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

Red:Programming, data science, ML - free courses by Big Data Specialist India31 607 Tecnologías y Aplicaciones9 380...

📈 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

1 106

Visitas de la publicación

~ 30424 horas

~ 44448 horas

8.09%

Tasa de compromiso

~ 1

Mensajes por día

Ads index

beta

Archivo de publicaciones

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Data Scientist, Data Engineer and Data Analyst

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Accelerating Deep Learning with GPUs (Login Required) Training complex deep learning models with large datasets takes along time. In this course, you will learn how to use accelerated GPU hardware to overcome the scalability problem in deep learning. You can use accelerated hardware such as Google’s Tensor Processing Unit (TPU) or Nvidia GPU to accelerate your convolutional neural network computations time on the Cloud. These chips are specifically designed to support the training of neural networks, as well as the use of trained networks (inference). Accelerated hardware has recently been proven to significantly reduce training time. 🆓 Free Online Course Rating⭐️: 4.7 out 5 🎬 video lesson 🏃‍♂️ Self paced Duration ⏰: More than 7 hours worth of material Source: cognitiveclass 🔗 Course Link #deep_Learning ➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖ Join @datascience_bds for more cool data science materials. *This channel belongs to @bigdataspecialist group

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Data Science vs AI vs ML

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Deep Learning Notes

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Introduction to the Data Science Process

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Data Science Ethics (Login Required) Utilize the framework provided in the course to analyze concerns related to data science ethics. Explore the broader impact of the data science field on modern society and the principles of fairness, accountability and transparency. Examine the need for voluntary disclosure when leveraging metadata to inform basic algorithms and/or complex artificial intelligence systems. Learn best practices for responsible data management. Gain an understanding of the significance of the Fair Information Practices Principles Act and the laws concerning the "right to be forgotten." 🎬 video lessons Rating⭐️: 4.1 out 5 🏃‍♂️ Self paced Source: University of Michigan 🔗 Course Link #data_science ➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖ Join @datascience_bds for more cool data science materials. *This channel belongs to @bigdataspecialist group

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Amazon Data Scientist Interview Process

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MIT 6.S191: Introduction to Deep Learning 2021 Created by MIT ⏰ 29 hours worth of material 🎬 43 Video lessons 👨‍🏫 Teacher: Alexander Amini 🔗 Course link #deeplearning #ai #MIT ➖➖➖➖➖➖➖➖➖➖➖➖➖➖ 👉Join @bigdataspecialist for more👈

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Your Guide to Latent Dirichlet Allocation Latent Dirichlet Allocation (LDA) is a “generative probabilistic model” of a collection of composites made up of parts. Its uses include Natural Language Processing (NLP) and topic modelling, among others. In terms of topic modelling, the composites are documents and the parts are words and/or phrases (phrases n words in length are referred to as n-grams). But you could apply LDA to DNA and nucleotides, pizzas and toppings, molecules and atoms, employees and skills, or keyboards and crumbs. The probabilistic topic model estimated by LDA consists of two tables (matrices). The first table describes the probability or chance of selecting a particular part when sampling a particular topic (category). Link #ml #data_science ➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖ Join @datascience_bds for more cool data science materials. *This channel belongs to @bigdataspecialist group

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Foundations of Data Science by Avrim Blum, John Hopcroft, and Ravindran Kannan 📄 479 pages #data_science #foundations_of_data_Science ➖➖➖➖➖➖➖➖➖➖➖➖➖ Join @datascience_bds for more

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Data Science with other fields of science

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Big and Sparse Data Sciences Integration with Theory, Experiment, Simulations, and Uncertainty Quantification

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100 Days of Data Science Challenge

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Why choose data science

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Data Science for Engineers, IIT Madras 🆓 Free Online Course 💻 50 Lecture Videos ⏰ 8 Module 🏃‍♂️ Self paced Teacher 👨‍🏫 : Prof. Shankar Narasimhan, Prof. Ragunathan Rengasamy 🔗 https://nptel.ac.in/courses/106106179 #Data_Science #IIT ➖➖➖➖➖➖➖➖➖➖➖➖➖➖ 👉Join @bigdataspecialist for more👈

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Data Science Components

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R for Data Science A weekly data project aimed at the R ecosystem. As this project was borne out of the R4DS Online Learning Community and the R for Data Science textbook, an emphasis was placed on understanding how to summarize and arrange data to make meaningful charts with ggplot2, tidyr, dplyr, and other tools in the tidyverse ecosystem. However, any code-based methodology is welcome - just please remember to share the code used to generate the results. Creator: rfordatascience Stars ⭐️: 5.6k Forked By: 2.3k https://github.com/rfordatascience/tidytuesday #R #data_science ➖➖➖➖➖➖➖➖➖➖➖➖➖➖ Join @datascience_bds for more cool repositories. *This channel belongs to @bigdataspecialist group

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21 most important equations in data science

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Essential Charts for Data Analysis