Hey guys,
CloudyML is going to conduct FREE Live Webinar on various Data Science topics every week and it is starting from today.
Today's Topic : Introduction of Python for Data Science, variables, Data type and Data structure.
Timing: 15/01/2022 07:00PM (IST)
After webinar, there will be Live QnA Session.
If you wanna attend today's Free Webinar, download our mobile App Now
App Link👉🏻https://ai.cloudyml.com/Download-App
Note: Free courses, Live webinar, Interview QnAs, Chat Features etc is available in the app.
CloudyML is going to conduct FREE Live Webinar on various Data Science topics every week and it is starting from today.
Today's Topic : Introduction of Python for Data Science, variables, Data type and Data structure.
Timing: 15/01/2022 07:00PM (IST)
After webinar, there will be Live QnA Session.
If you wanna attend today's Free Webinar, download our mobile App Now
App Link👉🏻https://ai.cloudyml.com/Download-App
Note: Free courses, Live webinar, Interview QnAs, Chat Features etc is available in the app.
Today's Interview QnAs
Company Name - Tredence
Role: Data Scientist
Q. Explain how the filter function works in python?
A. The filter() method filters a series using a function that checks if each element in the sequence is true or not. The filter() function takes two arguments: function - a function and iterable - an iterable like sets, lists, tuples etc.
Q. How to remove duplicate elements from a list?
A. First we have a List that contains duplicates. Create a dictionary, using the List items as keys. This will automatically remove any duplicates because dictionaries cannot have duplicate keys. Then, convert the dictionary back into a list.
Q. Difference between a shallow and a deep copy?
A. It's faster to do shallow repetitions. It does, however, handle pointers and references in a "lazy" manner. It just copies over the pointer price rather than producing a current copy of the specific knowledge the pointer links to. As a result, each of the initial and subsequent copies can have pointers that relate to the same underlying knowledge. Deep repetition clones the underlying data completely. It is not shared by the first and, as a result, by the copy.
Q. What is TF/IDF vectorization?
A. The TF-IDF statistic, which stands for term frequency–inverse document frequency, is a numerical measure of how essential a word is to a document in a collection or corpus. [1] It's frequently used in information retrieval, text mining, and user modelling searches as a weighting factor. The tf–idf value rises in proportion to the number of times a word appears in a document and is offset by the number of documents in the corpus that contain the term, which helps to compensate for the fact that some words appear more frequently than others.
Q. In what terms DBSCAN is better than K- Means Clustering?
A. Outliers and noisy datasets are easily handled by DBScan clustering, whereas outliers and noisy datasets are not adequately handled by K-means clustering. The number of clusters does not need to be stated in DBScan clustering. The number of clusters specified in K-means clustering is important.
P.S: Learn from Scratch and Switch to Data Science from any Non-Coding Background
Features
✅Hands on Guided assignments
✅Capstone End-to-end Projects
✅1-1 Skype Live Doubt Clearance
✅Frequently Asked Interview QnAs PDF collection
✅Course completion Certificate
Bonus
🟢Resume Review
🟢LinkedIn Profile Review
🟢Job Referrals after course completion
It is a Self-Paced course with lifetime access.
Enroll Now👉🏻https://ai.cloudyml.com/Data-Science-at-999
Happy Learning With CloudyML 💙
Company Name - Tredence
Role: Data Scientist
Q. Explain how the filter function works in python?
A. The filter() method filters a series using a function that checks if each element in the sequence is true or not. The filter() function takes two arguments: function - a function and iterable - an iterable like sets, lists, tuples etc.
Q. How to remove duplicate elements from a list?
A. First we have a List that contains duplicates. Create a dictionary, using the List items as keys. This will automatically remove any duplicates because dictionaries cannot have duplicate keys. Then, convert the dictionary back into a list.
Q. Difference between a shallow and a deep copy?
A. It's faster to do shallow repetitions. It does, however, handle pointers and references in a "lazy" manner. It just copies over the pointer price rather than producing a current copy of the specific knowledge the pointer links to. As a result, each of the initial and subsequent copies can have pointers that relate to the same underlying knowledge. Deep repetition clones the underlying data completely. It is not shared by the first and, as a result, by the copy.
Q. What is TF/IDF vectorization?
A. The TF-IDF statistic, which stands for term frequency–inverse document frequency, is a numerical measure of how essential a word is to a document in a collection or corpus. [1] It's frequently used in information retrieval, text mining, and user modelling searches as a weighting factor. The tf–idf value rises in proportion to the number of times a word appears in a document and is offset by the number of documents in the corpus that contain the term, which helps to compensate for the fact that some words appear more frequently than others.
Q. In what terms DBSCAN is better than K- Means Clustering?
A. Outliers and noisy datasets are easily handled by DBScan clustering, whereas outliers and noisy datasets are not adequately handled by K-means clustering. The number of clusters does not need to be stated in DBScan clustering. The number of clusters specified in K-means clustering is important.
