Today's Interview Quest
Company Name - Bridgei2i Analytics
Role - Data Scientist
Q1. How would you remove bias?
Ans. Ways to minimize Bias in ML:
Choose the correct learning model. There are two types of learning models, and each has its own pros and cons.
Use the right training dataset.
Perform data processing mindfully.
Monitor real-world performance across the ML lifecycle.
Make sure that there are no infrastructural issues.
Q2. In what situation would you consider mean over median
Ans. If your data contains outliers, then you would typically rather use the median because otherwise the value of the mean would be dominated by the outliers rather than the typical values. In conclusion, if you are considering the mean, check your data for outliers, if any then better choose median.
Q3. LASSO & RIDGE?
Ans. Ridge and Lasso regression are some of the simple techniques to reduce model complexity and prevent over-fitting which may result from simple linear regression . Ridge Regression : In ridge regression, the cost function is altered by adding a penalty equivalent to square of the magnitude of the coefficients. lasso (least absolute shrinkage and selection operator; also Lasso or LESSONS) is a regression analysis method that performs both variable selection and regularization in order to enhance the prediction accuracy and interpretability of the resulting statistical model.
Q4. Optimization?
Ans. Optimization is the problem of finding a set of inputs to an objective function that results in a maximum or minimum function evaluation. It is the challenging problem that underlies many machine learning algorithms, from fitting logistic regression models to training artificial neural networks.
Q5. What is standard error of the mean?
Ans. The standard error of mean tells you how accurate the mean of any given sample from that population is likely to be compared to the true population mean. When the standard error increases, i.e. the means are more spread out, it becomes more likely that any given mean is an inaccurate representation of the true population mean.
Happy learning with CloudyML ๐
Company Name - Bridgei2i Analytics
Role - Data Scientist
Q1. How would you remove bias?
Ans. Ways to minimize Bias in ML:
Choose the correct learning model. There are two types of learning models, and each has its own pros and cons.
Use the right training dataset.
Perform data processing mindfully.
Monitor real-world performance across the ML lifecycle.
Make sure that there are no infrastructural issues.
Q2. In what situation would you consider mean over median
Ans. If your data contains outliers, then you would typically rather use the median because otherwise the value of the mean would be dominated by the outliers rather than the typical values. In conclusion, if you are considering the mean, check your data for outliers, if any then better choose median.
Q3. LASSO & RIDGE?
Ans. Ridge and Lasso regression are some of the simple techniques to reduce model complexity and prevent over-fitting which may result from simple linear regression . Ridge Regression : In ridge regression, the cost function is altered by adding a penalty equivalent to square of the magnitude of the coefficients. lasso (least absolute shrinkage and selection operator; also Lasso or LESSONS) is a regression analysis method that performs both variable selection and regularization in order to enhance the prediction accuracy and interpretability of the resulting statistical model.
Q4. Optimization?
Ans. Optimization is the problem of finding a set of inputs to an objective function that results in a maximum or minimum function evaluation. It is the challenging problem that underlies many machine learning algorithms, from fitting logistic regression models to training artificial neural networks.
Q5. What is standard error of the mean?
Ans. The standard error of mean tells you how accurate the mean of any given sample from that population is likely to be compared to the true population mean. When the standard error increases, i.e. the means are more spread out, it becomes more likely that any given mean is an inaccurate representation of the true population mean.
Happy learning with CloudyML ๐
Which data manipulation command is used to combines the records from one or more tables? ๐จโ๐ป๐
Anonymous Quiz
13%
SELECT
8%
PROJECT
76%
JOIN
3%
PRODUCT
Today's Interview Quest N Ans
Company Name - Cognizant
Role - Data Scientist
Q1. Fix multi-collinearity in a regression model?
Ans. Follow these methods:
Remove some of the highly correlated independent variables.
Linearly combine the independent variables, such as adding them together.
Perform an analysis designed for highly correlated variables, such as principal components analysis or partial least squares regression.
Q2. Differentiate between gradient boosting and random forest.
