Data science/ML/AI

Ir al canal en 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

Red:Programming, data science, ML - free courses by Big Data Specialist India28 863 Tecnologías y Aplicaciones8 914...

📈 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 13 906 suscriptores, ocupando la posición 8 914 en la categoría Tecnologías y Aplicaciones y el puesto 28 863 en la región India.

📊 Métricas de audiencia y dinámica

Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 13 906 suscriptores.

Según los últimos datos del 29 agosto, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de 86, y en las últimas 24 horas de 0, conservando un alto alcance.

Estado de verificación: No verificado
Tasa de interacción (ER): El promedio de interacción de la audiencia es 7.48%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 2.04% de reacciones respecto al total de suscriptores.
Alcance de las publicaciones: Cada publicación recibe en promedio 1 039 visualizaciones. En el primer día suele acumular 284 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 30 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.

13 901

Suscriptores

Sin datos24 horas

-57 días

+8630 días

1 039

Visitas de la publicación

~ 28424 horas

~ 41048 horas

7.48%

Tasa de compromiso

~ 1

Mensajes por día

Ads index

beta

Archivo de publicaciones

13 906

Repost from Python Learning

Netflix ML Architecture

13 906

Finding your career path in Data

13 906

Data Analysis Masterclass Using Spreadsheet, MS excel and Tableau This is a Condensed course that will teach you all you need to know about how to get started and get going with data analysis we will start with learning how to use Excel and spreadsheet then use Tableau for Data analysis. This course is made for anyone who wants to learn the art of Data analysis and data visualization. We will begin by learning how to use Excel and Spreadsheet then we will learn the various steps for doing data analysis which are data cleaning, preparation and finally data visualization after which we will use all of these learned skills to create a real interaction dashboard for our projects in Tableau. This course should prepare you to utilize Excel, Spreadsheet and Tableau to do your next analysis with confidence. You will be able to proudly showcase your skills to the world and add it to your resume. 🆓 Free Online Course 🎬 video lessons 🏃‍♂️ Self paced Modules ⏰: 4 Source: Skill Share 🔗 Course Link #Data_Analysis #Excel #Tableau ➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖ Join @datascience_bds for more cool data science materials. *This channel belongs to @bigdataspecialist group

13 906

Data Scientist, Data Engineer and Data Analyst

13 906

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

13 906

Data Science vs AI vs ML

13 906

Deep Learning Notes

13 906

Introduction to the Data Science Process

13 906

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

13 906

Amazon Data Scientist Interview Process

13 906

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👈

13 906

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

13 906

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

13 906

Data Science with other fields of science

13 906

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

13 906

100 Days of Data Science Challenge

13 906

Why choose data science

13 906

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👈

13 906

Data Science Components