P.S: Learn from Scratch and Switch to Data Science from any Non-Coding Background
Features
✅Hands on Guided assignments
✅Capstone End-to-end Projects
✅1-1 Skype Live Doubt Clearance
✅Frequently Asked Interview QnAs PDF collection
✅Course completion Certificate
Bonus
🟢Resume Review
🟢LinkedIn Profile Review
🟢Job Referrals after course completion
It is a Self-Paced course with lifetime access.
Enroll Now👉🏻https://ai.cloudyml.com/Data-Science-at-999
Happy Learning With CloudyML 💙
#Job #Openings
Role: Data Scientist
Company: Qdegrees Services pvt ltd
Exp- 0-2yr
Link to Apply- https://bit.ly/3qFYE1b
Role: ML & DS - Technical Content Engineer - Intern
Company: HackerEarth
Exp- 0-1yr
Link to Apply- https://bit.ly/3rgMgU4
Role: Data Scientist / Senior DS/ Team Lead
Company: NebulARC Technologies Private Limited
Exp- 0-4yr
Link to Apply- https://bit.ly/33EtrSE
Role: Staff Software Engineer - Machine Learning
Company: Twitter
exp- 0-4yr
Link to Apply- https://bit.ly/3qw69Y6
Role: Data Scientist
Company: Qdegrees Services pvt ltd
Exp- 0-2yr
Link to Apply- https://bit.ly/3qFYE1b
Role: ML & DS - Technical Content Engineer - Intern
Company: HackerEarth
Exp- 0-1yr
Link to Apply- https://bit.ly/3rgMgU4
Role: Data Scientist / Senior DS/ Team Lead
Company: NebulARC Technologies Private Limited
Exp- 0-4yr
Link to Apply- https://bit.ly/33EtrSE
Role: Staff Software Engineer - Machine Learning
Company: Twitter
exp- 0-4yr
Link to Apply- https://bit.ly/3qw69Y6
Today's Interview QnAs
Company Name - FORD
Role- Data Scientist
Q. What are Entropy and Information gain in Decision tree algorithm?
A. Entropy is a measure of impurity or uncertainty in a set of data used in information theory. It determines how data is split by a decision tree. The quantity of information improved in the nodes before splitting them for making subsequent judgments can be characterized as the information obtained in the decision tree.
Q. What Will Happen If the Learning Rate Is Set inaccurately (Too Low or Too High)?
A. A high learning rate in gradient descent will cause the learning to jump over global minima, whereas a low learning rate will cause the learning to take too long to converge or become stuck in an unwanted local minimum.
Q. What is meant by ‘curse of dimensionality’?
A. The problem produced by the exponential rise in volume associated with adding extra dimensions to Euclidean space is known as the "curse of dimensionality." The curse of dimensionality states that as the number of characteristics grows, the error grows as well. It refers to the fact that high-dimensional algorithms are more difficult to build and often have a running duration that is proportional to the dimensions. A higher number of dimensions theoretically allows for more information to be stored, but in practice, it rarely helps because real-world data contains more noise and redundancy.
Q. Difference between remove, del and pop?
A. remove function removes the first matching value/object. It does not do anything with the indexing. del function removes the item at a specific index. And pop removes the item at a specific index and returns it.
P.S: Learn from Scratch and Switch to Data Science from any Non-Coding Background
Features
✅Hands on Guided assignments
✅Capstone End-to-end Projects
✅1-1 Skype Live Doubt Clearance
✅Frequently Asked Interview QnAs PDF collection
✅Course completion Certificate
Bonus
🟢Resume Review
🟢LinkedIn Profile Review
🟢Job Referrals after course completion
It is a Self-Paced course with lifetime access.
Enroll Now👉🏻https://ai.cloudyml.com/Data-Science-at-999
Happy Learning With CloudyML 💙
Company Name - FORD
Role- Data Scientist
Q. What are Entropy and Information gain in Decision tree algorithm?
A. Entropy is a measure of impurity or uncertainty in a set of data used in information theory. It determines how data is split by a decision tree. The quantity of information improved in the nodes before splitting them for making subsequent judgments can be characterized as the information obtained in the decision tree.
Q. What Will Happen If the Learning Rate Is Set inaccurately (Too Low or Too High)?
A. A high learning rate in gradient descent will cause the learning to jump over global minima, whereas a low learning rate will cause the learning to take too long to converge or become stuck in an unwanted local minimum.
Q. What is meant by ‘curse of dimensionality’?
A. The problem produced by the exponential rise in volume associated with adding extra dimensions to Euclidean space is known as the "curse of dimensionality." The curse of dimensionality states that as the number of characteristics grows, the error grows as well. It refers to the fact that high-dimensional algorithms are more difficult to build and often have a running duration that is proportional to the dimensions. A higher number of dimensions theoretically allows for more information to be stored, but in practice, it rarely helps because real-world data contains more noise and redundancy.
