متاح الآن! بحث تيليغرام 2025 — أهم رؤى العام 
Data science/ML/AI
الذهاب إلى القناة على Telegram
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
إظهار المزيد📈 نظرة تحليلية على قناة تيليجرام Data science/ML/AI
تُعد قناة Data science/ML/AI (@datascience_bds) في القطاع اللغوي الإنكليزية لاعباً نشطاً. يضم المجتمع حالياً 13 684 مشتركاً، محتلاً المرتبة 9 384 في فئة التكنولوجيات والتطبيقات والمرتبة 31 551 في منطقة الهند.
📊 مؤشرات الجمهور والحراك
منذ تأسيسه في невідомо، حقق المشروع نمواً سريعاً وجمع 13 684 مشتركاً.
بحسب آخر البيانات بتاريخ 11 يونيو, 2026، تحافظ القناة على نشاط مستقر. خلال آخر 30 يوماً تغيّر عدد الأعضاء بمقدار 150، وفي آخر 24 ساعة بمقدار 11، مع بقاء الوصول العام مرتفعاً.
- حالة التحقق: غير موثّقة
- معدل التفاعل (ER): يبلغ متوسط تفاعل الجمهور 8.13%. وخلال أول 24 ساعة من النشر يحصد المحتوى عادةً 2.20% من ردود الفعل نسبةً إلى إجمالي المشتركين.
- وصول المنشورات: يحصل كل منشور على متوسط 1 112 مشاهدة. وخلال اليوم الأول يجمع عادةً 301 مشاهدة.
- التفاعلات والاستجابة: يتفاعل الجمهور بانتظام؛ متوسط التفاعلات لكل منشور يبلغ 5.
- الاهتمامات الموضوعية: يركز المحتوى على مواضيع رئيسية مثل panda, learning, row, api, ethic.
📝 الوصف وسياسة المحتوى
يصف المؤلف القناة بأنها مساحة للتعبير عن الآراء الذاتية:
“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...”
بفضل وتيرة التحديث المرتفعة (أحدث البيانات بتاريخ 12 يونيو, 2026) تحافظ القناة على حداثتها ومستوى وصول مرتفع. وتُظهر التحليلات تفاعلاً نشطاً من الجمهور، ما يجعلها نقطة تأثير مهمة ضمن فئة التكنولوجيات والتطبيقات.
13 684
المشتركون
+1124 ساعات
+227 أيام
+15030 أيام
أرشيف المشاركات
13 690
🔗 Book link
#machinelearning #ml #datascience
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Join @coding_interview_preparation for more.
*This channel belongs to @bigdataspecialist group
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/
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Join @datascience_bds for more cool data science materials.
*This channel belongs to @bigdataspecialist group
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
👨🏫 Teachers: Michael M. Bronstein, Joan Bruna, Taco Cohen, Petar Veličković
📚12 lectures, 2 tutorials, and 4 seminars
This course follows GDL BOOK
🔗 Course link: https://geometricdeeplearning.com/lectures/
Machine Learning for Graphs and Sequential Data (MLGS)
by Stephan Günnemann
Awesome course covering in depth generative models, robustness, sequential data, clustering, label propagation, GNNs, and more
🔗 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
🔗 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 👨🎓: 65,523 students
Duration ⏰: 3hr 55min of on-demand video
🔗 Course link
Deep Learning Prerequisites: The Numpy Stack in Python V2 (Udemy)
Rating ⭐️: 4.6 out of 5
Students 👨🎓: 34,785
Duration ⏰: 1hr 59min of on-demand video
🔗 Course link
There is also this cool blogpost by Gordić Aleksa:
How to get started with Graph Machine Learning
And one early access version book:
Graph Powered Machine Learning
by: Allesandro Negro
🔗 Book link
#graphML #ML #machinelearning #deeplearning #python
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👉Join @bigdataspecialist for more👈
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.
👉 43 ML Rules to follow
🔗 http://martin.zinkevich.org/rules_of_ml/rules_of_ml.pdf
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Join @datascience_bds for more cool data science materials.
*This channel belongs to @bigdataspecialist group
13 690
Machine Learning for Healthcare (Spring 2019)
By Massachusetts Institute of Technology (MIT)
🎬 25 video lessons
⏰ 33 hours
👨🏫 Prof. Peter Szolovits
👨🏫 Prof. David Sontag
https://www.classcentral.com/course/mit-opencourseware-machine-learning-for-healthcare-spring-2019-40955/classroom
#ml #machinelearning #healthcare #MIT
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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/
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Join @datascience_bds for more cool DS/ML materials.
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
🔗 Course link: https://www.freecodecamp.org/news/free-machine-learning-course-10-hourse/
10-hour youtube video: https://www.youtube.com/watch?v=NWONeJKn6kc
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Join @datascience_bds for more cool DS/ML materials.
13 690
Data Science: Python for Data Analysis 2022 Full Bootcamp
Rating ⭐️: 4.3 out of 5
Students 👨🏫: 104,287
Created by: Ahmed Ibrahim and SDE OCTOPUS | AI
🔗 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
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*This channel belongs to @bigdataspecialist group
13 690
Introduction to Data Science by University of Washington
🎬 95 video sessions
⏰ Duration: 16h
👨🏫 Instructor: Bill Howe, PhD
✅ Completely free
🔗 Course link
#datascience #ds #ml #washingtonuniversity
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👉Join @bigdataspecialist for more👈
13 690
Introduction to Machine Learning (Fall 2020)
By Massachusetts Institute of Technology, MIT
Length: 13 weeks
🔗 Course link
#ml #machinelearning #datascience #MIT
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13 690
Neural Networks with JavaScript Succinctly
🔗 Book PDF
#javascript #datascience #neuralnetworks
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13 690
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
13 690
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
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/
13 690
Deep learning at Oxford 2015
🎬 16 lessons
⏰ 15 hours
https://www.youtube.com/playlist?list=PLE6Wd9FR--EfW8dtjAuPoTuPcqmOV53Fu
#deeplearning #oxford
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Join @datascience_bds for more cool data science materials.
*This channel belongs to @bigdataspecialist group
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
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*This channel belongs to @bigdataspecialist group
13 690
Four Deep Learning Papers to Read in September 2021
‘Auto-Sklearn 2.0: Hands-free AutoML via Meta-Learning’
Authors: Feurer et al. (2021)
📝 Paper
🤖 Code
‘How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers’
Authors: Steiner et al. (2021)
📝 Paper
🤖 Code
‘Catastrophic Fisher Explosion: Early Phase Fisher Matrix Impacts Generalization’
Authors: Jastrzebski et al. (2021)
📝 Paper
‘Do Vision Transformers See Like Convolutional Neural Networks?’
Authors: Raghu et al. (2021)
📝 Paper
Source: Medium
13 690
Learning From Data
Free course by Caltech - California Institute of Technology
✅ 23 sections with pdf slides and video lessons
https://work.caltech.edu/library/
👉 Join @datascience_bds and @bigdataspecialist for more
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.
