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
13 906
Python Machine Learning (3rd Ed.) Code Repository
Paperback: 770 pages
Publisher: Packt Publishing
Language: English
https://github.com/rasbt/python-machine-learning-book-3rd-edition
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13 906
Sorry I haven't forwarded it earlier, this post belongs to this channel as well. 👆
13 906
Data Analysis free courses
The Analytics Edge (Spring 2017)
by MIT
🎬 193 video lessons
⏰ 16 hours worth of material
🔗 Courses link
Statistics and data literacy for non-statisticians
Rating ⭐️: 4.7 out of 5
Students 👨🎓: 13,320
Duration ⏰: 1h 36min
Teacher: Mike X Cohen
🔗 Courses link
Data Analysis with Python courses
by freeCodeCamp
[
Data Analysis with Python
🎬 28 video lessons
Numpy
🎬 9 video lessons
Data Analysis with Python Projects
🔖 5 projects
🔗 Courses link
]
Data Analysis w/ Python 3 and Pandas
by sentdex
🎬 6 video lessons
⏰ 2-3 hours worth of material
🔗 Course link
Master Data Analysis with Python - Intro to Pandas 2022
Rating ⭐️: 4.6 out of 5
Students 👨🎓: 3,828
Duration ⏰: 1hr 49min
Teacher: Ted Petrou
🔗 Courses link
Learn to code for data analysis
by OpenLearn
⏳ 8 weeks
🔗 Course link
Lecture notes from Statistical Thinking and Data Analysis
by MIT
🔗 Notes link
Python for Data Analysis
Rating ⭐️: 4.2 out of 5
Students 👨🎓: 14,168
Duration ⏰: 1h 10min
Teacher: Bob Wakefield
🔗 Courses link
Prepare data for analysis
by Microsoft
📁2 modules
Get data in Power BI - 12 Units
Clean, transform, and load data in Power BI - 10 Units
Duration ⏰: 3 hr 26 min
🔗 Course link
NOC:Data Analysis and Decision Making - I, IIT Kanpur
NOC:Data Analysis & Decision Making - II, IIT Kanpur
NOC:Data Analysis & Decision Making - III, IIT Kanpur
👨🏫 Prof. Raghunandan Sengupta
Each of 3 parts lasts ⏳12 weeks!
#datanalysis #dataanalysis #datascience #powerbi #dataanalytics
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13 906
When to Choose CatBoost Over XGBoost or LightGBM [Practical Guide]
Boosting algorithms have become one of the most powerful algorithms for training on structural (tabular) data.
I have been working with these 3 for years, even my bachelor thesis was comparison of these 3 algorithms alongside AdaBoost. This article explains when to use CatBoost over other ones.
https://neptune.ai/blog/when-to-choose-catboost-over-xgboost-or-lightgbm
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13 906
Repost from Cool GitHub repositories
data-science
This is a path for those of you who want to complete the Data Science undergraduate curriculum on your own time, for free, with courses from the best universities in the World.
Creator: ossu
Stars ⭐️: 14.5k
Forked By: 2.6k
GithubRepo:https://github.com/ossu/data-science
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13 906
Knowledge Graphs Course
Data Models, Knowledge Acquisition, Inference and Applications
Department of Computer Science, Stanford University, Spring 2021
⏳10 weeks, each week has slides and video lessons 📽
https://web.stanford.edu/class/cs520/
#datascience #machinelearning #tensorflow #scikitlearn #keras
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13 906
Repost from Free programming books
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 2nd Edition
by Aurélien Géron
📑 510 pages
🔗 Book link
#datascience #machinelearning #tensorflow #scikitlearn #keras
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13 906
The R Programming For Data Science A-Z Complete Diploma 2022
Rating ⭐️: 4.5 out of 5
Students 👨🎓: 38,584
Duration ⏰: 5h 6min
🔗 Course link
13 906
Reproducible Data Science with Docker
https://archive.org/details/pyconza2018-Reproducible_Data_Science_with_Docker
13 906
Intro to Machine Learning
by Kaggle
Learn the core ideas in machine learning, and build your first models.
1 How Models Work
The first step if you're new to machine learning.
2 Basic Data Exploration
Load and understand your data.
3 Your First Machine Learning Model
Building your first model. Hurray!
#machinelearning #ml
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4 Model Validation
Measure the performance of your model, so you can test and compare alternatives.
5 Underfitting and Overfitting
Fine-tune your model for better performance.
6 Random Forests
Using a more sophisticated machine learning algorithm.
7 Machine Learning Competitions
Enter the world of machine learning competitions to keep improving and see your progress.
🔗 Course link
13 906
FOUNDATIONS OF MACHINE LEARNING
by Bloomberg
Understand the Concepts, Techniques and Mathematical Frameworks Used by Experts in Machine Learning
🎬 30 video lessons with slides
⏰ 28 hours
https://bloomberg.github.io/foml/#home
#machinelearning #ml
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13 906
The Incredible PyTorch
A curated list of tutorials, papers, projects, communities and more relating to PyTorch.
https://www.ritchieng.com/the-incredible-pytorch/
#pytorch
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