Q. Difference between remove, del and pop?
A. remove function removes the first matching value/object. It does not do anything with the indexing. del function removes the item at a specific index. And pop removes the item at a specific index and returns it.
P.S: Learn from Scratch and Switch to Data Science from any Non-Coding Background
Features
✅Hands on Guided assignments
✅Capstone End-to-end Projects
✅1-1 Skype Live Doubt Clearance
✅Frequently Asked Interview QnAs PDF collection
✅Course completion Certificate
Bonus
🟢Resume Review
🟢LinkedIn Profile Review
🟢Job Referrals after course completion
It is a Self-Paced course with lifetime access.
Enroll Now👉🏻https://ai.cloudyml.com/Data-Science-at-999
Happy Learning With CloudyML 💙
Today's Interview QnAs
Company Name - HP
Role: Data Scientist
Q. Is skewness in data bad for the model? Why?
A. In a statistical distribution, skewed data is defined as a curve that seems deformed or skewed to the left or right. Many statistical models fail when there is too much skewness in the data. The tail portion of skewed data may act as an outlier for the statistical model, and we know that outliers have a negative impact on model performance, particularly regression-based models.
Q. How to train a model robust to outliers?
A. You can employ an outlier-resistant model. Outliers have little effect on tree-based models, but they do alter regression-based models. If you're doing a statistical test, instead of using a parametric test, use a non-parametric one. A robust error metric can be used: The influence of outliers is reduced by switching from mean squared error to mean absolute difference. Set a limit on how much data you can collect. Try a log transformation if your data has a strong right tail.
Q. Show me how lamda and map function works together in python
A. In Python, the map() function accepts two arguments: a function and a list. The function is called with a lambda function and a list, and it returns a new list with all of the lambda modified items returned by that function for each item.
Q. Combat Overfitting?
A. When a model performs well on training data but not on new data, it is said to be overfitted. To avoid overfitting, enhance training data and simplify the model. During the training phase, you should end sooner rather than later (have an eye over the loss over the training period as soon as loss begins to increase stop training). Ridge Regularization and Lasso Regularization are two types of regularisation. To combat overfitting in neural networks, use dropout.
P.S: Learn from Scratch and Switch to Data Science from any Non-Coding Background
Features
✅Hands on Guided assignments
✅Capstone End-to-end Projects
✅1-1 Skype Live Doubt Clearance
✅Frequently Asked Interview QnAs PDF collection
✅Course completion Certificate
Bonus
🟢Resume Review
🟢LinkedIn Profile Review
🟢Job Referrals after course completion
It is a Self-Paced course with lifetime access.
Enroll Now👉🏻https://ai.cloudyml.com/Data-Science-at-999
Happy Learning With CloudyML 💙
Company Name - HP
Role: Data Scientist
Q. Is skewness in data bad for the model? Why?
A. In a statistical distribution, skewed data is defined as a curve that seems deformed or skewed to the left or right. Many statistical models fail when there is too much skewness in the data. The tail portion of skewed data may act as an outlier for the statistical model, and we know that outliers have a negative impact on model performance, particularly regression-based models.
Q. How to train a model robust to outliers?
A. You can employ an outlier-resistant model. Outliers have little effect on tree-based models, but they do alter regression-based models. If you're doing a statistical test, instead of using a parametric test, use a non-parametric one. A robust error metric can be used: The influence of outliers is reduced by switching from mean squared error to mean absolute difference. Set a limit on how much data you can collect. Try a log transformation if your data has a strong right tail.
Q. Show me how lamda and map function works together in python
A. In Python, the map() function accepts two arguments: a function and a list. The function is called with a lambda function and a list, and it returns a new list with all of the lambda modified items returned by that function for each item.
Q. Combat Overfitting?
A. When a model performs well on training data but not on new data, it is said to be overfitted. To avoid overfitting, enhance training data and simplify the model. During the training phase, you should end sooner rather than later (have an eye over the loss over the training period as soon as loss begins to increase stop training). Ridge Regularization and Lasso Regularization are two types of regularisation. To combat overfitting in neural networks, use dropout.
P.S: Learn from Scratch and Switch to Data Science from any Non-Coding Background
Features
✅Hands on Guided assignments
✅Capstone End-to-end Projects
✅1-1 Skype Live Doubt Clearance
✅Frequently Asked Interview QnAs PDF collection
✅Course completion Certificate
Bonus
🟢Resume Review
🟢LinkedIn Profile Review
🟢Job Referrals after course completion
It is a Self-Paced course with lifetime access.
Enroll Now👉🏻https://ai.cloudyml.com/Data-Science-at-999
Happy Learning With CloudyML 💙
Today's Quiz
What is file extension of jupyter notebook?
