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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
Больше📈 Аналитический обзор Telegram-канала Data Science & Machine Learning
Канал Data Science & Machine Learning (@datasciencefun) языкового сегмента Английский является активным участником. Сейчас сообщество объединяет 75 860 подписчиков, занимая 2 107 место в категории Образование и 4 219 место в регионе Индия.
📊 Показатели аудитории и динамика
С момента создания невідомо проект демонстрирует стремительный рост, собрав аудиторию из 75 860 подписчиков.
Согласно последним данным от 22 июня, 2026, канал показывает стабильную активность. За последние 30 дней изменение числа участников составило 728, а за последние 24 часа — -2, при этом общий охват остаётся высоким.
- Статус верификации: Не верифицирован
- Уровень вовлечённости (ER): Средний показатель вовлечённости аудитории составляет 3.00%. В первые 24 часа после публикации контент обычно набирает 1.05% реакций от общего числа подписчиков.
- Охват публикаций: В среднем каждый пост получает 2 278 просмотров. В течение первых суток публикация набирает 794 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 23 июня, 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.
75 860
Подписчики
-224 часа
+637 дней
+72830 день
Архив постов
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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
🤓 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
What are the benefits of a single decision tree compared to more complex models?
easy to implement
fast training
fast inference
good explainability
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.
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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Quiz Explaination
Supervised Learning: All data is labeled and the algorithms learn to predict the output from the
input data
Unsupervised Learning: All data is unlabeled and the algorithms learn to inherent structure from
the input data.
Semi-supervised Learning: Some data is labeled but most of it is unlabeled and a mixture of
supervised and unsupervised techniques can be used to solve problem.
Unsupervised learning problems can be further grouped into clustering and association problems.
Clustering: A clustering problem is where you want to discover the inherent groupings
in the data, such as grouping customers by purchasing behavior.
Association: An association rule learning problem is where you want to discover rules
that describe large portions of your data, such as people that buy A also tend to buy B.
Which of the following is not a type of unsupervised Learning?
In which technique, data is unlabeled and the algorithms learn to inherent structure from the input data?
Master_Machine_Learning_Algorithms_Discover_how_they_work_by_Jason.pdf1.09 MB
What are the main parameters of the random forest model?
max_depth: Longest Path between root node and the leaf
min_sample_split: The minimum number of observations needed to split a given node
max_leaf_nodes: Conditions the splitting of the tree and hence, limits the growth of the trees
min_samples_leaf: minimum number of samples in the leaf node
n_estimators: Number of trees
max_sample: Fraction of original dataset given to any individual tree in the given model
max_features: Limits the maximum number of features provided to trees in random forest model
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What are the main parameters in the gradient boosting model?
There are many parameters, but below are a few key defaults.
learning_rate=0.1 (shrinkage).
n_estimators=100 (number of trees).
max_depth=3.
min_samples_split=2.
min_samples_leaf=1.
subsample=1.0.
Which of the following Python Library can be exclusively used to plot graphs?
How does L2 regularization look like in a linear model?
L2 regularization adds a penalty term to our cost function which is equal to the sum of squares of models coefficients multiplied by a lambda hyperparameter.
This technique makes sure that the coefficients are close to zero and is widely used in cases when we have a lot of features that might correlate with each other.
Which regularization techniques do you know?
There are mainly two types of regularization,
L1 Regularization (Lasso regularization) - Adds the sum of absolute values of the coefficients to the cost function.
L2 Regularization (Ridge regularization) - Adds the sum of squares of coefficients to the cost function
Here, Lambda determines the amount of regularization.
