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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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Programming, data science, ML - free courses by Big Data SpecialistNetwork:Programming, data science, ML - free courses by Big Data SpecialistIndia31 607Technologies & Applications9 380

πŸ“ˆ Analytical overview of Telegram channel Data science/ML/AI

Channel Data science/ML/AI (@datascience_bds) in the English language segment is an active participant. Currently, the community unites 13 685 subscribers, ranking 9 380 in the Technologies & Applications category and 31 607 in the India region.

πŸ“Š Audience metrics and dynamics

Since its creation on Π½Π΅Π²Ρ–Π΄ΠΎΠΌΠΎ, the project has demonstrated rapid growth, gathering an audience of 13 685 subscribers.

According to the latest data from 10 June, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 143 over the last 30 days and by 2 over the last 24 hours, overall reach remains high.

  • Verification status: Not verified
  • Engagement rate (ER): The average audience engagement rate is 8.09%. Within the first 24 hours after publication, content typically collects 2.22% reactions from the total number of subscribers.
  • Post reach: On average, each post receives 1 106 views. Within the first day, a publication typically gains 304 views.
  • Reactions and interaction: The audience actively supports content: the average number of reactions per post is 5.
  • Thematic interests: Content is focused on key topics such as panda, learning, row, api, ethic.

πŸ“ Description and content policy

The author describes the resource as a platform for expressing subjective opinions:
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...”

Thanks to the high frequency of updates (latest data received on 11 June, 2026), the channel maintains relevance and a high level of publication reach. Analytics show that the audience actively interacts with content, making it an important point of influence in the Technologies & Applications category.

13 685
Subscribers
+224 hours
+217 days
+14330 days
Posts Archive
Data science/ML/AI
Data science/ML/AI
13 685
Data Scientist, Data Engineer and Data Analyst
Data Scientist, Data Engineer and Data Analyst

Data science/ML/AI
Data science/ML/AI
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. πŸ†“ Free Online Course Rating⭐️: 4.7 out 5 🎬 video lesson πŸƒβ€β™‚οΈ Self paced Duration ⏰: More than 7 hours worth of material Source: cognitiveclass πŸ”— Course Link #deep_Learning βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž– Join @datascience_bds for more cool data science materials. *This channel belongs to @bigdataspecialist group

Data science/ML/AI
Data science/ML/AI
13 685
Data Science vs AI vs ML
Data Science vs AI vs ML

Data science/ML/AI
Data science/ML/AI
13 685
Deep Learning Notes

Data science/ML/AI
Data science/ML/AI
13 685
Introduction to the Data Science Process
Introduction to the Data Science Process

Data science/ML/AI
Data science/ML/AI
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 πŸƒβ€β™‚οΈ Self paced Source: University of Michigan πŸ”— Course Link #data_science βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž– Join @datascience_bds for more cool data science materials. *This channel belongs to @bigdataspecialist group

Data science/ML/AI
Data science/ML/AI
13 685
Amazon Data Scientist Interview Process
Amazon Data Scientist Interview Process

Data science/ML/AI
Data science/ML/AI
13 685
MIT 6.S191: Introduction to Deep Learning 2021 Created by MIT ⏰ 29 hours worth of material 🎬 43 Video lessons πŸ‘¨β€πŸ« Teacher: Alexander Amini πŸ”— Course link #deeplearning #ai #MIT βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž– πŸ‘‰Join @bigdataspecialist for moreπŸ‘ˆ

Data science/ML/AI
Data science/ML/AI
13 685
Your Guide to Latent Dirichlet Allocation Latent Dirichlet Allocation (LDA) is a β€œgenerative probabilistic model” of a collec
Your Guide to Latent Dirichlet Allocation Latent Dirichlet Allocation (LDA) is a β€œgenerative 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 βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž– Join @datascience_bds for more cool data science materials. *This channel belongs to @bigdataspecialist group

Data science/ML/AI
Data science/ML/AI
13 685
Foundations of Data Science by Avrim Blum, John Hopcroft, and Ravindran Kannan πŸ“„ 479 pages #data_science #foundations_of_data_Science βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž– Join @datascience_bds for more

Data science/ML/AI
Data science/ML/AI
13 685
Data Science with other fields of science
Data Science with other fields of science

Data science/ML/AI
Data science/ML/AI
13 685
Big and Sparse Data Sciences Integration with Theory, Experiment, Simulations, and Uncertainty Quantification
Big and Sparse Data Sciences Integration with Theory, Experiment, Simulations, and Uncertainty Quantification

Data science/ML/AI
Data science/ML/AI
13 685
100 Days of Data Science Challenge
100 Days of Data Science Challenge

Data science/ML/AI
Data science/ML/AI
13 685
Why choose data science
Why choose data science

Data science/ML/AI
Data science/ML/AI
13 685
photo content

Data science/ML/AI
Data science/ML/AI
13 685
Data Science for Engineers, IIT Madras πŸ†“ Free Online Course πŸ’» 50 Lecture Videos ⏰ 8 Module πŸƒβ€β™‚οΈ Self paced Teacher πŸ‘¨β€πŸ« : Prof. Shankar Narasimhan, Prof. Ragunathan Rengasamy πŸ”— https://nptel.ac.in/courses/106106179 #Data_Science #IIT βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž– πŸ‘‰Join @bigdataspecialist for moreπŸ‘ˆ

Data science/ML/AI
Data science/ML/AI
13 685
Data Science Components
Data Science Components

Data science/ML/AI
Data science/ML/AI
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 βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž– Join @datascience_bds for more cool repositories. *This channel belongs to @bigdataspecialist group

Data science/ML/AI
Data science/ML/AI
13 685
21 most important equations in data science
21 most important equations in data science

Data science/ML/AI
Data science/ML/AI
13 685
Essential Charts for Data Analysis
Essential Charts for Data Analysis