What is file extension of jupyter notebook?
Anonymous Quiz
22%
.py
3%
.css
66%
. ipynb
9%
.jv
Media is too big
VIEW IN TELEGRAM
Learn from scratch and switch to Data science from any non-coding background with our Assignments based Data Science course.
Features
✅ Hands on Guided assignments
✅ Capstone End-to-end Projects
✅ 1-1 Skype Live Doubt Clearance
✅ Frequently Asked Interview QnAs PDF collection
✅ Course completion Certificate
✅ Resume and LinkedIn Review
✅ Job Referrals after course completion
✅ Job Opening Mails, Quiz, Newsletters and more
✅ Self-Paced course with Lifetime Access
Get everything at best affordable price
Enroll Now 👉🏻https://ai.cloudyml.com/Data-Science-at-999
Features
✅ Hands on Guided assignments
✅ Capstone End-to-end Projects
✅ 1-1 Skype Live Doubt Clearance
✅ Frequently Asked Interview QnAs PDF collection
✅ Course completion Certificate
✅ Resume and LinkedIn Review
✅ Job Referrals after course completion
✅ Job Opening Mails, Quiz, Newsletters and more
✅ Self-Paced course with Lifetime Access
Get everything at best affordable price
Enroll Now 👉🏻https://ai.cloudyml.com/Data-Science-at-999
Today's Interview QnAs
Company Name - Course i5
Role: Data Scientist
Q. How can outlier values be treated?
A. An outlier is an observation in a dataset that differs significantly from the rest of the data. This signifies that an outlier is much larger or smaller than the rest of the data.
Given are some of the methods of treating the outliers: Trimming or removing the outlier, Quantile based flooring and capping, Mean/Median imputation.
Q. What is root cause analysis?
A. A root cause is a component that contributed to a nonconformance and should be eradicated permanently through process improvement. The root cause is the most fundamental problem—the most fundamental reason—that puts in motion the entire cause-and-effect chain that leads to the problem (s). Root cause analysis (RCA) is a word that refers to a variety of approaches, tools, and procedures used to identify the root causes of problems. Some RCA approaches are more directed toward uncovering actual root causes than others, while others are more general problem-solving procedures, and yet others just provide support for the root cause analysis core activity.
Q. What is bias and variance in Data Science?
A. The model's simplifying assumptions simplify the target function, making it easier to estimate. Bias is the difference between the Predicted Value and the Expected Value in its most basic form. Variance refers to how much the target function's estimate will fluctuate as a result of varied training data. In contrast to bias, variance occurs when the model takes into account the data's fluctuations, or noise.
Q. What is a confusion matrix?
A. A confusion matrix is a method of summarising a classification algorithm's performance. Calculating a confusion matrix can help you understand what your classification model is getting right and where it is going wrong. This gives us the following: "True positive" for event values that were successfully predicted. "False positive" for event values that were mistakenly predicted. For successfully anticipated no-event values, "true negative" is used. "False negative" for no-event values that were mistakenly predicted.
P.S: Learn from scratch and switch to Data science from any non-coding background with our Assignments based Data Science course.
Features
✅ Hands on Guided assignments
✅ Capstone End-to-end Projects
✅ 1-1 Skype Live Doubt Clearance
✅ Frequently Asked Interview QnAs PDF collection
✅ Course completion Certificate
✅ Resume and LinkedIn Review
✅ Job Referrals after course completion
✅ Job Opening Mails, Quiz, Newsletters and more
✅ Self-Paced course with Lifetime Access
Get everything at best affordable price
Enroll Now 👉🏻https://ai.cloudyml.com/Data-Science-at-999
Happy Learning With CloudyML 💙
Company Name - Course i5
Role: Data Scientist
Q. How can outlier values be treated?
A. An outlier is an observation in a dataset that differs significantly from the rest of the data. This signifies that an outlier is much larger or smaller than the rest of the data.
Given are some of the methods of treating the outliers: Trimming or removing the outlier, Quantile based flooring and capping, Mean/Median imputation.
Q. What is root cause analysis?
A. A root cause is a component that contributed to a nonconformance and should be eradicated permanently through process improvement. The root cause is the most fundamental problem—the most fundamental reason—that puts in motion the entire cause-and-effect chain that leads to the problem (s). Root cause analysis (RCA) is a word that refers to a variety of approaches, tools, and procedures used to identify the root causes of problems. Some RCA approaches are more directed toward uncovering actual root causes than others, while others are more general problem-solving procedures, and yet others just provide support for the root cause analysis core activity.
Q. What is bias and variance in Data Science?
A. The model's simplifying assumptions simplify the target function, making it easier to estimate. Bias is the difference between the Predicted Value and the Expected Value in its most basic form. Variance refers to how much the target function's estimate will fluctuate as a result of varied training data. In contrast to bias, variance occurs when the model takes into account the data's fluctuations, or noise.