Ans. The two main differences are: How trees are built: random forests builds each tree independently while gradient boosting builds one tree at a time. Combining results: random forests combine results at the end of the process (by averaging or "majority rules") while gradient boosting combines results along the way.
Q3. Can you write a user-defined function in SQL? how?
Ans. SQL Server allows us to create our functions called as user defined functions in SQL Server. For example, if we want to perform some complex calculations, then we can place them in a separate function, and store it in the database. Whenever we need the calculation, we can call it.
Q4. Hash tables? Where do you use it?
Ans. A hash table is a data structure that is used to store keys/value pairs. It uses a hash function to compute an index into an array in which an element will be inserted or searched. They are widely used in many kinds of computer software, particularly for associative arrays, database indexing, caches and sets. The idea of a hash table is to provide a direct access to its items. So that is why the it calculates the "hash code" of the key and uses it to store the item, instead of the key itself.
Q5. in a scenario, what would you prioritize: bias or variance?
Ans. Bias is an error between the actual values and the model's predicted values. Variance is also an error but from the model's sensitivity to the training data. A prioritization of Bias over Variance will lead to a model that overfits the data. Prioritizing Variance will have a model underfit the data.
Happy learning with CloudyMLML๐
Company Name - Cognizant
Role - Data Scientist
Q1. Fix multi-collinearity in a regression model?
Ans. Follow these methods:
Remove some of the highly correlated independent variables.
Linearly combine the independent variables, such as adding them together.
Perform an analysis designed for highly correlated variables, such as principal components analysis or partial least squares regression.
Q2. Differentiate between gradient boosting and random forest.
Ans. The two main differences are: How trees are built: random forests builds each tree independently while gradient boosting builds one tree at a time. Combining results: random forests combine results at the end of the process (by averaging or "majority rules") while gradient boosting combines results along the way.
Q3. Can you write a user-defined function in SQL? how?
Ans. SQL Server allows us to create our functions called as user defined functions in SQL Server. For example, if we want to perform some complex calculations, then we can place them in a separate function, and store it in the database. Whenever we need the calculation, we can call it.
Q4. Hash tables? Where do you use it?
Ans. A hash table is a data structure that is used to store keys/value pairs. It uses a hash function to compute an index into an array in which an element will be inserted or searched. They are widely used in many kinds of computer software, particularly for associative arrays, database indexing, caches and sets. The idea of a hash table is to provide a direct access to its items. So that is why the it calculates the "hash code" of the key and uses it to store the item, instead of the key itself.
Q5. in a scenario, what would you prioritize: bias or variance?
Ans. Bias is an error between the actual values and the model's predicted values. Variance is also an error but from the model's sensitivity to the training data. A prioritization of Bias over Variance will lead to a model that overfits the data. Prioritizing Variance will have a model underfit the data.
Happy learning with CloudyMLML๐
What kind of distance metric(s) are suitable for categorical variables to find the closest neighbors? ๐จโ๐ป๐ง
Anonymous Quiz
52%
Euclidean distance.
22%
Manhattan distance.
12%
Minkowski distance.
15%
Hamming distance.
Company Name - Intuit
Role - Data Scientist
Today's Interview Quest N Ans
Q1. Do gradient descent methods always converge to similar points?
Ans. Gradient Descent need not always converge at global minimum. It all depends on following conditions; If the line segment between any two points on the graph of the function lies above or on the graph then it is convex function.
Q2. For the given points, how will you calculate the euclidean distance in python?
Ans. Example:
import numpy as np
# initializing points in
# numpy arrays
point1 = np.array((1, 2, 3))
point2 = np.array((1, 1, 1))
# finding sum of squares
sum_sq = np.sum(np.square(point1 - point2))
# Doing squareroot and # printing Euclidean distance
print(np.sqrt(sum_sq))
Q3. What is a box-cox transformation?
Ans. A Box Cox transformation is a transformation of non-normal dependent variables into a normal shape.
Q4. What is collaborative filtering? And a content based?
