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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 ๐Ÿ‘‰ https://rebrand.ly/bigdatachannels DMCA: @disclosure_bds Contact: @mldatascientist

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Programming, data science, ML - free courses by Big Data SpecialistTarmoq:Programming, data science, ML - free courses by Big Data SpecialistHindiston31 551Texnologiyalar & Aralashmalar9 384

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Data science/ML/AI (@datascience_bds) Ingliz til segmentidagi kanali faol ishtirokchi. Hozirda hamjamiyat 13 690 obunachidan iborat bo'lib, Texnologiyalar & Aralashmalar toifasida 9 384-o'rinni va Hindiston mintaqasida 31 551-o'rinni egallagan.

๐Ÿ“Š Auditoriya koโ€˜rsatkichlari va dinamika

ะฝะตะฒั–ะดะพะผะพ sanasidan buyon loyiha tez oโ€˜sib, 13 690 obunachiga ega boโ€˜ldi.

11 Iyun, 2026 dagi oxirgi maโ€™lumotlarga koโ€˜ra kanal barqaror faollikka ega. Oxirgi 30 kunda obunachilar soni 150 ga, soโ€˜nggi 24 soatda esa 11 ga oโ€˜zgardi va umumiy qamrov yuqori darajada qolmoqda.

  • Tasdiqlash holati: Tasdiqlanmagan
  • Jalb etish (ER): Auditoriya oโ€˜rtacha 8.13% darajada jalb etiladi. Nashrdan keyingi dastlabki 24 soatda kontent odatda umumiy obunachilar sonining 2.20% ini tashkil etuvchi reaksiyalarni toโ€˜playdi.
  • Post qamrovi: Har bir post oโ€˜rtacha 1 112 marta koโ€˜riladi; birinchi sutkada odatda 301 ta koโ€˜rish yigโ€˜iladi.
  • Reaksiyalar va oโ€˜zaro taโ€™sir: Auditoriya faol: har bir postga oโ€˜rtacha 5 ta reaksiya keladi.
  • Tematik yoโ€˜nalishlar: Kontent panda, learning, row, api, ethic kabi asosiy mavzularga jamlangan.

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Muallif resursni shaxsiy fikrni ifoda etish maydoni sifatida taโ€™riflaydi:
โ€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 ๐Ÿ‘‰ https://rebrand.ly/bigdatachannels DMCA: @disclosure_bds Contact: @mldatasci...โ€

Yuqori yangilanish chastotasi (oxirgi maโ€™lumot 12 Iyun, 2026 da olingan) sababli kanal doimo dolzarb va katta qamrovli boโ€˜lib qoladi. Analitika auditoriya kontent bilan faol hamkorlik qilishini, uni Texnologiyalar & Aralashmalar toifasidagi muhim taโ€™sir nuqtasiga aylantirishini koโ€˜rsatadi.

13 690
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Postlar arxiv
Data science/ML/AI
Data science/ML/AI
13 690
๐Ÿ”— Book link #machinelearning #ml #datascience โž–โž–โž–โž–โž–โž–โž–โž–โž–โž–โž–โž–โž–โž– Join @coding_interview_preparation for more. *This channel belo
๐Ÿ”— Book link #machinelearning #ml #datascience โž–โž–โž–โž–โž–โž–โž–โž–โž–โž–โž–โž–โž–โž– 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/ โž–โž–โž–โž–โž–โž–โž–โž–โž–โž–โž–โž–โž–โž– 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 ๐Ÿ‘จโ€๐Ÿซ Teachers: Michael M. Bronstein, Joan Bruna, Taco Cohen, Petar Veliฤkoviฤ‡ ๐Ÿ“š12 lectures, 2 tutorials, and 4 seminars This course follows GDL BOOK ๐Ÿ”— Course link: https://geometricdeeplearning.com/lectures/ Machine Learning for Graphs and Sequential Data (MLGS) by Stephan Gรผnnemann Awesome course covering in depth generative models, robustness, sequential data, clustering, label propagation, GNNs, and more ๐Ÿ”— 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 ๐ŸŽฌ 60 Videos โฐ 30h ๐Ÿ”— Course link Python For Data Science (Udemy) This course specifically created for Data Science / AI / ML / DL. It covers BASICS PYTHON ONLY Rating โญ๏ธ: 4.1 out of 5 Students ๐Ÿ‘จโ€๐ŸŽ“: 65,523 students Duration โฐ: 3hr 55min of on-demand video ๐Ÿ”— Course link Deep Learning Prerequisites: The Numpy Stack in Python V2 (Udemy) Rating โญ๏ธ: 4.6 out of 5 Students ๐Ÿ‘จโ€๐ŸŽ“: 34,785 Duration โฐ: 1hr 59min of on-demand video ๐Ÿ”— Course link There is also this cool blogpost by Gordiฤ‡ Aleksa: How to get started with Graph Machine Learning And one early access version book: Graph Powered Machine Learning by: Allesandro Negro ๐Ÿ”— Book link #graphML #ML #machinelearning #deeplearning #python โž–โž–โž–โž–โž–โž–โž–โž–โž–โž–โž–โž–โž–โž– ๐Ÿ‘‰Join @bigdataspecialist for more๐Ÿ‘ˆ