Q. What is a confusion matrix?
A. A confusion matrix is a method of summarising a classification algorithm's performance. Calculating a confusion matrix can help you understand what your classification model is getting right and where it is going wrong. This gives us the following: "True positive" for event values that were successfully predicted. "False positive" for event values that were mistakenly predicted. For successfully anticipated no-event values, "true negative" is used. "False negative" for no-event values that were mistakenly predicted.
P.S: Learn from scratch and switch to Data science from any non-coding background with our Assignments based Data Science course.
Features
✅ Hands on Guided assignments
✅ Capstone End-to-end Projects
✅ 1-1 Skype Live Doubt Clearance
✅ Frequently Asked Interview QnAs PDF collection
✅ Course completion Certificate
✅ Resume and LinkedIn Review
✅ Job Referrals after course completion
✅ Job Opening Mails, Quiz, Newsletters and more
✅ Self-Paced course with Lifetime Access
Get everything at best affordable price
Enroll Now 👉🏻https://ai.cloudyml.com/Data-Science-at-999
Happy Learning With CloudyML 💙
Day 21 | What's the difference between list and array in python ? | Data Science Interview QnA
👉🏻https://youtu.be/dVXwSwldsgE
👉🏻https://youtu.be/dVXwSwldsgE
YouTube
Day 21 | What's the difference between list and array in python ? | Data Science Interview QnA
Understand the difference between list and python.
If this video helped you, please Like, Share and Subscribe to my channel.
If you wanna learn Python and Data Science from Scratch, join our Assignment Based Data Science course for complete hands-on practical…
If this video helped you, please Like, Share and Subscribe to my channel.
If you wanna learn Python and Data Science from Scratch, join our Assignment Based Data Science course for complete hands-on practical…
Today's Interview QnAs
Company Name - Genpact
Role: Data Scientist
Q. How will you measure the Euclidean distance between the two arrays in NumPy?
A. The magnitude or length of the line segment between two locations is known as the Euclidean distance in mathematics. We first create two numpy arrays in the first procedure. The Euclidean distance is then computed directly using numpy's linalg.norm() function.
Q. Difference between Normalisation and Standardization?
A. Normalization is the process of rescaling values into a range of [0,1]. Typically, standardisation entails rescaling data to a mean of 0 and a standard deviation of 1. (unit variance).
Q. What is Ensemble Learning?
A. Ensemble learning is the process of systematically generating and combining many models, such as classifiers or experts, to tackle a specific computational intelligence problem. Ensemble learning is largely used to improve a model's performance (classification, prediction, function approximation, etc.) or to lessen the risk of an unintentional poor model selection.
Q. Difference between Batch and Stochastic Gradient Descent?
A. Batch Gradient Descent is highly slow on very big training sets since it entails calculations over the entire training set at each step. As a result, Batch GD becomes extremely computationally expensive. SGD is stochastic in nature, which means it chooses up a "random" instance of training data at each step and then computes the gradient, which is significantly faster than Batch GD because there is much less data to modify at once.
P.S: Learn from scratch and switch to Data science from any non-coding background with our Assignments based Data Science course.
Features
✅ Hands on Guided assignments
✅ Capstone End-to-end Projects
✅ 1-1 Skype Live Doubt Clearance
✅ Frequently Asked Interview QnAs PDF collection
✅ Course completion Certificate
✅ Resume and LinkedIn Review
✅ Job Referrals after course completion
✅ Job Opening Mails, Quiz, Newsletters and more
✅ Self-Paced course with Lifetime Access
Get everything at best affordable price
Enroll Now 👉🏻https://ai.cloudyml.com/Data-Science-at-999
Happy Learning With CloudyML 💙
Company Name - Genpact
Role: Data Scientist
Q. How will you measure the Euclidean distance between the two arrays in NumPy?
A. The magnitude or length of the line segment between two locations is known as the Euclidean distance in mathematics. We first create two numpy arrays in the first procedure. The Euclidean distance is then computed directly using numpy's linalg.norm() function.
Q. Difference between Normalisation and Standardization?
A. Normalization is the process of rescaling values into a range of [0,1]. Typically, standardisation entails rescaling data to a mean of 0 and a standard deviation of 1. (unit variance).
Q. What is Ensemble Learning?
A. Ensemble learning is the process of systematically generating and combining many models, such as classifiers or experts, to tackle a specific computational intelligence problem. Ensemble learning is largely used to improve a model's performance (classification, prediction, function approximation, etc.) or to lessen the risk of an unintentional poor model selection.
Q. Difference between Batch and Stochastic Gradient Descent?
A. Batch Gradient Descent is highly slow on very big training sets since it entails calculations over the entire training set at each step. As a result, Batch GD becomes extremely computationally expensive. SGD is stochastic in nature, which means it chooses up a "random" instance of training data at each step and then computes the gradient, which is significantly faster than Batch GD because there is much less data to modify at once.
