Вже доступно! Дослідження Telegram за 2025 — головні інсайти року 
Data Science & Machine Learning
Відкрити в Telegram
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
Показати більше📈 Аналітичний огляд Telegram-каналу Data Science & Machine Learning
Канал Data Science & Machine Learning (@datasciencefun) у мовному сегменті Англійська є активним учасником. На даний момент спільнота об'єднує 75 822 підписників, посідаючи 2 109 місце в категорії Освіта та 4 254 місце у регіоні Індія.
📊 Показники аудиторії та динаміка
З моменту свого створення невідомо, проект продемонстрував стрімке зростання, зібравши аудиторію у 75 822 підписників.
За останніми даними від 20 червня, 2026, канал демонструє стабільну активність. Хоча за останні 30 днів спостерігається зміна кількості учасників на 833, а за останні 24 години на 1, загальне охоплення залишається високим.
- Статус верифікації: Не верифікований
- Рівень залученості (ER): Середній показник залученості аудиторії становить 3.15%. Протягом перших 24 годин після публікації контент зазвичай збирає 1.15% реакцій від загальної кількості підписників.
- Охоплення публікацій: В середньому кожен допис отримує 2 391 переглядів. Протягом першої доби публікація в середньому набирає 875 переглядів.
- Реакції та взаємодія: Аудиторія активно підтримує контент: середня кількість реакцій на один пост – 3.
- Тематичні інтереси: Контент зосереджений навколо ключових тем, таких як learning, accuracy, distribution, panda, dataset.
📝 Опис та контентна політика
Автор описує ресурс як майданчик для висловлення суб'єктивної думки:
“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”
Завдяки високій частоті оновлень (останні дані отримано 21 червня, 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 Освіта.
75 822
Підписники
+124 години
+1047 днів
+83330 день
Архів дописів
1. Mention The Different Types Of Data Structures In pandas?
There are two data structures supported by pandas library, Series and DataFrames. Both of the data structures are built on top of Numpy. Series is a one-dimensional data structure in pandas and DataFrame is the two-dimensional data structure in pandas. There is one more axis label known as Panel which is a three-dimensional data structure and it includes items, major_axis, and minor_axis.
2. Why is KNN a non-parametric Algorithm?
The term “non-parametric” refers to not making any assumptions on the underlying data distribution. These methods do not have any fixed numbers of parameters in the model.
Similarly in KNN, the model parameters grow with the training data by considering each training case as a parameter of the model. So, KNN is a non-parametric algorithm.
3. Explain the CART Algorithm for Decision Trees.
CART is a variation of the decision tree algorithm. It can handle both classification and regression tasks.The CART stands for Classification and Regression Trees is a greedy algorithm that greedily searches for an optimum split at the top level, then repeats the same process at each of the subsequent levels. Moreover, it does verify whether the split will lead to the lowest impurity or not as well as the solution provided by the greedy algorithm is not guaranteed to be optimal, it often produces a solution that’s reasonably good since finding the optimal Tree is an NP-Complete problem that requires exponential time complexity.
4. Explain leave-p-out cross validation.
When using this exhaustive method, we take p number of points out from the total number of data points in the dataset(say n). While training the model we train it on these (n – p) data points and test the model on p data points. We repeat this process for all the possible combinations of p from the original dataset. Then to get the final accuracy, we average the accuracies from all these iterations.
Machine Learning and AI Foundations: Causal Inference and Modeling
Machine Learning Bookcamp Build a portfolio of real-life pr.pdf40.02 MB
1. What are Different Kernels in SVM?
Linear kernel - used when data is linearly separable.
Polynomial kernel - When you have discrete data that has no natural notion of smoothness.
Radial basis kernel - Create a decision boundary able to do a much better job of separating two classes than the linear kernel.
Sigmoid kernel - used as an activation function for neural networks.
2. What is Cross-Validation?
Cross-validation is a method of splitting all your data into three parts: training, testing, and validation data. Data is split into k subsets, and the model has trained on k-1of those datasets.
The last subset is held for testing. This is done for each of the subsets. This is k-fold cross-validation. Finally, the scores from all the k-folds are averaged to produce the final score.
