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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Сітка:📈 Аналітичний огляд Telegram-каналу Data science/ML/AI
Канал Data science/ML/AI (@datascience_bds) у мовному сегменті Англійська є активним учасником. На даний момент спільнота об'єднує 13 906 підписників, посідаючи 8 914 місце в категорії Технології та додатки та 28 863 місце у регіоні Індія.
📊 Показники аудиторії та динаміка
З моменту свого створення невідомо, проект продемонстрував стрімке зростання, зібравши аудиторію у 13 906 підписників.
За останніми даними від 29 серпня, 2026, канал демонструє стабільну активність. Хоча за останні 30 днів спостерігається зміна кількості учасників на 86, а за останні 24 години на 0, загальне охоплення залишається високим.
- Статус верифікації: Не верифікований
- Рівень залученості (ER): Середній показник залученості аудиторії становить 7.48%. Протягом перших 24 годин після публікації контент зазвичай збирає 2.04% реакцій від загальної кількості підписників.
- Охоплення публікацій: В середньому кожен допис отримує 1 039 переглядів. Протягом першої доби публікація в середньому набирає 284 переглядів.
- Реакції та взаємодія: Аудиторія активно підтримує контент: середня кількість реакцій на один пост – 5.
- Тематичні інтереси: Контент зосереджений навколо ключових тем, таких як panda, learning, row, api, ethic.
📝 Опис та контентна політика
Автор описує ресурс як майданчик для висловлення суб'єктивної думки:
“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...”
Завдяки високій частоті оновлень (останні дані отримано 30 серпня, 2026), канал підтримує актуальність та високий рівень охоплення публікацій. Аналітика показує, що аудиторія активно взаємодіє з контентом, що робить його важливою точкою впливу в категорії Технології та додатки.
13 906
Підписники
Немає даних24 години
-57 днів
+8630 день
Архів дописів
13 906
Free Machine Learning Courses
Machine Learning with Python: Zero to GBMs
🎬 Watch hands-on coding-focused video tutorials
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📊 You will solve 2 coding assignments & build a course project where you'll train ML models using a large real-world datasets
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MIT RES.LL-005 Mathematics of Machine Learning and Big Data, IAP 2020
🎬 20 video lesson
Duration ⏰: 14 hours worth of material
🏃♂️ Self paced
Source: MIT open courseware
🔗 Course Link
Introduction to Machine Learning, IIT Kharagpur
🆓 Free Online Course
💻 44 Lecture Videos
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🧮 30+ Exercises
⏰ 15 hours
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📜 Lectures from Google researchers
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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
🔗 Course Link
Introduction to Machine Learning (Fall 2020)
By Massachusetts Institute of Technology, MIT
🎬 Video Lectures
⏰ Length: 13 weeks
🏃♂️ Self paced
Source: MIT
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Introduction to Machine Learning Problem Framing
By Google
⏰ Length: 1 hour
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Undergraduate Machine Learning (Nando de Freitas/University of British Columbia)
Author: prof Nando de Freitas
🎬 33 lessons
⏰ 21 hours
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Stanford CS224W course on graph ML
A legendary Stanford CS224W course on graph ML now releases videos on YouTube for 2021
🎬 60 Videos
⏰ 30h
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Books
Mathematics for Machine Learning
Approaching (Almost) Any Machine Learning Problem
Machine Learning
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Machine learning cheat sheet with code
Article
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13 906
Free Data Courses
Master course of Data Modeling
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Join @datascience_bds for more cool data science materials.
*This channel belongs to* @bigdataspecialist group
13 906
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13 906
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13 906
![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
13 906
Free Big Data Courses
Complete Big Data
🎬 13 video lesson
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Big Data 101 (Login Required)
⏳Modules: 6
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Source: IBM via Cognitive Class
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Introduction to Big Data - an overview of the 10 V's
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MIT RES.LL-005 Mathematics of Big Data and Machine Learning, IAP 2020
🎬 20 video lesson
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NOC:Big Data Computing, IIT Patna
🎬 35 video lesson
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Algorithms for Big Data (COMPSCI 229r)
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NOC:Algorithms for Big Data, IIT Madras
🎬 48 video lesson
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Big Data Hadoop Tutorial for Beginners
🎬 17 video lesson
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Big Data Analytics Full Course In 10 Hours | Big Data Hadoop Tutorial
🎬 5 video lesson
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Big Data Analytics
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Stanford Seminar - Big Data is (at least) Four Different Problems
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13 906
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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NOC:Business analytics and data mining Modeling using R, IIT Roorkee
🎬 56 video lesson
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NOC:Business Analytics & Data Mining Modeling Using R Part II, IIT Roorkee
🎬 20 video lesson
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Taught by: Dr. Gaurav Dixit
Source: NPTEL
Data Mining with Weka MOOC
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Data Mining Crash Course
🎬 6 video lesson
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Clustering in Data mining | K means Clustering Algorithm | Hierarchical Clustering | Great Learning
🎬 86 video lesson
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Mining Online Data Across Social Networks
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🎬 30 video lesson
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DATA MINING (DM)
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13 906
Applications of Deep Neural Networks
Washington University in St. Louis
https://sites.wustl.edu/jeffheaton/t81-558/
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Join @datascience_bds for more cool data science materials.
*This channel belongs to @bigdataspecialist group
13 906
NOC: Reinforcement Learning, IIT Madras
🆓 Free Online Course
💻 65 Lecture Videos
⏰ 12 Modules
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🔗 https://nptel.ac.in/courses/106106143
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13 906
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 🎄🥳😊
13 906
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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Join @datascience_bds for more cool data science materials.
*This channel belongs to @bigdataspecialist group
13 906
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