P.S: Learn from scratch and switch to Data science from any non-coding background with our Assignments based Data Science course.
Features
✅ Hands on Guided assignments
✅ Capstone End-to-end Projects
✅ 1-1 Skype Live Doubt Clearance
✅ Frequently Asked Interview QnAs PDF collection
✅ Course completion Certificate
✅ Resume and LinkedIn Review
✅ Job Referrals after course completion
✅ Job Opening Mails, Quiz, Newsletters and more
✅ Self-Paced course with Lifetime Access
Get everything at best affordable price
Enroll Now 👉🏻https://ai.cloudyml.com/Data-Science-at-999
Happy Learning With CloudyML 💙
❤1
We provide Assignment Driven Data Science course for complete hands on practical learning experience with 1-1 Live Doubt Clearance Support everyday.
What are you getting in this course?
✅Pre-recorded tutorial videos on Python, ML, SQL, Statistics, Model Deployment and AWS Sagemaker.
✅25+Guided Assignments for Hands-on Practical Learning
✅We have 10+ capstone projects to enhance your resume
✅We provide 1-1 Skype Course Support to resolve all the doubts between 3PM to 11:59PM, everyday including weekends
✅You will get 300+ data science interview questions and answers PDF collection
✅Job Referrals after course completion
✅ Resume and LinkedIn profile review feature
✅It is a Self-Paced course with lifetime access, you can learn according to your time availability
✅Job Opening Mails
✅ Company wise Interview QnAs Newsletter via mail everyday
✅Course Completion Certificate
Chosen and Trusted by 3000+ Learning
Enroll Now👉🏻https://www.cloudyml.com/cloudyml-data-science-course/
Offer is valid till 26 Jan, 11:59PM
What are you getting in this course?
✅Pre-recorded tutorial videos on Python, ML, SQL, Statistics, Model Deployment and AWS Sagemaker.
✅25+Guided Assignments for Hands-on Practical Learning
✅We have 10+ capstone projects to enhance your resume
✅We provide 1-1 Skype Course Support to resolve all the doubts between 3PM to 11:59PM, everyday including weekends
✅You will get 300+ data science interview questions and answers PDF collection
✅Job Referrals after course completion
✅ Resume and LinkedIn profile review feature
✅It is a Self-Paced course with lifetime access, you can learn according to your time availability
✅Job Opening Mails
✅ Company wise Interview QnAs Newsletter via mail everyday
✅Course Completion Certificate
Chosen and Trusted by 3000+ Learning
Enroll Now👉🏻https://www.cloudyml.com/cloudyml-data-science-course/
Offer is valid till 26 Jan, 11:59PM
🔴Job Opening Updates
Role - Data Scientist
Company- Snaphunt
Exp - 0-2yr
Link - https://ai.cloudyml.com/3tXYPH8
Role - Data Scientist
Company- Wunderman Thompson
Exp - 0-2yr
Link - https://ai.cloudyml.com/3FYt7vH
Role - Data Scientist (Risk Analytics - Derivatives) -JP Morgan - Mumbai
Company - Jpmorgan Chase
Exp - 0-4 yr
Link - https://ai.cloudyml.com/3FXlc1K
Role - Business Analyst
Company- Swiggy
Exp - 0-3yr
Link - https://ai.cloudyml.com/3AssDN2
Role - ML Engineer
Company - Talent Bee Consulting Pvt. Ltd
Exp - 1-2yr
Link - https://ai.cloudyml.com/3gbH3Ip
Role - Data Scientist
Company- Snaphunt
Exp - 0-2yr
Link - https://ai.cloudyml.com/3tXYPH8
Role - Data Scientist
Company- Wunderman Thompson
Exp - 0-2yr
Link - https://ai.cloudyml.com/3FYt7vH
Role - Data Scientist (Risk Analytics - Derivatives) -JP Morgan - Mumbai
Company - Jpmorgan Chase
Exp - 0-4 yr
Link - https://ai.cloudyml.com/3FXlc1K
Role - Business Analyst
Company- Swiggy
Exp - 0-3yr
Link - https://ai.cloudyml.com/3AssDN2
Role - ML Engineer
Company - Talent Bee Consulting Pvt. Ltd
Exp - 1-2yr
Link - https://ai.cloudyml.com/3gbH3Ip
Not just Hands-on Practical Learning Experience, a Complete Roadmap to become an Interview Ready Data Scientist.
Visit Course Now 👉🏻https://www.cloudyml.com/cloudyml-data-science-course
Visit Course Now 👉🏻https://www.cloudyml.com/cloudyml-data-science-course
In this video, i have shared my experience of job searching last year when I was switching my company, I hope it will help you.