Ans. Collaborative filtering filters information by using the interactions and data collected by the system from other users. It's based on the idea that people who agreed in their evaluation of certain items are likely to agree again in the future. Content-based filtering uses item features to recommend other items similar to what the user likes, based on their previous actions or explicit feedback.
Q5. What are entropy and information gain in decision tree algorithm?
Ans. Entropy can be defined as a measure of the purity of the sub split. Entropy always lies between 0 to 1. The information gain is based on the decrease in entropy after a dataset is split on an attribute. Constructing a decision tree is all about finding attribute that returns the highest information gain (i.e., the most homogeneous branches).
Q6. What is pooling on cnn, and how does it work?
Ans. Pooling layers are used to reduce the dimensions of the feature maps. Thus, it reduces the number of parameters to learn and the amount of computation performed in the network. The pooling layer summarises the features present in a region of the feature map generated by a convolution layer.
Q7. What are recurrent neural networks (rnns)?
Ans. Recurrent Neural Networks (RNN) are a class of Artificial Neural Networks that can process a sequence of inputs in deep learning and retain its state while processing the next sequence of inputs. Traditional neural networks will process an input and move onto the next one disregarding its sequence.
Q8. What is dropout and batch normalization?
Ans. Dropout is a technique where randomly selected neurons are ignored during training. Batch normalization is a technique for training very deep neural networks that standardizes the inputs to a layer for each mini-batch. This has the effect of stabilizing the learning process and dramatically reducing the number of training epochs required to train deep networks.
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๐.๐.- T๐ก๐ ๐๐ข๐ฐ๐๐ฅ๐ข ๐๐ข๐ฌ๐๐จ๐ฎ๐ง๐ญ ๐จ๐๐๐๐ซ ๐ก๐๐ฌ ๐๐๐ ๐ฎ๐ง. ๐๐๐ญ ๐ญ๐ก๐ โ๐๐ ๐๐จ๐ซ ๐๐๐โ ๐๐๐ญ๐ ๐๐๐ข๐๐ง๐๐ ๐๐จ๐ฎ๐ซ๐ฌ๐ ๐๐จ๐ซ ๐ฃ๐ฎ๐ฌ๐ญ โน999 ๐ข๐ง๐ฌ๐ญ๐๐๐ ๐จ๐ ๐ซ๐๐ ๐ฎ๐ฅ๐๐ซ ๐ฉ๐ซ๐ข๐๐ ๐จ๐ โน3499
๐Check Out The Link Below๐
Role - Data Scientist
Today's Interview Quest N Ans
Q1. Do gradient descent methods always converge to similar points?
Ans. Gradient Descent need not always converge at global minimum. It all depends on following conditions; If the line segment between any two points on the graph of the function lies above or on the graph then it is convex function.
Q2. For the given points, how will you calculate the euclidean distance in python?
Ans. Example:
import numpy as np
# initializing points in
# numpy arrays
point1 = np.array((1, 2, 3))
point2 = np.array((1, 1, 1))
# finding sum of squares
sum_sq = np.sum(np.square(point1 - point2))
# Doing squareroot and # printing Euclidean distance
print(np.sqrt(sum_sq))
Q3. What is a box-cox transformation?
Ans. A Box Cox transformation is a transformation of non-normal dependent variables into a normal shape.
Q4. What is collaborative filtering? And a content based?
Ans. Collaborative filtering filters information by using the interactions and data collected by the system from other users. It's based on the idea that people who agreed in their evaluation of certain items are likely to agree again in the future. Content-based filtering uses item features to recommend other items similar to what the user likes, based on their previous actions or explicit feedback.
Q5. What are entropy and information gain in decision tree algorithm?
Ans. Entropy can be defined as a measure of the purity of the sub split. Entropy always lies between 0 to 1. The information gain is based on the decrease in entropy after a dataset is split on an attribute. Constructing a decision tree is all about finding attribute that returns the highest information gain (i.e., the most homogeneous branches).
Q6. What is pooling on cnn, and how does it work?
Ans. Pooling layers are used to reduce the dimensions of the feature maps. Thus, it reduces the number of parameters to learn and the amount of computation performed in the network. The pooling layer summarises the features present in a region of the feature map generated by a convolution layer.
