uz
Feedback
Data science/ML/AI

Data science/ML/AI

Kanalga Telegram’da o‘tish

Data science and machine learning hub Python, SQL, stats, ML, deep learning, projects, PDFs, roadmaps and AI resources. For beginners, data scientists and ML engineers 👉 https://rebrand.ly/bigdatachannels DMCA: @disclosure_bds Contact: @mldatascientist

Ko'proq ko'rsatish

📈 Telegram kanali Data science/ML/AI analitikasi

Data science/ML/AI (@datascience_bds) Ingliz til segmentidagi kanali faol ishtirokchi. Hozirda hamjamiyat 13 674 obunachidan iborat bo'lib, Texnologiyalar & Aralashmalar toifasida 9 377-o'rinni va Hindiston mintaqasida 31 635-o'rinni egallagan.

📊 Auditoriya ko‘rsatkichlari va dinamika

невідомо sanasidan buyon loyiha tez o‘sib, 13 674 obunachiga ega bo‘ldi.

09 Iyun, 2026 dagi oxirgi ma’lumotlarga ko‘ra kanal barqaror faollikka ega. Oxirgi 30 kunda obunachilar soni 155 ga, so‘nggi 24 soatda esa 5 ga o‘zgardi va umumiy qamrov yuqori darajada qolmoqda.

  • Tasdiqlash holati: Tasdiqlanmagan
  • Jalb etish (ER): Auditoriya o‘rtacha 8.03% darajada jalb etiladi. Nashrdan keyingi dastlabki 24 soatda kontent odatda umumiy obunachilar sonining 2.25% ini tashkil etuvchi reaksiyalarni to‘playdi.
  • Post qamrovi: Har bir post o‘rtacha 1 098 marta ko‘riladi; birinchi sutkada odatda 308 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 panda, learning, row, api, ethic kabi asosiy mavzularga jamlangan.

📝 Tavsif va kontent siyosati

Muallif resursni shaxsiy fikrni ifoda etish maydoni sifatida ta’riflaydi:
Data science and machine learning hub Python, SQL, stats, ML, deep learning, projects, PDFs, roadmaps and AI resources. For beginners, data scientists and ML engineers 👉 https://rebrand.ly/bigdatachannels DMCA: @disclosure_bds Contact: @mldatasci...

Yuqori yangilanish chastotasi (oxirgi ma’lumot 10 Iyun, 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.

13 674
Obunachilar
+524 soatlar
+197 kunlar
+15530 kunlar
Postlar arxiv
The RAG Developer Stack 2025 - Build Intelligent Al That Thinks, Remembers & Acts
The RAG Developer Stack 2025 - Build Intelligent Al That Thinks, Remembers & Acts

Data Structures in R
Data Structures in R

An Artificial Neuron
An Artificial Neuron

Layers of AI
Layers of AI

📚 Data Science Riddle What metric is commonly used to decide splits in decision trees?
Anonymous voting

7 In Demand Data Analytics Skills
7 In Demand Data Analytics Skills

Essential Pandas Methods For Data Science
Essential Pandas Methods For Data Science

📚 Data Science Riddle In PCA, what do eigenvectors represent?
Anonymous voting

AI Agents Quick Guide
AI Agents Quick Guide

📚 Data Science Riddle Which algorithm groups data into clusters without labels?
Anonymous voting

Extracting Features from Text - A Step-by-Step NLP Guide.pdf8.32 KB

Dropout Explained Simply Neural networks are notorious for overfitting ( they memorize training data instead of generalizing)
Dropout Explained Simply Neural networks are notorious for overfitting ( they memorize training data instead of generalizing). One of the simplest yet most powerful solutions? Dropout. During training, dropout randomly “drops” a percentage of neurons ( 20–50%). Those neurons temporarily go offline, meaning their activations aren’t passed forward and their weights aren’t updated in that round. 👉 What this does: ✔️ Forces the network to avoid relying on any single path. ✔️ Creates redundancy → multiple neurons learn useful features. ✔️ Makes the model more robust and less sensitive to noise. When testing happens, dropout is turned off, and all neurons fire but now they collectively represent stronger, generalized patterns. Imagine dropout like training with handicaps. It’s as if your brain had random “short blackouts” while studying, forcing you to truly understand instead of memorizing. And that’s why dropout remains a go-to regularization technique in deep learning and even in advanced architectures.

Importance of Statistics and Exploratory Data Analysis
Importance of Statistics and Exploratory Data Analysis

photo content

What is RAG? 🤖📚 RAG stands for Retrieval-Augmented Generation. It’s a technique where an AI model first retrieves relevant
What is RAG? 🤖📚 RAG stands for Retrieval-Augmented Generation. It’s a technique where an AI model first retrieves relevant info (like from documents or a database), and then generates an answer using that info. 🧠 Think of it like this: Instead of relying only on what it "knows", the model looks things up first - just like you would Google something before replying. 🔍 Retrieval + 📝 Generation = Smarter, up-to-date answers!

Repost from Data visualization
How Data Science Roles are Changing With The Rise of AI
How Data Science Roles are Changing With The Rise of AI

📚 Data Science Riddle You have a dataset with 1,000 samples and 10,000 features. What’s a common problem you might face when training a model on this data?
Anonymous voting

Morning brain teaser! 🧠 Let's see who's awake... 📚 Data Science Riddle You have a dataset with 1,000 samples and 10,000 features. What’s a common problem you might face when training a model on this data?
Anonymous voting

Linear Algebra for Data Science.pdf6.12 KB

🚀 Fast-Track Machine Learning Roadmap 2025 Mindset: Build first, learn just-in-time. Share progress publicly (GitHub + posts). Consistency > cramming. Weeks 1–2: Master Python, NumPy, Pandas, EDA, and data cleaning. Mini-win: load CSVs, handle missing data. Weeks 3–6: Learn ML fundamentals with scikit-learn — train/test splits, cross-validation, classifiers (LogReg, RF, XGB), and regressors. Project: spam classifier or house price predictor. Weeks 7–10: Dive into deep learning — tensors, autograd, PyTorch. Build CNN or text classifier + track experiments (Weights & Biases). Weeks 11–12: Specialize (NLP, CV, recommenders, MLOps) and ship a niche AI app. ———————— Weekly Routine:  Mon-Tue: Learn concept + code example  Wed-Thu: Build feature + log metrics  Fri: Refactor + README + demo  Sat: Share + get feedback + plan fixes  Sun: Rest & review ———————— Portfolio Tips: Clear READMEs, reproducible env, demo videos, honest metric analysis. Avoid “math purgatory” and messy repos. Ship small every week! ———————— This approach gets you practical, portfolio-ready ML skills in ~3-4 months with real projects and solid evaluation for 2025 job markets!