10 More to go for 5K subscribers on my YouTube channel.
Watch Now👉🏻 https://youtu.be/nSzWRKvlmbY
10 More to go for 5K subscribers on my YouTube channel.
Watch Now👉🏻 https://youtu.be/nSzWRKvlmbY
YouTube
How do you manage to get so many interview calls for Data Scientist role?
This question I am asked to so many people in LinkedIn, so thought of making a video for the same.
Link to the post: https://www.linkedin.com/posts/akashraj001_how-do-you-manage-to-get-so-many-interview-activity-6743017430391549952-Co6G
Join our "AI for…
Link to the post: https://www.linkedin.com/posts/akashraj001_how-do-you-manage-to-get-so-many-interview-activity-6743017430391549952-Co6G
Join our "AI for…
Today's Interview QnAs
Company Name - Accenture
Role: Data Scientist
Q. Why do we use Cross validation?
A. Cross-validation is a technique used in applied machine learning to estimate a machine learning model's skill on unknown data. That is, to use a small sample to assess how the model will perform in general when used to generate predictions on data that was not utilised during the model's training.
Q. How will you work in a machine learning project if there is a huge imbalance in the data?
A. Machine Learning algorithms tend to produce unsatisfactory classifiers when faced with imbalanced datasets. To overcome this, methods like randome oversampling and random undersampling can be used. Synthetic Minority Over-sampling Technique, Modified synthetic minority oversampling technique (MSMOTE), Algorithmic Ensemble Techniques can also be used.
Q. Formula of sigmoid function?
A. A sigmoid function is a mathematical function having a characteristic "S"-shaped curve or sigmoid curve. Sigmoid functions most often show a return value (y axis) in the range 0 to 1. A common example of a sigmoid function is the logistic function shown in the first figure and defined by the formula
S(x) = 1/(1+exp(-x))
Q. Can we use sigmoid function in case of multiple classification?
A. You must use Softmax if your task is a type of classification where the labels are mutually exclusive and each input only has one label. If you have numerous labels for each input in your classification work, your classes are not mutually exclusive, and you can utilise Sigmoid for each output. In the first scenario, the output should be the output entry with the highest value. In the latter scenario, you have an activation value for each class that belongs to the last sigmoid. You can assert that an entry exists in the input if each activation is greater than 0.5.
..................
P.S: If you wanna crack interviews of Data Science and become Job Ready, join our" Assignment based Data Science course" today!
Features
✅ Hands on Guided assignments
✅ Capstone End-to-end Projects
✅ 1-1 Skype Live Doubt Clearance
✅ Frequently Asked Interview QnAs PDF collection
✅ Course completion Certificate
✅ Resume and LinkedIn Review
✅ Job Referrals after course completion
✅ Job Opening Mails, Quiz, Newsletters and more
✅ 1 Month Internship Opportunity
✅ Self-Paced course with Lifetime Access
Get everything at best affordable price
Enroll Now 👉🏻https://ai.cloudyml.com/Data-Science-at-999
Happy Learning With CloudyML 💙
Company Name - Accenture
Role: Data Scientist
Q. Why do we use Cross validation?
A. Cross-validation is a technique used in applied machine learning to estimate a machine learning model's skill on unknown data. That is, to use a small sample to assess how the model will perform in general when used to generate predictions on data that was not utilised during the model's training.
Q. How will you work in a machine learning project if there is a huge imbalance in the data?
A. Machine Learning algorithms tend to produce unsatisfactory classifiers when faced with imbalanced datasets. To overcome this, methods like randome oversampling and random undersampling can be used. Synthetic Minority Over-sampling Technique, Modified synthetic minority oversampling technique (MSMOTE), Algorithmic Ensemble Techniques can also be used.
Q. Formula of sigmoid function?
A. A sigmoid function is a mathematical function having a characteristic "S"-shaped curve or sigmoid curve. Sigmoid functions most often show a return value (y axis) in the range 0 to 1. A common example of a sigmoid function is the logistic function shown in the first figure and defined by the formula
S(x) = 1/(1+exp(-x))
Q. Can we use sigmoid function in case of multiple classification?
A. You must use Softmax if your task is a type of classification where the labels are mutually exclusive and each input only has one label. If you have numerous labels for each input in your classification work, your classes are not mutually exclusive, and you can utilise Sigmoid for each output. In the first scenario, the output should be the output entry with the highest value. In the latter scenario, you have an activation value for each class that belongs to the last sigmoid. You can assert that an entry exists in the input if each activation is greater than 0.5.
..................
P.S: If you wanna crack interviews of Data Science and become Job Ready, join our" Assignment based Data Science course" today!
