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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 👉 https://rebrand.ly/bigdatachannels DMCA: @disclosure_bds Contact: @mldatascientist

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📈 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 9 380-o'rinni va Hindiston mintaqasida 31 607-o'rinni egallagan.

📊 Auditoriya ko‘rsatkichlari va dinamika

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

10 Iyun, 2026 dagi oxirgi ma’lumotlarga ko‘ra kanal barqaror faollikka ega. Oxirgi 30 kunda obunachilar soni 143 ga, so‘nggi 24 soatda esa 2 ga o‘zgardi va umumiy qamrov yuqori darajada qolmoqda.

  • Tasdiqlash holati: Tasdiqlanmagan
  • Jalb etish (ER): Auditoriya o‘rtacha 8.09% darajada jalb etiladi. Nashrdan keyingi dastlabki 24 soatda kontent odatda umumiy obunachilar sonining 2.22% ini tashkil etuvchi reaksiyalarni to‘playdi.
  • Post qamrovi: Har bir post o‘rtacha 1 106 marta ko‘riladi; birinchi sutkada odatda 304 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 11 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
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+224 soatlar
+217 kunlar
+14330 kunlar
Postlar arxiv

Data Science Techniques
Data Science Techniques

WHICH CHART WHEN? The data Analyst's guide to choosing the right charts

Cloud Engineer Roadmap
Cloud Engineer Roadmap

Self guide to become a data analyst
Self guide to become a data analyst

Going Denser with Open-Vocabulary Part Segmentation Publication date: 18 May 2023 Topic: Object detection Paper: https://arxi
Going Denser with Open-Vocabulary Part Segmentation Publication date: 18 May 2023 Topic: Object detection Paper: https://arxiv.org/pdf/2305.11173v1.pdf GitHub: https://github.com/facebookresearch/vlpart Description: Object detection has been expanded from a limited number of categories to open vocabulary. Moving forward, a complete intelligent vision system requires understanding more fine-grained object descriptions, object parts. In this work, we propose a detector with the ability to predict both open-vocabulary objects and their part segmentation. This ability comes from two designs: 🔹 We train the detector on the joint of part-level, object-level and image-level data. 🔹 We parse the novel object into its parts by its dense semantic correspondence with the base object.

Roadmap to Devops
Roadmap to Devops

Why Statistics Matter in Data Science even in 2023

How to choose a graph
How to choose a graph

One question to make your data project 10x more valuable If you are the "data person" for your organization, then providing m
One question to make your data project 10x more valuable If you are the "data person" for your organization, then providing meaningful results to stakeholder data requests can sometimes feel like shots in the dark. However, you can make sure your data analysis is actionable by asking one magic question before getting started. The magic question Luckily, we don't need to spend all of our time defining the problem. Here is the one simple question that will get to the heart of any data request within minutes: "What decision are you trying to make?" Subtext: What action will you take once you have the answers? If there is no action, then there will be no impact. This question will cut through all of the clutter and get straight to the action. And the answer can be VERY telling! That's why it's so powerful. A good response is specific! Almost immediately, you should be able to picture what they'll do once they see the data. 🔗 Read more

Introduction to Data Science

R, ggplot, and Simple Linear Regression Begin to use R and ggplot while learning the basics of linear regression Rating ⭐️: 4.1 out 5 Students 👨‍🎓 : 42,633 Duration ⏰ : 2hr 14min of on-demand video Created by 👨‍🏫: Charles Redmond 🔗 Course Link #R #linear #Regression ➖➖➖➖➖➖➖➖➖➖➖➖➖➖ 👉Join @datascience_bds for more👈

Data Engineer's Pathway
Data Engineer's Pathway

Data Science: Theories, Models, Algorithms, and Analytics by SANJIV RANJAN DAS

Essential AI Tools For Data Analysis
Essential AI Tools For Data Analysis

Top 5 Reasons Why Machine Learning Projects Fail The intent of our article today is to help you get acquainted with the many
Top 5 Reasons Why Machine Learning Projects Fail The intent of our article today is to help you get acquainted with the many reasons behind machine learning projects’ failure. We are hopeful that the information will help you plan a better implementation, one that carries fewer chances of failure in all three stages of ML execution: pre-project, during the project, and post-project. 1. Insufficient data 2. ML Models unsynchronized with the legacy systems 3. Lack of enough data scientists 4. Difficulty in updating 5. Lack of leaders’ support The solution to addressing these challenges more often than not lies with partnering with a skilled machine learning solution provider company that understands both business and technical implications of applying a new-gen technology in a non-digital organization. They can help you in not just creating a work plan of how to integrate machine learning projects but also with adopting the new system in the most optimal way. 🔗 Read more

Python Libraries For Data Science
Python Libraries For Data Science

7 Platforms for Getting High Paying Data Science Jobs 1. LinkedIn 2. Wellfound 3. Toptal 4. Upwork 5. Kolabtree 6. Indeed 7.
7 Platforms for Getting High Paying Data Science Jobs 1. LinkedIn 2. Wellfound 3. Toptal 4. Upwork 5. Kolabtree 6. Indeed 7. Amazon Jobs

Logistic Regression Practical Case Study Breast Cancer detection using Logistic Regression Rating ⭐️: 4.7 out 5 Students 👨‍🎓 : 35,819 Duration ⏰ : 1hr 4min of on-demand video Created by 👨‍🏫: Hadelin de Ponteves, SuperDataScience Team, Ligency Team 🔗 Course Link #Logistic #Regression ➖➖➖➖➖➖➖➖➖➖➖➖➖➖ 👉Join @datascience_bds for more👈

8 Books that Will Teach You the Basics of Data Science In an era where data is hailed as the new oil, the demand for data sci
8 Books that Will Teach You the Basics of Data Science In an era where data is hailed as the new oil, the demand for data scientists continues to soar. Data science, a multidisciplinary field that extracts insights and knowledge from data, has become a cornerstone of many industries. For those aspiring to enter this dynamic field, building a solid foundation is essential. Books are a timeless source of knowledge, and in this article, we’ll explore eight must-read books that will teach you the basics of data science, making your journey into this fascinating world more accessible. 1. “Python for Data Analysis” by Wes McKinney Wes McKinney’s book is a fantastic starting point for beginners. It focuses on the practical use of Python, one of the most popular programming languages in data science. You’ll learn how to work with data structures, perform data cleaning, and apply statistical analysis. The book also introduces the powerful Pandas library for data manipulation. Source-Link: analyticsinsight