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

Tarmoq:Programming, data science, ML - free courses by Big Data Specialist Hindiston31 607 Texnologiyalar & Aralashmalar9 380...

📈 Telegram kanali Data science/ML/AI analitikasi

Data science/ML/AI (@datascience_bds) Ingliz til segmentidagi kanali faol ishtirokchi. Hozirda hamjamiyat 13 685 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, 13 685 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.

13 685

Obunachilar

+224 soatlar

+217 kunlar

+14330 kunlar

1 106

Post ko'rishlar

~ 30424 soatlar

~ 44448 soatlar

8.09%

Muloqot nisbati

~ 1

Kuniga postlar

Ads index

beta

Postlar arxiv

13 685

Data Scientist, Data Engineer and Data Analyst

13 685

Accelerating Deep Learning with GPUs (Login Required) Training complex deep learning models with large datasets takes along time. In this course, you will learn how to use accelerated GPU hardware to overcome the scalability problem in deep learning. You can use accelerated hardware such as Google’s Tensor Processing Unit (TPU) or Nvidia GPU to accelerate your convolutional neural network computations time on the Cloud. These chips are specifically designed to support the training of neural networks, as well as the use of trained networks (inference). Accelerated hardware has recently been proven to significantly reduce training time. 🆓 Free Online Course Rating⭐️: 4.7 out 5 🎬 video lesson 🏃‍♂️ Self paced Duration ⏰: More than 7 hours worth of material Source: cognitiveclass 🔗 Course Link #deep_Learning ➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖ Join @datascience_bds for more cool data science materials. *This channel belongs to @bigdataspecialist group

13 685

Data Science vs AI vs ML

13 685

Deep Learning Notes

13 685

Introduction to the Data Science Process

13 685

Data Science Ethics (Login Required) Utilize the framework provided in the course to analyze concerns related to data science ethics. Explore the broader impact of the data science field on modern society and the principles of fairness, accountability and transparency. Examine the need for voluntary disclosure when leveraging metadata to inform basic algorithms and/or complex artificial intelligence systems. Learn best practices for responsible data management. Gain an understanding of the significance of the Fair Information Practices Principles Act and the laws concerning the "right to be forgotten." 🎬 video lessons Rating⭐️: 4.1 out 5 🏃‍♂️ Self paced Source: University of Michigan 🔗 Course Link #data_science ➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖ Join @datascience_bds for more cool data science materials. *This channel belongs to @bigdataspecialist group

13 685

Amazon Data Scientist Interview Process

13 685

MIT 6.S191: Introduction to Deep Learning 2021 Created by MIT ⏰ 29 hours worth of material 🎬 43 Video lessons 👨‍🏫 Teacher: Alexander Amini 🔗 Course link #deeplearning #ai #MIT ➖➖➖➖➖➖➖➖➖➖➖➖➖➖ 👉Join @bigdataspecialist for more👈

13 685

Your Guide to Latent Dirichlet Allocation Latent Dirichlet Allocation (LDA) is a “generative probabilistic model” of a collection of composites made up of parts. Its uses include Natural Language Processing (NLP) and topic modelling, among others. In terms of topic modelling, the composites are documents and the parts are words and/or phrases (phrases n words in length are referred to as n-grams). But you could apply LDA to DNA and nucleotides, pizzas and toppings, molecules and atoms, employees and skills, or keyboards and crumbs. The probabilistic topic model estimated by LDA consists of two tables (matrices). The first table describes the probability or chance of selecting a particular part when sampling a particular topic (category). Link #ml #data_science ➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖ Join @datascience_bds for more cool data science materials. *This channel belongs to @bigdataspecialist group

13 685

Foundations of Data Science by Avrim Blum, John Hopcroft, and Ravindran Kannan 📄 479 pages #data_science #foundations_of_data_Science ➖➖➖➖➖➖➖➖➖➖➖➖➖ Join @datascience_bds for more

13 685

Data Science with other fields of science

13 685

Big and Sparse Data Sciences Integration with Theory, Experiment, Simulations, and Uncertainty Quantification

13 685

100 Days of Data Science Challenge

13 685

Why choose data science

13 685

Data Science for Engineers, IIT Madras 🆓 Free Online Course 💻 50 Lecture Videos ⏰ 8 Module 🏃‍♂️ Self paced Teacher 👨‍🏫 : Prof. Shankar Narasimhan, Prof. Ragunathan Rengasamy 🔗 https://nptel.ac.in/courses/106106179 #Data_Science #IIT ➖➖➖➖➖➖➖➖➖➖➖➖➖➖ 👉Join @bigdataspecialist for more👈

13 685

Data Science Components

13 685

R for Data Science A weekly data project aimed at the R ecosystem. As this project was borne out of the R4DS Online Learning Community and the R for Data Science textbook, an emphasis was placed on understanding how to summarize and arrange data to make meaningful charts with ggplot2, tidyr, dplyr, and other tools in the tidyverse ecosystem. However, any code-based methodology is welcome - just please remember to share the code used to generate the results. Creator: rfordatascience Stars ⭐️: 5.6k Forked By: 2.3k https://github.com/rfordatascience/tidytuesday #R #data_science ➖➖➖➖➖➖➖➖➖➖➖➖➖➖ Join @datascience_bds for more cool repositories. *This channel belongs to @bigdataspecialist group

13 685

21 most important equations in data science

13 685

Essential Charts for Data Analysis