Features
✅ Hands on Guided assignments
✅ Capstone End-to-end Projects
✅ 1-1 Skype Live Doubt Clearance
✅ Frequently Asked Interview QnAs PDF collection
✅ Course completion Certificate
✅ Resume and LinkedIn Review
✅ Job Referrals after course completion
✅ Job Opening Mails, Quiz, Newsletters and more
✅ 1 Month Internship Opportunity
✅ Self-Paced course with Lifetime Access
Get everything at best affordable price
Enroll Now 👉🏻https://ai.cloudyml.com/Data-Science-at-999
Happy Learning With CloudyML 💙
Today's Interview QnAs
Company Name - Mindtree
Role: Data Scientist
Q. What is central tendency ?
A. The central tendency is a descriptive description of a dataset represented by a single value that represents the data distribution's center. It does not provide information on individual values in the dataset, but it does provide a thorough overview of the entire dataset. The following measurements can be used to describe the central tendency of a dataset in general: Mean: Represents the sum of all values in a dataset divided by total number of values in the dataset. The median is the midpoint value in an ascending-ordered dataset. The most often occurring value in a dataset is defined by the mode.
Q. Which central tendency method is used If there exists any outliers ?
A. Because the value of the mean might be skewed by outliers, the median is frequently recommended in these instances. It will, however, depend on how powerful the outliers are. Using the mean as the measure of central tendency is usually preferred if they do not significantly alter the mean.
Q. Explain Central limit theorem ?
A. The central limit theorem (CLT) asserts that as the sample size grows higher, the distribution of a sample variable approaches a normal distribution (i.e., a "bell curve"), assuming that all samples are identical in size and independent of the population's actual distribution shape.
Q. What is Chi-Square test ?
A. A hypothesis testing method is the Chi-square test. Checking if observed frequencies in one or more categories match expected frequencies is one of two frequent Chi-square tests. The Chi-square test is used to determine whether your data is as expected. The test's core premise is to compare the observed values in your data to the expected values that you would see if the null hypothesis is true. The Chi-square goodness of fit test and the Chi-square test of independence are two regularly used Chi-square tests. Both tests use variables to categorize your data into groups.
..................
P.S: If you wanna crack interviews of Data Science and become Job Ready, join our" Assignment based Data Science course" today!
Features
✅ Hands on Guided assignments
✅ Capstone End-to-end Projects
✅ 1-1 Skype Live Doubt Clearance
✅ Frequently Asked Interview QnAs PDF collection
✅ Course completion Certificate
✅ Resume and LinkedIn Review
✅ Job Referrals after course completion
✅ Job Opening Mails, Quiz, Newsletters and more
✅ 1 Month Internship Opportunity
✅ Self-Paced course with Lifetime Access
Get everything at best affordable price
Enroll Now 👉🏻https://ai.cloudyml.com/Data-Science-at-999
Happy Learning With CloudyML 💙
Company Name - Mindtree
Role: Data Scientist
Q. What is central tendency ?
A. The central tendency is a descriptive description of a dataset represented by a single value that represents the data distribution's center. It does not provide information on individual values in the dataset, but it does provide a thorough overview of the entire dataset. The following measurements can be used to describe the central tendency of a dataset in general: Mean: Represents the sum of all values in a dataset divided by total number of values in the dataset. The median is the midpoint value in an ascending-ordered dataset. The most often occurring value in a dataset is defined by the mode.
Q. Which central tendency method is used If there exists any outliers ?
A. Because the value of the mean might be skewed by outliers, the median is frequently recommended in these instances. It will, however, depend on how powerful the outliers are. Using the mean as the measure of central tendency is usually preferred if they do not significantly alter the mean.
Q. Explain Central limit theorem ?
A. The central limit theorem (CLT) asserts that as the sample size grows higher, the distribution of a sample variable approaches a normal distribution (i.e., a "bell curve"), assuming that all samples are identical in size and independent of the population's actual distribution shape.
Q. What is Chi-Square test ?
A. A hypothesis testing method is the Chi-square test. Checking if observed frequencies in one or more categories match expected frequencies is one of two frequent Chi-square tests. The Chi-square test is used to determine whether your data is as expected. The test's core premise is to compare the observed values in your data to the expected values that you would see if the null hypothesis is true. The Chi-square goodness of fit test and the Chi-square test of independence are two regularly used Chi-square tests. Both tests use variables to categorize your data into groups.
..................
P.S: If you wanna crack interviews of Data Science and become Job Ready, join our" Assignment based Data Science course" today!
Features
✅ Hands on Guided assignments
✅ Capstone End-to-end Projects
✅ 1-1 Skype Live Doubt Clearance
✅ Frequently Asked Interview QnAs PDF collection
✅ Course completion Certificate
✅ Resume and LinkedIn Review
✅ Job Referrals after course completion
✅ Job Opening Mails, Quiz, Newsletters and more
✅ 1 Month Internship Opportunity
✅ Self-Paced course with Lifetime Access
Get everything at best affordable price
Enroll Now 👉🏻https://ai.cloudyml.com/Data-Science-at-999
Happy Learning With CloudyML 💙