Q7. What are recurrent neural networks (rnns)?
Ans. Recurrent Neural Networks (RNN) are a class of Artificial Neural Networks that can process a sequence of inputs in deep learning and retain its state while processing the next sequence of inputs. Traditional neural networks will process an input and move onto the next one disregarding its sequence.
Q8. What is dropout and batch normalization?
Ans. Dropout is a technique where randomly selected neurons are ignored during training. Batch normalization is a technique for training very deep neural networks that standardizes the inputs to a layer for each mini-batch. This has the effect of stabilizing the learning process and dramatically reducing the number of training epochs required to train deep networks.
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๐.๐.- T๐ก๐ ๐๐ข๐ฐ๐๐ฅ๐ข ๐๐ข๐ฌ๐๐จ๐ฎ๐ง๐ญ ๐จ๐๐๐๐ซ ๐ก๐๐ฌ ๐๐๐ ๐ฎ๐ง. ๐๐๐ญ ๐ญ๐ก๐ โ๐๐ ๐๐จ๐ซ ๐๐๐โ ๐๐๐ญ๐ ๐๐๐ข๐๐ง๐๐ ๐๐จ๐ฎ๐ซ๐ฌ๐ ๐๐จ๐ซ ๐ฃ๐ฎ๐ฌ๐ญ โน999 ๐ข๐ง๐ฌ๐ญ๐๐๐ ๐จ๐ ๐ซ๐๐ ๐ฎ๐ฅ๐๐ซ ๐ฉ๐ซ๐ข๐๐ ๐จ๐ โน3499
๐Check Out The Link Below๐
โค1
Which of the following words cannot be a variable in python language?
Anonymous Quiz
12%
_val
42%
try
11%
_try
35%
None of above
Today's Interview Quest
Company Name - Mindtree
Role - Data Scientist
Q1. Central tendency
A. Central tendency is defined as โthe statistical measure that identifies a single value as representative of an entire distribution.โ It aims to provide an accurate description of the entire data. It is the single value that is most typical/representative of the collected data.
Q2. Central limit theorem.
A. The central limit theorem states that if you have a population with mean ฮผ and standard deviation ฯ and take sufficiently large random samples from the population with replacement , then the distribution of the sample means will be approximately normally distributed.
Q3. Chi-Square test
A. A chi-square test is a statistical test used to compare observed results with expected results. The purpose of this test is to determine if a difference between observed data and expected data is due to chance, or if it is due to a relationship between the variables you are studying.
Q4. A/B testing
A. A/B testing is an optimisation technique often used to understand how an altered variable affects audience or user engagement. It's a common method used in marketing, web design, product development, and user experience design to improve campaigns and goal conversion rates.
Q5. Outlier treatment method
A. Removing outliers is legitimate only for specific reasons. Outliers can be very informative about the subject-area and data collection process. If the outlier does not change the results but does affect assumptions, you may drop the outlier. Or just trim the data set, but replace outliers with the nearest โgoodโ data, as opposed to truncating them completely.
Q6. ANOVA test
A. Analysis of variance (ANOVA) is a statistical technique that is used to check if the means of two or more groups are significantly different from each other. ANOVA checks the impact of one or more factors by comparing the means of different samples.
Q7. Cross validation
A. Cross-validation is a resampling procedure used to evaluate machine learning models on a limited data sample. The procedure has a single parameter called k that refers to the number of groups that a given data sample is to be split into. As such, the procedure is often called k-fold cross-validation.
Q8. How will you work in a machine learning project if there is a huge imbalance in the data
A. Follow these techniques:
*Use the right evaluation metrics.
*Use K-fold Cross-Validation in the right way.
*Ensemble different resampled datasets.
*Resample with different ratios.
*Cluster the abundant class.
*Design your own models.
Q9. Formula of sigmoid function
A. It is a mathematical function having a characteristic that can take any real value and map it to between 0 to 1 shaped like the letter โSโ.