3. List the different types of relationships in SQL.
One-to-One - This can be defined as the relationship between two tables where each record in one table is associated with the maximum of one record in the other table.
One-to-Many & Many-to-One - This is the most commonly used relationship where a record in a table is associated with multiple records in the other table.
Many-to-Many - This is used in cases when multiple instances on both sides are needed for defining a relationship.
Self-Referencing Relationships - This is used when a table needs to define a relationship with itself.
4. What Are the Data Types Supported in Tableau?
Following data types are supported in Tableau:
Text (string) values
Date values
Date and time values
Numerical values
Boolean values (relational only)
Geographical values (used with maps)
ENJOY LEARNING 👍👍
class Coding game TONight:
def __init__(Write code, Have fun, Earn crypto):
self.Write code = Write code
self.Have fun = Have fun
self.Earn crypto = Earn crypto
print(You woke up on January 19, 2038 and realized that the world has changed a lot. Gaming NPC need the help of a person who knows Programming language and they will reward with crypto TON)
Follow the link and start the game right now:
👉 https://cutt.ly/i7eIAQ5
1. What are the uses of using RNN in NLP?
The RNN is a stateful neural network, which means that it not only retains information from the previous layer but also from the previous pass. Thus, this neuron is said to have connections between passes, and through time.
For the RNN the order of the input matters due to being stateful. The same words with different orders will yield different outputs.
RNN can be used for unsegmented, connected applications such as handwriting recognition or speech recognition.
2. How to remove values to a python array?
Ans: Array elements can be removed using pop() or remove() method. The difference between these two functions is that the former returns the deleted value whereas the latter does not.
3. What are the advantages and disadvantages of views in the database?
Answer: Advantages of Views:
As there is no physical location where the data in the view is stored, it generates output without wasting resources.
Data access is restricted as it does not allow commands like insertion, updation, and deletion.
Disadvantages of Views:
The view becomes irrelevant if we drop a table related to that view.
Much memory space is occupied when the view is created for large tables.
4. How to create a calculated field in Tableau?
Click the drop down to the right of Dimensions on the Data pane and select “Create > Calculated Field” to open the calculation editor.
Name the new field and create a formula.
ENJOY LEARNING 👍👍
Complete Data Science BootCamp
👇👇
https://t.me/udemy_free_courses_with_certi/1644
[Free for limited time only]
1. What are decorators in Python?
Ans: Decorators are used to add some design patterns to a function without changing its structure. Decorators generally are defined before the function they are enhancing. To apply a decorator we first define the decorator function. Then we write the function it is applied to and simply add the decorator function above the function it has to be applied to. For this, we use the @ symbol before the decorator.
2. What is the ACID property in a database?
The full form of ACID is atomicity, consistency, isolation, and durability.
• Atomicity refers that if any aspect of a transaction fails, the whole transaction fails and the database state remains unchanged.
• Consistency means that the data meets all validity guidelines.
• Concurrency management is the primary objective of isolation.
• Durability ensures that once a transaction is committed, it will occur regardless of what happens in between such as a power outage, fire, or some other kind of disturbance.
3. What is the meaning of KPI in statistics?
KPI is an acronym for a key performance indicator. It can be defined as a quantifiable measure to understand whether the goal is being achieved or not. KPI is a reliable metric to measure the performance level of an organization or individual with respect to the objectives. An example of KPI in an organization is the expense ratio.
4. Explain One-hot encoding and Label Encoding. How do they affect the dimensionality of the given dataset?
One-hot encoding is the representation of categorical variables as binary vectors. Label Encoding is converting labels/words into numeric form. Using one-hot encoding increases the dimensionality of the data set. Label encoding doesn’t affect the dimensionality of the data set. One-hot encoding creates a new variable for each level in the variable whereas, in Label encoding, the levels of a variable get encoded as 1 and 0.
Do you enjoy reading this channel?
Perhaps you have thought about placing ads on it?
To do this, follow three simple steps:
1) Sign up: https://telega.io/c/datasciencefun
2) Top up the balance in a convenient way
3) Create an advertising post
If the topic of your post fits our channel, we will publish it with pleasure.
ARTIFICIAL_INTELLIGENCE_FOR_ROBOTICS @computer_books.pdf26.27 MB
