‘rsatkichlari va dinamika

невідомо sanasidan buyon loyiha tez o‘sib, 40 202 obunachiga ega bo‘ldi.

02 Iyul, 2026 dagi oxirgi ma’lumotlarga ko‘ra kanal barqaror faollikka ega. Oxirgi 30 kunda obunachilar soni 343 ga, so‘nggi 24 soatda esa 10 ga o‘zgardi va umumiy qamrov yuqori darajada qolmoqda.

Tasdiqlash holati: Tasdiqlanmagan
Jalb etish (ER): Auditoriya o‘rtacha 1.99% darajada jalb etiladi. Nashrdan keyingi dastlabki 24 soatda kontent odatda umumiy obunachilar sonining 2.28% ini tashkil etuvchi reaksiyalarni to‘playdi.
Post qamrovi: Har bir post o‘rtacha 800 marta ko‘riladi; birinchi sutkada odatda 915 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 03 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.

40 202

Obunachilar

+1024 soatlar

+837 kunlar

+34330 kunlar

800

Post ko'rishlar

~ 91524 soatlar

~ 1 23548 soatlar

1.99%

Muloqot nisbati

~ 3

Kuniga postlar

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Postlar arxiv

40 202

📌 Essential Guide to Continuous Ranked Probability Score (CRPS) for Forecasting 🗂 Category: DATA SCIENCE 🕒 Date: 2024-08-31 | ⏱️ Read time: 7 min read Learn how to evaluate probabilistic forecasts and how CRPS relates to other metrics

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📌 How to Deal with Time Series Outliers 🗂 Category: DATA SCIENCE 🕒 Date: 2024-08-31 | ⏱️ Read time: 6 min read Understanding, detecting and replacing outliers in time series

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📌 Data Scientists Can’t Excel in Python Without Mastering These Functions 🗂 Category: DATA SCIENCE 🕒 Date: 2024-08-31 | ⏱️ Read time: 11 min read Introduction of Python’s core functions, use cases, scripts, and underlying mechanisms

40 202

📌 Streamline Property Data Management: Advanced Data Extraction & Retrieval with Indexify 🗂 Category: 🕒 Date: 2024-08-31 | ⏱️ Read time: 15 min read A Step-by-Step Guide to Document Querying with Indexify

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📌 The DIY Path to AI Product Management: Picking a Starter Project 🗂 Category: CHATGPT 🕒 Date: 2024-08-31 | ⏱️ Read time: 8 min read Building real-world skills through hands-on trial and error.

40 202

📌 Building Scalable Data Platforms 🗂 Category: ANALYTICS 🕒 Date: 2024-09-01 | ⏱️ Read time: 14 min read Data Mesh trends in data platform design

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📌 Training AI Models on CPU 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-09-01 | ⏱️ Read time: 16 min read Revisiting CPU for ML in an Era of GPU Scarcity

40 202

📌 Create Your Own Meal Planner Using ChatGPT 🗂 Category: CHATGPT 🕒 Date: 2024-09-02 | ⏱️ Read time: 19 min read A brief guide to prompt engineering

40 202

📌 Mathematics of Love: Optimizing a Dining-Room Seating Arrangement for Weddings with Python 🗂 Category: DATA SCIENCE 🕒 Date: 2024-09-02 | ⏱️ Read time: 19 min read Solving the Restricted Quadratic Multi-Knapsack Problem (RQMKP) with mathematical programming and Python

40 202

📌 An Easy Way to Remove Tourists from Photos 🗂 Category: PYTHON 🕒 Date: 2024-09-02 | ⏱️ Read time: 9 min read Image cleanup with Python, PIL, and OpenCV

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📌 Encoding Categorical Data, Explained: A Visual Guide with Code Example for Beginners 🗂 Category: DATA SCIENCE 🕒 Date: 2024-09-02 | ⏱️ Read time: 10 min read Six ways of matchmaking categories and numbers

40 202

📌 Use R to build Clinical Flowchart with shinyCyJS 🗂 Category: 🕒 Date: 2024-09-03 | ⏱️ Read time: 6 min read Customizable R package for Graph / Network visualization

40 202

📌 Subway Route Data Extraction with Overpass API: A Step-by-Step Guide 🗂 Category: DATA SCIENCE 🕒 Date: 2024-09-03 | ⏱️ Read time: 11 min read Simplify Geodata Extraction from OpenStreetMaps via the Overpass API

40 202

📌 Information in Noise 🗂 Category: DATA SCIENCE 🕒 Date: 2024-09-03 | ⏱️ Read time: 4 min read Two Techniques for Visualizing Many Time-Series at Once

40 202

📌 5 Pillars for a Hyper-Optimized AI Workflow 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2024-09-03 | ⏱️ Read time: 8 min read A gentle introduction to a methodology for creating production-ready, extensible & highly optimized AI workflows

40 202

📌 Line-By-Line, Let’s Reproduce GPT-2: Section 3 – Training 🗂 Category: 🕒 Date: 2024-09-03 | ⏱️ Read time: 20 min read This blog post will go line-by-line through the code in Section 3 of Andrej Karpathy’s…

40 202

📌 Using Generative AI To Get Insights From Disorderly Data 🗂 Category: 🕒 Date: 2024-09-03 | ⏱️ Read time: 41 min read Best practices for using Large Language Models to extract actionable insights even with poor metadata

40 202

📌 Here Comes Mamba: The Selective State Space Model 🗂 Category: DEEP LEARNING 🕒 Date: 2024-09-03 | ⏱️ Read time: 22 min read Part 3 – Towards Mamba State Space Models for Images, Videos and Time Series

40 202

📌 Diving Deeper with Structured Outputs 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2024-09-03 | ⏱️ Read time: 10 min read Enhancing our understanding and optimal usage of structured outputs

40 202

📌 Approximating Stochastic Functions with Multivariate Outputs 🗂 Category: 🕒 Date: 2024-09-04 | ⏱️ Read time: 25 min read A generic approach for training probabilistic machine learning models