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Data Science & Machine Learning

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Join this channel to learn data science, artificial intelligence and machine learning with funny quizzes, interesting projects and amazing resources for free For collaborations: @love_data

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Сеть: Free Courses with Certificate - Python Programming, Data Science, Java Coding, SQL, Web Development, AI, ML, ChatGPT ExpertИндия3 821Образование2 017...

📈 Аналитический обзор Telegram-канала Data Science & Machine Learning

Канал Data Science & Machine Learning (@datasciencefun) языкового сегмента Английский является активным участником. Сейчас сообщество объединяет 77 621 подписчиков, занимая 2 017 место в категории Образование и 3 821 место в регионе Индия.

📊 Показатели аудитории и динамика

С момента создания невідомо проект демонстрирует стремительный рост, собрав аудиторию из 77 621 подписчиков.

Согласно последним данным от 13 ноября, 2026, канал показывает стабильную активность. За последние 30 дней изменение числа участников составило -142, а за последние 24 часа — -20, при этом общий охват остаётся высоким.

Статус верификации: Не верифицирован
Уровень вовлечённости (ER): Средний показатель вовлечённости аудитории составляет 2.37%. В первые 24 часа после публикации контент обычно набирает 1.01% реакций от общего числа подписчиков.
Охват публикаций: В среднем каждый пост получает 1 843 просмотров. В течение первых суток публикация набирает 781 просмотров.
Реакции и взаимодействия: Аудитория активно поддерживает контент: среднее количество реакций на один пост — 3.
Тематические интересы: Контент сосредоточен на ключевых темах, таких как learning, accuracy, distribution, panda, dataset.

📝 Описание и контентная политика

Автор описывает ресурс как площадку для выражения субъективного мнения:
“Join this channel to learn data science, artificial intelligence and machine learning with funny quizzes, interesting projects and amazing resources for free For collaborations: @love_data”

Благодаря высокой частоте обновлений (последние данные получены 14 ноября, 2026) канал поддерживает актуальность и высокий уровень охвата публикаций. Аналитика показывает, что аудитория активно взаимодействует с контентом, что делает его важной точкой влияния в категории Образование.

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77 621

Подписчики

-2024 часа

-957 дней

-14230 дней

1 843

Просмотры поста

~ 78124 часа

~ 1 05048 часов

2.37%

Коэффициент вовлеченности

~ 2

Постов в день

Ads index

Архив постов

77 621

Artificial Neural Networks with Java

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LinkedIn - Python for Data Science Essential Training Part 2.zip390.08 MB

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🌐 Join the researchers and programmers channel (Courses, Books, Papere and Codes). t.me/DataScience_Books

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Advanced ML with Python

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Introduction to Data Science - A Python Approach to Concepts, Techniques and Applications - Laura Igual, Santi Segui (Springer, 2017)

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What is unsupervised learning? Unsupervised learning aims to detect patterns in the data where no labels are given.

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Tableau_Cheatsheet.pdf1.65 KB

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git-cheat-sheet-education.pdf0.98 KB

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What are precision, recall, and F1-score? Precision and recall are classification evaluation metrics: P = TP / (TP + FP) and R = TP / (TP + FN). Where TP is true positives, FP is false positives and FN is false negatives In both cases the score of 1 is the best: we get no false positives or false negatives and only true positives. F1 is a combination of both precision and recall in one score (harmonic mean): F1 = 2 * PR / (P + R). Max F score is 1 and min is 0, with 1 being the best.

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How NASA Auto Colourise Images with Deep Learning? Free Live Sessions on Aug 19th,20th @7.00pm IST Register here : https://bit.ly/2SLpFlw

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Machine Learning God by Stefan Stavrev (version 2.0).pdf1.57 MB

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Some useful Machine Learning projects for practice https://elitedatascience.com/machine-learning-projects-for-beginners https://hackernoon.com/top-5-machine-learning-projects-for-beginners-47b184e7837f https://www.springboard.com/blog/machine-learning-projects https://www.ubuntupit.com/top-20-best-machine-learning-projects-for-beginner-to-professional

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Learn how Amazon Optimise Delivery Routes using Genetic Algorithm 👇👇 https://bit.ly/3izxvZJ Free Live Session with Certificate

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Some interview questions related to Data science 1- what is difference between structured data and unstructured data. 2- what is multicollinearity.and how to remove them 3- which algorithms you use to find the most correlated features in the datasets. 4- define entropy 5- what is the workflow of principal component analysis 6- what are the applications of principal component analysis not with respect to dimensionality reduction 7- what is the Convolutional neural network. Explain me its working

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🤓 Technical Python concepts tested in the data science job interviews are: - Data types. - Built-in data structures. - User-defined data structures. - Built-in functions. - Loops and conditionals. - External libraries (Pandas). Source Article: https://www.kdnuggets.com/2021/07/top-python-data-science-interview-questions.html

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What are the benefits of a single decision tree compared to more complex models? easy to implement fast training fast inference good explainability

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What are the decision trees? This is a type of supervised learning algorithm that is mostly used for classification problems. Surprisingly, it works for both categorical and continuous dependent variables. In this algorithm, we split the population into two or more homogeneous sets. This is done based on most significant attributes/ independent variables to make as distinct groups as possible. A decision tree is a flowchart-like tree structure, where each internal node (non-leaf node) denotes a test on an attribute, each branch represents an outcome of the test, and each leaf node (or terminal node) holds a value for the target variable. Various techniques : like Gini, Information Gain, Chi-square, entropy.

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What is feature selection? Why do we need it? Feature Selection is a method used to select the relevant features for the model to train on. We need feature selection to remove the irrelevant features which leads the model to under-perform.

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