Y = 1 / 1+(e*-z)
Q10. Which metric is used to split a node in Decision Tree
A. The Gini Index and the Entropy and Information gain metrics are the metrics to use in the algorithm to create a decision tree
Q11. What is ensemble learning
A. Ensemble methods is a machine learning technique that combines several base models in order to produce one optimal predictive model.
Happy learning with CloudyML๐
Company Name - Mindtree
Role - Data Scientist
Q1. Central tendency
A. Central tendency is defined as โthe statistical measure that identifies a single value as representative of an entire distribution.โ It aims to provide an accurate description of the entire data. It is the single value that is most typical/representative of the collected data.
Q2. Central limit theorem.
A. The central limit theorem states that if you have a population with mean ฮผ and standard deviation ฯ and take sufficiently large random samples from the population with replacement , then the distribution of the sample means will be approximately normally distributed.
Q3. Chi-Square test
A. A chi-square test is a statistical test used to compare observed results with expected results. The purpose of this test is to determine if a difference between observed data and expected data is due to chance, or if it is due to a relationship between the variables you are studying.
Q4. A/B testing
A. A/B testing is an optimisation technique often used to understand how an altered variable affects audience or user engagement. It's a common method used in marketing, web design, product development, and user experience design to improve campaigns and goal conversion rates.
Q5. Outlier treatment method
A. Removing outliers is legitimate only for specific reasons. Outliers can be very informative about the subject-area and data collection process. If the outlier does not change the results but does affect assumptions, you may drop the outlier. Or just trim the data set, but replace outliers with the nearest โgoodโ data, as opposed to truncating them completely.
Q6. ANOVA test
A. Analysis of variance (ANOVA) is a statistical technique that is used to check if the means of two or more groups are significantly different from each other. ANOVA checks the impact of one or more factors by comparing the means of different samples.
Q7. Cross validation
A. Cross-validation is a resampling procedure used to evaluate machine learning models on a limited data sample. The procedure has a single parameter called k that refers to the number of groups that a given data sample is to be split into. As such, the procedure is often called k-fold cross-validation.
Q8. How will you work in a machine learning project if there is a huge imbalance in the data
A. Follow these techniques:
*Use the right evaluation metrics.
*Use K-fold Cross-Validation in the right way.
*Ensemble different resampled datasets.
*Resample with different ratios.
*Cluster the abundant class.
*Design your own models.
Q9. Formula of sigmoid function
A. It is a mathematical function having a characteristic that can take any real value and map it to between 0 to 1 shaped like the letter โSโ.
Y = 1 / 1+(e*-z)
Q10. Which metric is used to split a node in Decision Tree
A. The Gini Index and the Entropy and Information gain metrics are the metrics to use in the algorithm to create a decision tree
Q11. What is ensemble learning
A. Ensemble methods is a machine learning technique that combines several base models in order to produce one optimal predictive model.
Happy learning with CloudyML๐
Normal Distribution is symmetric is about
Anonymous Quiz
19%
Variance
46%
Mean
30%
Standard deviation
6%
Covariance
Happy Diwali To Everyone
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On This Special Day, we are offering our popular Assignment Driven Data Science Course at โน999/- with lifetime access instead of regular price โน3499/-
This Course helped more than 510 Learners to get their dream jobs.
You also can become a Job-Ready Data Scientist.
Now we are a community of 5,000+ Data Science enthusiasts on Telegram. Thank you everyone for making this possible.
We will Keep Posting More and More Data Science Related Updates for You.
โโ โโ โโ โโ โโ โโ โโ โโ โโ
On This Special Day, we are offering our popular Assignment Driven Data Science Course at โน999/- with lifetime access instead of regular price โน3499/-
This Course helped more than 510 Learners to get their dream jobs.
You also can become a Job-Ready Data Scientist.
