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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 👉 https://rebrand.ly/bigdatachannels DMCA: @disclosure_bds Contact: @mldatascientist
Mostrar más📈 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 13 685 suscriptores, ocupando la posición 9 380 en la categoría Tecnologías y Aplicaciones y el puesto 31 607 en la región India.
📊 Métricas de audiencia y dinámica
Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 13 685 suscriptores.
Según los últimos datos del 10 junio, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de 143, y en las últimas 24 horas de 2, conservando un alto alcance.
- Estado de verificación: No verificado
- Tasa de interacción (ER): El promedio de interacción de la audiencia es 8.09%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 2.22% de reacciones respecto al total de suscriptores.
- Alcance de las publicaciones: Cada publicación recibe en promedio 1 106 visualizaciones. En el primer día suele acumular 304 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 11 junio, 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.
13 685
Suscriptores
+224 horas
+217 días
+14330 días
Archivo de publicaciones
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![Data Science and Machine Learning [PDF] Mathematical and Statistical Methods Dirk P. Kroese, Zdravko I. Botev, Thomas Taimre,](https://cdn.tlmtr.io/_next/static/media/empty-channel.35ed232f.svg)
Data Science and Machine Learning [PDF]
Mathematical and Statistical Methods
Dirk P. Kroese, Zdravko I. Botev, Thomas Taimre, Radislav Vaisman
8th May 2022
533 pages
🔗 Read online
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Free Big Data Courses
Complete Big Data
🎬 13 video lesson
Duration ⏰: 2-3 hours worth of material
🏃♂️ Self paced
Source: Class Central
🔗 COURSE LINK
Big Data 101 (Login Required)
⏳Modules: 6
Duration ⏰: 3 hours worth of material
🏃♂️ Self paced
Source: IBM via Cognitive Class
🔗 COURSE LINK
Introduction to Big Data - an overview of the 10 V's
Rating⭐️: 4.4 out 5
Students 👨🎓 :15,630
Duration ⏰ : 40min of on-demand video
Teacher 👨🏫: Taimur Zahid
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MIT RES.LL-005 Mathematics of Big Data and Machine Learning, IAP 2020
🎬 20 video lesson
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Source: MIT open courseware
🔗 Course Link
NOC:Big Data Computing, IIT Patna
🎬 35 video lesson
⏰ 8 Modules
Taught by: Dr. Rajiv Misra
Source: NPTEL
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Algorithms for Big Data (COMPSCI 229r)
🎬 25 video lesson
Duration ⏰: 34 hours worth of material
🏃♂️ Self paced
Source: Harvard University
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NOC:Algorithms for Big Data, IIT Madras
🎬 48 video lesson
⏰ 8 Modules
Taught by: Prof. John Augustine
Source: NPTEL
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Big Data Hadoop Tutorial for Beginners
🎬 17 video lesson
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Source: Great Learning
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Big Data Analytics Full Course In 10 Hours | Big Data Hadoop Tutorial
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Big Data Analytics
⏰ Free Online Course
🎬 70 video lesson
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Source: caltech via youtube
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Stanford Seminar - Big Data is (at least) Four Different Problems
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Free Data Mining Courses
NOC:Data Mining, IIT Kharagpur
🎬 44 video lesson
⏰ 8 Modules
Taught by: Prof. Pabitra Mitra
Source: NPTEL
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Data Mining for Beginners | Data Mining Full course | Learn Data Mining in 10 Hours | Great Learning
🎬 17 video lesson
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Source: Great Learning
🔗 COURSE LINK
NOC:Business analytics and data mining Modeling using R, IIT Roorkee
🎬 56 video lesson
⏰ 12 Modules
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NOC:Business Analytics & Data Mining Modeling Using R Part II, IIT Roorkee
🎬 20 video lesson
⏰ 4 Modules
🔗 COURSE LINK
Taught by: Dr. Gaurav Dixit
Source: NPTEL
Data Mining with Weka MOOC
✅ Free Online Course
🧱 5 modules
🎬 Video Lectures
🏃♂️ Self paced
Source: University of Waikato
Taught by: Ian H. Witten
🔗 Course Link
WEKA - Data Mining with Open Source Machine Learning Tool
Rating⭐️: 4.2 out 5
Students 👨🎓 : 12,485`
Duration ⏰ : 3hr 30min of on-demand video
Teacher 👨🏫: DATAhill Solutions Srinivas Reddy
🔗 COURSE LINK
Data Mining Crash Course
🎬 6 video lesson
Duration ⏰: 1-2 hours worth of material
🏃♂️ Self paced
Source: Data Science Dojo
🔗 Course Link
Clustering in Data mining | K means Clustering Algorithm | Hierarchical Clustering | Great Learning
🎬 86 video lesson
Duration ⏰: 3-4 hours worth of material
🏃♂️ Self paced
Source: Great Learning
🔗 Course Link
Mining Online Data Across Social Networks
⏰ Free Online Course
🎬 30 video lesson
Duration ⏰: 1-2 hours worth of material
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Source: Class Central
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DATA MINING (DM)
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Applications of Deep Neural Networks
Washington University in St. Louis
https://sites.wustl.edu/jeffheaton/t81-558/
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NOC: Reinforcement Learning, IIT Madras
🆓 Free Online Course
💻 65 Lecture Videos
⏰ 12 Modules
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Teacher 👨🏫 : Dr. B. Ravindran
🔗 https://nptel.ac.in/courses/106106143
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Hey folks, this week's round of our programming quiz league is about data science. Here is the quiz link:
http://t.me/QuizBot?start=H4Ow9sU8
Feel free to answer on those 8 short questions and let me know about your placement on final score.
Also to those who celebrate today I wish Merry Christmas 🎄🥳😊
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DIVE INTO DEEP LEARNING ||d2l.ai
Here's an Interactive deep learning book with code, math, and discussions.
Implemented with PyTorch, NumPy/MXNet, and TensorFlow.
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Stars: 15.7K
Forks:3.4K
#deep_learning #pyTorch #numPy #MXNet #TensorFlow #neural_networks
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13 684
Often the hardest part of solving a machine learning problem can be finding the right estimator for the job.
Different estimators are better suited for different types of data and different problems.
The flowchart below is designed to give users a bit of a rough guide on how to approach problems with regard to which estimators to try on your data.
Source: Scikit-learn
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Graphic SQL reference for data manipulation
Source: https://raw.githubusercontent.com/kelsfarmer/SQL/master/SQL%20Data%20Manipulation%20Language%20Cheat%20Sheet.png
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DIMENSIONALITY REDUCTION
Have you heard of Dimensionality Reduction👀?
If this is your first time😃, then get your seats closer🙂.
It means trimming down data to remove unwanted features👌.
Did this make any sense🤷♀️? If it didn't then you must know that whenever you have a very large dataset, It can help you capture the majority of your dataset's information within a few number of features.
Here's one method😃 of Dimensionality Reduction you must know.
It's the Principal Component Analysis (PCA)😎. It gives us the ability to plot multivariate data🤯 in 2 dimensions and works perfectly☺️ in identifying the axis of greatest variance in our dataset.
In this method, we take old sets of variables and convert them into a newer set. The new sets created are called principal components⭐️. There is a trade-off between reducing the number of variables while maintaining the accuracy of your model👍🏼.
The next time you have problems working with very large datasets 🤯, you could try Dimensionality Reduction😉