Data science/ML/AI
Data science/ML/AI
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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. ๐Ÿ‘‰ 43 ML Rules to follow ๐Ÿ”— http://martin.zinkevich.org/rules_of_ml/rules_of_ml.pdf โž–โž–โž–โž–โž–โž–โž–โž–โž–โž–โž–โž–โž–โž– Join @datascience_bds for more cool data science materials. *This channel belongs to @bigdataspecialist group

Data science/ML/AI
Data science/ML/AI
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Machine Learning for Healthcare (Spring 2019) By Massachusetts Institute of Technology (MIT) ๐ŸŽฌ 25 video lessons โฐ 33 hours ๐Ÿ‘จโ€๐Ÿซ Prof. Peter Szolovits ๐Ÿ‘จโ€๐Ÿซ Prof. David Sontag https://www.classcentral.com/course/mit-opencourseware-machine-learning-for-healthcare-spring-2019-40955/classroom #ml #machinelearning #healthcare #MIT โž–โž–โž–โž–โž–โž–โž–โž–โž–โž–โž–โž–โž–โž– 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/ โž–โž–โž–โž–โž–โž–โž–โž–โž–โž–โž–โž–โž–โž– Join @datascience_bds for more cool DS/ML materials.

Data science/ML/AI
Data science/ML/AI
13 690
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 ๐Ÿ”— Course link: https://www.freecodecamp.org/news/free-machine-learning-course-10-hourse/ 10-hour youtube video: https://www.youtube.com/watch?v=NWONeJKn6kc โž–โž–โž–โž–โž–โž–โž–โž–โž–โž–โž–โž–โž–โž– 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 โญ๏ธ: 4.3 out of 5 Students ๐Ÿ‘จโ€๐Ÿซ: 104,287 Created by: Ahmed Ibrahim and SDE OCTOPUS | AI ๐Ÿ”— 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 โž–โž–โž–โž–โž–โž–โž–โž–โž–โž–โž–โž–โž–โž– 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 ๐ŸŽฌ 95 video sessions โฐ Duration: 16h ๐Ÿ‘จโ€๐Ÿซ Instructor: Bill Howe, Ph
Introduction to Data Science by University of Washington ๐ŸŽฌ 95 video sessions โฐ Duration: 16h ๐Ÿ‘จโ€๐Ÿซ Instructor: Bill Howe, PhD โœ… Completely free ๐Ÿ”— Course link #datascience #ds #ml #washingtonuniversity โž–โž–โž–โž–โž–โž–โž–โž–โž–โž–โž–โž–โž–โž– ๐Ÿ‘‰Join @bigdataspecialist for more๐Ÿ‘ˆ

Data science/ML/AI
Data science/ML/AI
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Introduction to Machine Learning (Fall 2020) By Massachusetts Institute of Technology, MIT Length: 13 weeks ๐Ÿ”— Course link #m
Introduction to Machine Learning (Fall 2020) By Massachusetts Institute of Technology, MIT Length: 13 weeks ๐Ÿ”— Course link #ml #machinelearning #datascience #MIT โž–โž–โž–โž–โž–โž–โž–โž–โž–โž–โž–โž–โž–โž– 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 ๐Ÿ”— Book PDF #javascript #datascience #neuralnetworks โž–โž–โž–โž–โž–โž–โž–โž–โž–โž–โž–โž–โž– Join @programmi
Neural Networks with JavaScript Succinctly ๐Ÿ”— Book PDF #javascript #datascience #neuralnetworks โž–โž–โž–โž–โž–โž–โž–โž–โž–โž–โž–โž–โž– 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 ๐ŸŽฌ 16 lessons โฐ 15 hours https://www.youtube.com/playlist?list=PLE6Wd9FR--EfW8dtjAuPoTuPcqmOV53Fu #deeplearning #oxford โž–โž–โž–โž–โž–โž–โž–โž–โž–โž–โž–โž–โž–โž– 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 ๐ŸŽฌ 13 lessons โฐ 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 โž–โž–โž–โž–โž–โž–โž–โž–โž–โž–โž–โž–โž–โž– 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 โ€˜Auto-Sklearn 2.0: Hands-free AutoML via Meta-Learningโ€™ Authors: Feurer et al. (2021) ๐Ÿ“ Paper ๐Ÿค– Code โ€˜How to train your ViT? Data, Augmentation, and Regularization in Vision Transformersโ€™ Authors: Steiner et al. (2021) ๐Ÿ“ Paper ๐Ÿค– Code โ€˜Catastrophic Fisher Explosion: Early Phase Fisher Matrix Impacts Generalizationโ€™ Authors: Jastrzebski et al. (2021) ๐Ÿ“ Paper โ€˜Do Vision Transformers See Like Convolutional Neural Networks?โ€™ Authors: Raghu et al. (2021) ๐Ÿ“ Paper Source: Medium

Data science/ML/AI
Data science/ML/AI
13 690
Learning From Data Free course by Caltech - California Institute of Technology โœ… 23 sections with pdf slides and video lessons https://work.caltech.edu/library/ ๐Ÿ‘‰ 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.