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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 551Technologies & Applications9 384

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 13 690 subscribers, ranking 9 384 in the Technologies & Applications category and 31 551 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 13 690 subscribers.

According to the latest data from 11 June, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 150 over the last 30 days and by 11 over the last 24 hours, overall reach remains high.

  • Verification status: Not verified
  • Engagement rate (ER): The average audience engagement rate is 8.13%. Within the first 24 hours after publication, content typically collects 2.20% reactions from the total number of subscribers.
  • Post reach: On average, each post receives 1 112 views. Within the first day, a publication typically gains 301 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 12 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.

13 690
Subscribers
+1124 hours
+227 days
+15030 days
Posts Archive
Data science/ML/AI
Data science/ML/AI
13 690
πŸ”— Book link #machinelearning #ml #datascience βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž– Join @coding_interview_preparation for more. *This channel belo
πŸ”— Book link #machinelearning #ml #datascience βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž– Join @coding_interview_preparation for more. *This channel belongs to @bigdataspecialist group

Data science/ML/AI
Data science/ML/AI
13 690
The Periodic Table Of Data Science
The Periodic Table Of Data Science

Data science/ML/AI
Data science/ML/AI
13 690
Deep Learning Do It Yourself! This site collects resources to learn Deep Learning in the form of Modules available through the sidebar on the left. https://dataflowr.github.io/website/ 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
13 690
Graph ML and deep learning courses This is another post on your request. Other courses you requested will be shared in following days. Geometric Deep learning course AMMI21 πŸ‘¨β€πŸ« Teachers: Michael M. Bronstein, Joan Bruna, Taco Cohen, Petar VeličkoviΔ‡ πŸ“š12 lectures, 2 tutorials, and 4 seminars This course follows GDL BOOK πŸ”— Course link: https://geometricdeeplearning.com/lectures/ Machine Learning for Graphs and Sequential Data (MLGS) by Stephan GΓΌnnemann Awesome course covering in depth generative models, robustness, sequential data, clustering, label propagation, GNNs, and more πŸ”— Course link: https://www.in.tum.de/daml/teaching/mlgs/ Stanford CS224W course on graph ML A legendary Stanford CS224W course on graph ML now releases videos on YouTube for 2021 🎬 60 Videos ⏰ 30h πŸ”— Course link Python For Data Science (Udemy) This course specifically created for Data Science / AI / ML / DL. It covers BASICS PYTHON ONLY Rating ⭐️: 4.1 out of 5 Students πŸ‘¨β€πŸŽ“: 65,523 students Duration ⏰: 3hr 55min of on-demand video πŸ”— Course link Deep Learning Prerequisites: The Numpy Stack in Python V2 (Udemy) Rating ⭐️: 4.6 out of 5 Students πŸ‘¨β€πŸŽ“: 34,785 Duration ⏰: 1hr 59min of on-demand video πŸ”— Course link There is also this cool blogpost by GordiΔ‡ Aleksa: How to get started with Graph Machine Learning And one early access version book: Graph Powered Machine Learning by: Allesandro Negro πŸ”— Book link #graphML #ML #machinelearning #deeplearning #python 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
13 690
Rules of Machine Learning: Best Practices for ML Engineering Author: Martin Zinkevich This document is intended to help those with a basic knowledge of machine learning get thebenefit of best practices in machine learning from around Google. πŸ‘‰ 43 ML Rules to follow πŸ”— http://martin.zinkevich.org/rules_of_ml/rules_of_ml.pdf 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
13 690
Machine Learning for Healthcare (Spring 2019) By Massachusetts Institute of Technology (MIT) 🎬 25 video lessons ⏰ 33 hours πŸ‘¨β€πŸ« Prof. Peter Szolovits πŸ‘¨β€πŸ« Prof. David Sontag https://www.classcentral.com/course/mit-opencourseware-machine-learning-for-healthcare-spring-2019-40955/classroom #ml #machinelearning #healthcare #MIT 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
13 690
ML and NLP Research Highlights of 2021 by Sebastian Ruder This post summarizes progress across multiple impactful areas in ML and NLP in 2021. Contents: Universal Models Massive Multi-task Learning Beyond the Transformer Prompting Efficient Methods Benchmarking Conditional Image Generation ML for Science Program Synthesis Bias Retrieval Augmentation Token-free Models Temporal Adaptation The Importance of Data Meta-learning https://ruder.io/ml-highlights-2021/ βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž– Join @datascience_bds for more cool DS/ML materials.

Data science/ML/AI
Data science/ML/AI
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Free 10-Hour Machine Learning Course by freecodecamp Section 1: Basics of Machine Learning Section 2: Linear Regression & Regularization Section 3: Logistic Regression & Performance Metrics Section 4: Support Vector Machine Section 5: PCA Section 6: Learning Theory Section 7: Decision Trees & Random Forest Section 7.5: Learning more algorithms and building more projects Section 8: Unsupervised Learning Algorithms Section 9: Building Applications πŸ”— Course link: https://www.freecodecamp.org/news/free-machine-learning-course-10-hourse/ 10-hour youtube video: https://www.youtube.com/watch?v=NWONeJKn6kc βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž– Join @datascience_bds for more cool DS/ML materials.

