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Data science/ML/AI

Data science/ML/AI

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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 πŸ‘‰ https://rebrand.ly/bigdatachannels DMCA: @disclosure_bds Contact: @mldatascientist

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πŸ“ˆ Telegram kanali Data science/ML/AI analitikasi

Data science/ML/AI (@datascience_bds) Ingliz til segmentidagi kanali faol ishtirokchi. Hozirda hamjamiyat 14 053 obunachidan iborat bo'lib, Texnologiyalar & Aralashmalar toifasida 9 088-o'rinni va Hindiston mintaqasida 28 116-o'rinni egallagan.

πŸ“Š Auditoriya koβ€˜rsatkichlari va dinamika

Π½Π΅Π²Ρ–Π΄ΠΎΠΌΠΎ sanasidan buyon loyiha tez oβ€˜sib, 14 053 obunachiga ega boβ€˜ldi.

29 Oktabr, 2026 dagi oxirgi ma’lumotlarga koβ€˜ra kanal barqaror faollikka ega. Oxirgi 30 kunda obunachilar soni 79 ga, soβ€˜nggi 24 soatda esa -4 ga oβ€˜zgardi va umumiy qamrov yuqori darajada qolmoqda.

  • Tasdiqlash holati: Tasdiqlanmagan
  • Jalb etish (ER): Auditoriya oβ€˜rtacha 9.66% darajada jalb etiladi. Nashrdan keyingi dastlabki 24 soatda kontent odatda umumiy obunachilar sonining 2.14% ini tashkil etuvchi reaksiyalarni toβ€˜playdi.
  • Post qamrovi: Har bir post oβ€˜rtacha 1 358 marta koβ€˜riladi; birinchi sutkada odatda 301 ta koβ€˜rish yigβ€˜iladi.
  • Reaksiyalar va oβ€˜zaro ta’sir: Auditoriya faol: har bir postga oβ€˜rtacha 4 ta reaksiya keladi.
  • Tematik yoβ€˜nalishlar: Kontent panda, learning, row, api, ethic kabi asosiy mavzularga jamlangan.

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β€œData science and machine learning hub Python, SQL, stats, ML, deep learning, projects, PDFs, roadmaps and AI resources. For beginners, data scientists and ML engineers πŸ‘‰ https://rebrand.ly/bigdatachannels DMCA: @disclosure_bds Contact: @mldatasci...”

Yuqori yangilanish chastotasi (oxirgi ma’lumot 30 Oktabr, 2026 da olingan) sababli kanal doimo dolzarb va katta qamrovli boβ€˜lib qoladi. Analitika auditoriya kontent bilan faol hamkorlik qilishini, uni Texnologiyalar & Aralashmalar toifasidagi muhim ta’sir nuqtasiga aylantirishini koβ€˜rsatadi.

14 053
Obunachilar
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+7930 kun
Postlar arxiv
Applications of Deep Learning
Applications of Deep Learning

Data Science vs Mathematics
Data Science vs Mathematics

Python for Data Science with Assignments A Comprehensive and Practical Hands-On Guide to Learning Python for Beginners, Aspiring Developers, Self-Learners, etc. Rating ⭐️: 4.7 out 5 Students πŸ‘¨β€πŸŽ“ : 18046 Duration ⏰ : 9.5 hours on-demand video Created by πŸ‘¨β€πŸ«: Meritshot Academy πŸ”— Course Link ⚠️ Its free for first 1000 enrollments only! #python #datascience βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž– πŸ‘‰Join @bigdataspecialist for moreπŸ‘ˆ

Completely unimportant but interesting fact we have 7777 subscribers ATM
Completely unimportant but interesting fact we have 7777 subscribers ATM

Statistics test flow chart
Statistics test flow chart

Accelerate Data Science Workflows with Zero Code Changes by nvidia Across industries, modern data science requires large amounts of data to be processed quickly and efficiently. These workloads need to be accelerated to ensure prompt results and increase overall productivity. NVIDIA RAPIDS offers a seamless experience to enable GPU-acceleration for many existing data science tasks with zero code changes. In this workshop, you’ll learn to use RAPIDS to speed up your CPU-based data science workflows. By participating in this course, you will: Understand the benefits of a unified workflow across CPUs and GPUs for data science tasks Learn how to GPU-accelerate various data processing and machine learning workflows with zero code changes Experience the significant reduction in processing time when workflows are GPU-accelerated Prerequisites: Basic understanding of data processing and knowledge of a standard data science workflow on tabular data Experience using common Python libraries for data analytics Tools, libraries, frameworks used: NVIDIA RAPIDS (cuDF, cuML, cuGraph), pandas, scikit-learn, and NetworkX πŸ†“ Free Online Course ⏰ Duration : More than 1 hour πŸƒβ€β™‚οΈ Self paced βœ… Certification available Course Link #datascience #nvidia  βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž– πŸ‘‰Join @bigdataspecialist for moreπŸ‘ˆ

The Data Science Sandwich
The Data Science Sandwich

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Data Science Techniques
Data Science Techniques

Important Data Terms
Important Data Terms

Statistical models cheatsheet
Statistical models cheatsheet

+1
Harolds_Stats_Distributions_Cheat_Sheet.pdf1.16 MB

Statistical distributions cheatsheet

Career Path of A Data Analyst
Career Path of A Data Analyst

Flow chart of commonly used statistical tests
Flow chart of commonly used statistical tests

Introduction to Probability and Statistics for Engineers List of probability and statistics cheatsheets by Stanford
Introduction to Probability and Statistics for Engineers List of probability and statistics cheatsheets by Stanford

Brain of an AI Engineer
Brain of an AI Engineer

[Compilation]1000+ Data Science Interview Questions/Preparation Resources Compilation created by kaggle users 1. GIT interview questions for DS and SQL Interview questions 2. 50 ML questions 3. Four years on interview questions 4. Compilation of pandas interview questions 5. Difference between common ML algortihms 6. Scenario based Data questions 7. Top python interview questions 8. Internship questions for DS interns 9. Questions from DS- Netflix 10. India specific Data science interview questions 11. R interview questions 12. Explain a project in Data science 13. A great collection of cheatsheets, analyzed here 14. A collection of questions on Github here 15. Cheat Sheets for Machine Learning Interview Topics 16. Compiled list of 600+ Q&As for Data Science interview prep πŸŽ‰ 17. Approaching almost any ML Problem, originally shared on Kaggle 18. A Basics refresher 19. A notebook 20. Companies and Data Science Interview questions Megathread 21. Data Scientist - Interview Question Bank 22. ML Interview questions 23. Machine Learning Interviews Book πŸ‘‡ https://www.kaggle.com/discussions/questions-and-answers/239533 βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž– πŸ‘‰Join @datascience_bds for moreπŸ‘ˆ

The LLM Scientist Roadmap
The LLM Scientist Roadmap

LLMOps vs MLOps
LLMOps vs MLOps