Key Features of this course
1. We provide Assignment based Data Science course for complete hands-on practical learning experience. You will get 25+ Guided Assignments
2. We provide 1-1 Skype Course Support to resolve all the doubts everyday between 3PM to 11:59PM.
3. You will get 300+ data science interview questions and answers PDF collection
4. We have 10+ capstone end-to-end projects in our course that will enhance learners' resume
5. Anybody from any background can learn from our course. No prior coding Experience required.
6. Anyone who wanna switch their career into data science can join this amazing course
7. Trusted by 1800+ Learners in 6 months, It is the best affordable Data Science course.
8. It is a Self-Paced course with lifetime access
9. You will get course completion certificate
10. Additionally we provide job resources for reference like how to apply for jobs, resume samples, how to choose good ML projects, etc.
Start Your Data Science Journey Today
Course Link: https://go.cloudyml.com/ai4all-Data-Science-Course
๐Happy Learning With CloudyML๐
1. We provide Assignment based Data Science course for complete hands-on practical learning experience. You will get 25+ Guided Assignments
2. We provide 1-1 Skype Course Support to resolve all the doubts everyday between 3PM to 11:59PM.
3. You will get 300+ data science interview questions and answers PDF collection
4. We have 10+ capstone end-to-end projects in our course that will enhance learners' resume
5. Anybody from any background can learn from our course. No prior coding Experience required.
6. Anyone who wanna switch their career into data science can join this amazing course
7. Trusted by 1800+ Learners in 6 months, It is the best affordable Data Science course.
8. It is a Self-Paced course with lifetime access
9. You will get course completion certificate
10. Additionally we provide job resources for reference like how to apply for jobs, resume samples, how to choose good ML projects, etc.
Start Your Data Science Journey Today
Course Link: https://go.cloudyml.com/ai4all-Data-Science-Course
๐Happy Learning With CloudyML๐
๐๐จ๐ฆ๐ฉ๐๐ง๐ฒ ๐๐๐ฆ๐ - ๐๐ง๐๐จ๐ฌ๐ฒ๐ฌ
๐๐จ๐ฅ๐ - ๐๐๐ญ๐ ๐๐๐ข๐๐ง๐ญ๐ข๐ฌ๐ญ
๐๐จ๐๐๐ฒ'๐ฌ ๐๐ง๐ญ๐๐ซ๐ฏ๐ข๐๐ฐ ๐๐ฎ๐๐ฌ๐ญ ๐ ๐๐ง๐ฌ๐ฐ๐๐ซ
๐1. ๐๐จ๐ฐ ๐ญ๐จ ๐ข๐๐๐ง๐ญ๐ข๐๐ฒ ๐ ๐๐๐ฎ๐ฌ๐ ๐ฏ๐ฌ. ๐ ๐๐จ๐ซ๐ซ๐๐ฅ๐๐ญ๐ข๐จ๐ง? ๐๐ข๐ฏ๐ ๐๐ฑ๐๐ฆ๐ฉ๐ฅ๐๐ฌ.
Ans. While causation and correlation can exist at the same time, correlation does not imply causation. Causation explicitly applies to cases where action A causes outcome B. On the other hand, correlation is simply a relationship. Correlation between Ice cream sales and sunglasses sold. As the sales of ice creams is increasing so do the sales of sunglasses. Causation takes a step further than correlation.
๐2. ๐ฉ๐ซ๐๐๐ข๐ฌ๐ข๐จ๐ง, ๐๐๐๐ฎ๐ซ๐๐๐ฒ ๐๐ง๐ ๐ซ๐๐๐๐ฅ๐ฅ?
Ans. The recall is the ratio of the relevant results returned by the search engine to the total number of the relevant results that could have been returned. The precision is the proportion of relevant results in the list of all returned search results. Accuracy is the measurement used to determine which model is best at identifying relationships and patterns between variables in a dataset based on the input, or training, data.
๐3. ๐๐ก๐จ๐จ๐ฌ๐ ๐ค ๐ข๐ง ๐ค-๐ฆ๐๐๐ง๐ฌ?
Ans. There is a popular method known as elbow method which is used to determine the optimal value of K to perform the K-Means Clustering Algorithm. The basic idea behind this method is that it plots the various values of cost with changing k. As the value of K increases, there will be fewer elements in the cluster.