Data science/ML/AI
Data science/ML/AI
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Data Science: Python for Data Analysis 2022 Full Bootcamp Rating ⭐️: 4.3 out of 5 Students πŸ‘¨β€πŸ«: 104,287 Created by: Ahmed Ibrahim and SDE OCTOPUS | AI πŸ”— Course link Note: Free coupon is inserted in URL. Number of free spots is limited to 1000. Once this number is reached, coupon won't be valid anymore. #python #datanalysis #datascience 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
13 690
Introduction to Data Science by University of Washington 🎬 95 video sessions ⏰ Duration: 16h πŸ‘¨β€πŸ« Instructor: Bill Howe, Ph
Introduction to Data Science by University of Washington 🎬 95 video sessions ⏰ Duration: 16h πŸ‘¨β€πŸ« Instructor: Bill Howe, PhD βœ… Completely free πŸ”— Course link #datascience #ds #ml #washingtonuniversity 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
13 690
Introduction to Machine Learning (Fall 2020) By Massachusetts Institute of Technology, MIT Length: 13 weeks πŸ”— Course link #m
Introduction to Machine Learning (Fall 2020) By Massachusetts Institute of Technology, MIT Length: 13 weeks πŸ”— Course link #ml #machinelearning #datascience #MIT 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
13 690
Neural Networks with JavaScript Succinctly πŸ”— Book PDF #javascript #datascience #neuralnetworks βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž– Join @programmi
Neural Networks with JavaScript Succinctly πŸ”— Book PDF #javascript #datascience #neuralnetworks βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž– Join @programming_books_bds for more

Data science/ML/AI
Data science/ML/AI
13 690
Mathematics for Machine Learning Published by Cambridge University Press (published April 2020) https://mml-book.com PDF: htt
Mathematics for Machine Learning Published by Cambridge University Press (published April 2020) https://mml-book.com PDF: https://mml-book.github.io/book/mml-book.pdf

Data science/ML/AI
Data science/ML/AI
13 690
Get ready for second annual #NLPSummit by John Snow Labs. Week One comes with 50+ unique sessions with a special track on #NL
Get ready for second annual #NLPSummit by John Snow Labs. Week One comes with 50+ unique sessions with a special track on #NLP in #Healthcare. Week Two - beginner to advanced training workshops with certification. Hear from industry leaders at NASA, Vonage, Zillow, Merck, Amazon, Walmart Labs, Booz Allen Hamilton, Morgan Stanley, Salesforce, Roku, Zillow and many more! Free registration: https://www.nlpsummit.org/2021-events/ #ML #AI #digitalhealthcare #dataengineer #deeplearning

Data science/ML/AI
Data science/ML/AI
13 690
The People + AI Guidebook by Google The People + AI Guidebook is a set of methods, best practices and examples for designing with AI. https://pair.withgoogle.com/guidebook/

Data science/ML/AI
Data science/ML/AI
13 690
Deep learning at Oxford 2015 🎬 16 lessons ⏰ 15 hours https://www.youtube.com/playlist?list=PLE6Wd9FR--EfW8dtjAuPoTuPcqmOV53Fu #deeplearning #oxford 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
13 690
Reinforcement Learning Lecture Series 2021 🎬 13 lessons ⏰ 14 hours Taught by DeepMind researchers, this series was created in collaboration with University College London (UCL) to offer students a comprehensive introduction to modern reinforcement learning. https://deepmind.com/learning-resources/reinforcement-learning-series-2021 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
13 690
Four Deep Learning Papers to Read in September 2021 β€˜Auto-Sklearn 2.0: Hands-free AutoML via Meta-Learning’ Authors: Feurer et al. (2021) πŸ“ Paper πŸ€– Code β€˜How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers’ Authors: Steiner et al. (2021) πŸ“ Paper πŸ€– Code β€˜Catastrophic Fisher Explosion: Early Phase Fisher Matrix Impacts Generalization’ Authors: Jastrzebski et al. (2021) πŸ“ Paper β€˜Do Vision Transformers See Like Convolutional Neural Networks?’ Authors: Raghu et al. (2021) πŸ“ Paper Source: Medium

Data science/ML/AI
Data science/ML/AI
13 690
Learning From Data Free course by Caltech - California Institute of Technology βœ… 23 sections with pdf slides and video lessons https://work.caltech.edu/library/ πŸ‘‰ Join @datascience_bds and @bigdataspecialist for more

Data science/ML/AI
Data science/ML/AI
13 690
Graph ML in Industry Workshop When I wrote top applications of GNNs at the beginning of this year, I had a feeling that graph ML community is mature enough to start being used in industrial companies. Nine months ahead we decided to gather researchers, engineers, and industry professionals to talk about applications of graphs in the companies. Please, join us on 23rd Sept, 17-00 Paris time (free, online, ~3 hours) by registering at the link.