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 👉 https://rebrand.ly/bigdatachannels DMCA: @disclosure_bds Contact: @mldatascientist
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Red:📈 Análisis del canal de Telegram Data science/ML/AI
El canal Data science/ML/AI (@datascience_bds) en el segmento lingüístico de Inglés es un actor destacado. Actualmente la comunidad reúne a 13 905 suscriptores, ocupando la posición 8 911 en la categoría Tecnologías y Aplicaciones y el puesto 28 819 en la región India.
📊 Métricas de audiencia y dinámica
Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 13 905 suscriptores.
Según los últimos datos del 30 agosto, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de 90, y en las últimas 24 horas de 7, conservando un alto alcance.
- Estado de verificación: No verificado
- Tasa de interacción (ER): El promedio de interacción de la audiencia es 7.35%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 2.05% de reacciones respecto al total de suscriptores.
- Alcance de las publicaciones: Cada publicación recibe en promedio 1 022 visualizaciones. En el primer día suele acumular 285 visualizaciones.
- Reacciones e interacción: La audiencia responde de forma activa: el promedio de reacciones por publicación es 5.
- Intereses temáticos: El contenido se centra en temas clave como panda, learning, row, api, ethic.
📝 Descripción y política de contenido
El autor describe el recurso como un espacio para expresar opiniones subjetivas:
“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...”
Gracias a la alta frecuencia de actualizaciones (últimos datos recibidos el 31 agosto, 2026), el canal mantiene la vigencia y un amplio alcance. La analítica demuestra que la audiencia interactúa activamente con el contenido, lo que lo convierte en un punto de referencia dentro de la categoría Tecnologías y Aplicaciones.
13 905
Suscriptores
+724 horas
+17 días
+9030 días
Archivo de publicaciones
13 907
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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13 907
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
13 907
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
13 907
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.
13 907
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.
13 907
CS109 Data Science
By Harvard University
⌛️ 12 weeks
✅ Video lectures
✅ Slides
✅ Lab exercises
🔗 http://cs109.github.io/2015/pages/videos.html
Note: i have issues with first video link but others are fine.
#datascience #pyton #harvard
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13 907
InsightFace: 2D and 3D Face Analysis Project
Good implementation for face recognition, and landmark detection
ArcFace, CosFace, SubCenter-ArcFace, VPL, Partial-FC
https://github.com/deepinsight/insightface
13 907
Neural Networks and Deep Learning, a free online book.
The book will teach you about:
* Neural networks, a beautiful biologically-inspired programming paradigm which enables a computer to learn from observational data
* Deep learning, a powerful set of techniques for learning in neural networks
http://neuralnetworksanddeeplearning.com/index.html
13 907
Graph Neural Networks: Algorithms and Applications
A great presentation by Jian Tang about GNN basics, training many layers, self-supervised learning and statistical relational learning.
13 907
30 Days of ML, free Kaggle challenge
Machine learning beginner → Kaggle competitor in 30 days.
Non-coders welcome.
Starts August 2nd!
FAQ
I already have some familiarity with Python and/or Machine Learning. Can I still join the program?
Anyone can join! You’ll get more out of the program if you’re not a very advanced Python user, or if you are relatively new to machine learning.
What is the time commitment for the program?
Assignments should take about 1 hour/day to complete.
How much is the program?
Nothing! All you need is a Kaggle account.
Do I need to bring my own GPU or deep learning workstation?
No, Kaggle provides free hosted notebooks with access to GPUs and TPUs to complete your data science projects.
🔗 https://www.kaggle.com/thirty-days-of-ml
Sign Up for the challenge.
#kaggle #python #machinelearning #ml
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Join @bigdataspecialist for more
13 907
Introduction to Machine Learning Problem Framing
By Google
Estimated Course Length: 1 hour
https://developers.google.com/machine-learning/problem-framing
#machinelearning #ml
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13 907
AI Expert Roadmap
Below you find a set of charts demonstrating the paths that you can take and the technologies that you would want to adopt in order to become a data scientist, machine learning or an AI expert.
What is actually pretty cool is that you can click in any part of roadmap and learn more about mentioned concept!
https://i.am.ai/roadmap/
#ai #artificialintellignece #ml #machinelearning #datascience #roadmap
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13 907
Artificial Intelligence course by MIT
Professor: Patrick Winston, Ford Professor of Artificial Intelligence and Computer Science.
🎬 23 lessons
⏰ 17 hours
This course includes interactive demonstrations which are intended to stimulate interest and to help students gain intuition about how artificial intelligence methods work under a variety of circumstances.
🔗 Link to couse
🔗 Link to video lessons 🎬
#ai #artificialintellignece #mit
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13 907
CS231n: Convolutional Neural Networks for Visual Recognition
Stanford - Spring 2021
These notes accompany the Stanford CS class CS231n: Convolutional Neural Networks for Visual Recognition. You can also find google colab notebooks and all assignments here. For questions/concerns/bug reports, you can submit a pull request directly to their git repo.
🔗 https://cs231n.github.io/
#stanford #cnn #visual recognition
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13 907
ACL Year-ROUND Mentorship
Incredible opportunity from NLP community of the Association for Computational Linguistics. The students all over the world can apply and get the mentorship in their research career during the whole year!
You can discuss anything — starting from the choice of the career to the questions how to manage your time and life.
More details here:
https://mentorship.aclweb.org/Home.html
13 907
Undergraduate Machine Learning (Nando de Freitas/University of British Columbia)
Author: prof Nando de Freitas
🎬 33 lessons
⏰ 21 hours
An undergraduate machine learning course. Lectures are filmed and put on YouTube with the slides posted on the course website. The course assignments are posted as well (no solutions, though). De Freitas is now a full-time professor at the University of Oxford and receives praise for his teaching abilities in various forums. Graduate version available.
https://www.cs.ubc.ca/~nando/340-2012/index.php
#machinelearning #datascience #statistics
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13 907
Practical Deep Learning for Coders
Deep Learning for Coders with fastai and PyTorch: AI Applications Without a PhD - the book and the course
🎬 8 lessons
⏰ 16 hours
https://course.fast.ai/
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