๐4. ๐ฐ๐จ๐ซ๐2๐ฏ๐๐ ๐ฆ๐๐ญ๐ก๐จ๐๐ฌ?
Ans. Word2vec is a technique for natural language processing published in 2013. The word2vec algorithm uses a neural network model to learn word associations from a large corpus of text. Once trained, such a model can detect synonymous words or suggest additional words for a partial sentence.
๐5. P๐ซ๐ฎ๐ง๐ข๐ง๐ ๐ข๐ง ๐๐๐ฌ๐ ๐จ๐ ๐๐๐๐ข๐ฌ๐ข๐จ๐ง ๐ญ๐ซ๐๐๐ฌ?
Ans. Pruning is a data compression technique in machine learning and search algorithms that reduces the size of decision trees by removing sections of the tree that are non-critical and redundant to classify instances.
Happy Learning With CloudyML๐
๐๐จ๐ฅ๐ - ๐๐๐ญ๐ ๐๐๐ข๐๐ง๐ญ๐ข๐ฌ๐ญ
๐๐จ๐๐๐ฒ'๐ฌ ๐๐ง๐ญ๐๐ซ๐ฏ๐ข๐๐ฐ ๐๐ฎ๐๐ฌ๐ญ ๐ ๐๐ง๐ฌ๐ฐ๐๐ซ
๐1. ๐๐จ๐ฐ ๐ญ๐จ ๐ข๐๐๐ง๐ญ๐ข๐๐ฒ ๐ ๐๐๐ฎ๐ฌ๐ ๐ฏ๐ฌ. ๐ ๐๐จ๐ซ๐ซ๐๐ฅ๐๐ญ๐ข๐จ๐ง? ๐๐ข๐ฏ๐ ๐๐ฑ๐๐ฆ๐ฉ๐ฅ๐๐ฌ.
Ans. While causation and correlation can exist at the same time, correlation does not imply causation. Causation explicitly applies to cases where action A causes outcome B. On the other hand, correlation is simply a relationship. Correlation between Ice cream sales and sunglasses sold. As the sales of ice creams is increasing so do the sales of sunglasses. Causation takes a step further than correlation.
๐2. ๐ฉ๐ซ๐๐๐ข๐ฌ๐ข๐จ๐ง, ๐๐๐๐ฎ๐ซ๐๐๐ฒ ๐๐ง๐ ๐ซ๐๐๐๐ฅ๐ฅ?
Ans. The recall is the ratio of the relevant results returned by the search engine to the total number of the relevant results that could have been returned. The precision is the proportion of relevant results in the list of all returned search results. Accuracy is the measurement used to determine which model is best at identifying relationships and patterns between variables in a dataset based on the input, or training, data.
๐3. ๐๐ก๐จ๐จ๐ฌ๐ ๐ค ๐ข๐ง ๐ค-๐ฆ๐๐๐ง๐ฌ?
Ans. There is a popular method known as elbow method which is used to determine the optimal value of K to perform the K-Means Clustering Algorithm. The basic idea behind this method is that it plots the various values of cost with changing k. As the value of K increases, there will be fewer elements in the cluster.
๐4. ๐ฐ๐จ๐ซ๐2๐ฏ๐๐ ๐ฆ๐๐ญ๐ก๐จ๐๐ฌ?
Ans. Word2vec is a technique for natural language processing published in 2013. The word2vec algorithm uses a neural network model to learn word associations from a large corpus of text. Once trained, such a model can detect synonymous words or suggest additional words for a partial sentence.
๐5. P๐ซ๐ฎ๐ง๐ข๐ง๐ ๐ข๐ง ๐๐๐ฌ๐ ๐จ๐ ๐๐๐๐ข๐ฌ๐ข๐จ๐ง ๐ญ๐ซ๐๐๐ฌ?
Ans. Pruning is a data compression technique in machine learning and search algorithms that reduces the size of decision trees by removing sections of the tree that are non-critical and redundant to classify instances.
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What is the maximum possible length of an identifier (variable name) in python ? ๐ง ๐ค
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79 characters