Date: 23rd August, 2022
Company - Wipro
Role- Data Analyst
Topic: DBMS
1. What are different types of Collation Sensitivity?
Following are the different types of Collation Sensitivity:
- Case sensitive: A and a, B and b
- Kana sensitive: Japanese Kana characters
- Width sensitive: single byte characters and double-byte characters.
- Accent Sensitive.
2. What is OLTP ?
OLTP or Online Transaction Processing is a type of data processing that consists of executing a number of transactions occurring concurrently—online banking, shopping, order entry, or sending text messages, for example. These transactions traditionally are referred to as economic or financial transactions, recorded and secured so that an enterprise can access the information anytime for accounting or reporting purposes.
3. What is OLAP?
OLAP stands for On-Line Analytical Processing. OLAP is a classification of software technology which authorizes analysts, managers, and executives to gain insight into information through fast, consistent, interactive access in a wide variety of possible views of data that has been transformed from raw information to reflect the real dimensionality of the enterprise as understood by the clients.
OLAP implement the multidimensional analysis of business information and support the capability for complex estimations, trend analysis, and sophisticated data modeling.
4. How OLAP Works?
Fundamentally, OLAP has a very simple concept. It pre-calculates most of the queries that are typically very hard to execute over tabular databases, namely aggregation, joining, and grouping. These queries are calculated during a process that is usually called 'building' or 'processing' of the OLAP cube. This process happens overnight, and by the time end users get to work - data will have been updated.
Company - Wipro
Role- Data Analyst
Topic: DBMS
1. What are different types of Collation Sensitivity?
Following are the different types of Collation Sensitivity:
- Case sensitive: A and a, B and b
- Kana sensitive: Japanese Kana characters
- Width sensitive: single byte characters and double-byte characters.
- Accent Sensitive.
2. What is OLTP ?
OLTP or Online Transaction Processing is a type of data processing that consists of executing a number of transactions occurring concurrently—online banking, shopping, order entry, or sending text messages, for example. These transactions traditionally are referred to as economic or financial transactions, recorded and secured so that an enterprise can access the information anytime for accounting or reporting purposes.
3. What is OLAP?
OLAP stands for On-Line Analytical Processing. OLAP is a classification of software technology which authorizes analysts, managers, and executives to gain insight into information through fast, consistent, interactive access in a wide variety of possible views of data that has been transformed from raw information to reflect the real dimensionality of the enterprise as understood by the clients.
OLAP implement the multidimensional analysis of business information and support the capability for complex estimations, trend analysis, and sophisticated data modeling.
4. How OLAP Works?
Fundamentally, OLAP has a very simple concept. It pre-calculates most of the queries that are typically very hard to execute over tabular databases, namely aggregation, joining, and grouping. These queries are calculated during a process that is usually called 'building' or 'processing' of the OLAP cube. This process happens overnight, and by the time end users get to work - data will have been updated.
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Date: 24th August, 2022
Company Name: IBM
Role: Machine Learning Engineer
Topic: Machine Learning
1. When does regularization become necessary in Machine Learning?
Regularization is necessary whenever the model begins to overfit/ underfit. It is a cost term for bringing in more features with the objective function. Hence, it tries to push the coefficients for many variables to zero and reduce cost term. It helps to reduce model complexity so that the model can become better at predicting (generalizing).
2. What do you understand by the F1 score?
The F1 score represents the measurement of a model's performance. It is referred to as a weighted average of the precision and recall of a model. The results tending to 1 are considered as the best, and those tending to 0 are the worst. It could be used in classification tests, where true negatives don't matter much.
3. What according to you, is more important between model accuracy and model performance?
Model accuracy is only a subset of model performance, and sometimes a misleading one. Let’s say you wanted to detect fraud in a massive dataset with a sample of millions, a more accurate model would most likely predict no fraud at all if only a minority of cases were fraud. However, this would be useless for a predictive model—a model designed to find fraud that asserted there was no fraud at all! Questions like this help you demonstrate that model accuracy isn’t always great way to assess performance of the model. So Model performance is more important.
4. What are the necessary steps involved in Machine Learning Project?
There are several essential steps we must follow to achieve a good working model while doing a Machine Learning Project. Those steps may include parameter tuning, data preparation, data collection, training the model, model evaluation, and prediction, etc.
————————————————————-
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Company Name: IBM
Role: Machine Learning Engineer
Topic: Machine Learning
1. When does regularization become necessary in Machine Learning?
Regularization is necessary whenever the model begins to overfit/ underfit. It is a cost term for bringing in more features with the objective function. Hence, it tries to push the coefficients for many variables to zero and reduce cost term. It helps to reduce model complexity so that the model can become better at predicting (generalizing).
2. What do you understand by the F1 score?
The F1 score represents the measurement of a model's performance. It is referred to as a weighted average of the precision and recall of a model. The results tending to 1 are considered as the best, and those tending to 0 are the worst. It could be used in classification tests, where true negatives don't matter much.
3. What according to you, is more important between model accuracy and model performance?
Model accuracy is only a subset of model performance, and sometimes a misleading one. Let’s say you wanted to detect fraud in a massive dataset with a sample of millions, a more accurate model would most likely predict no fraud at all if only a minority of cases were fraud. However, this would be useless for a predictive model—a model designed to find fraud that asserted there was no fraud at all! Questions like this help you demonstrate that model accuracy isn’t always great way to assess performance of the model. So Model performance is more important.
4. What are the necessary steps involved in Machine Learning Project?
There are several essential steps we must follow to achieve a good working model while doing a Machine Learning Project. Those steps may include parameter tuning, data preparation, data collection, training the model, model evaluation, and prediction, etc.
————————————————————-
🚨MAKE YOUR CAREER IN DATA SCIENCE AND ANALYTICS WITH CLOUDYML FROM SCRATCH. TRUSTED BY 7500+ HAPPY LEARNERS
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Step 1. After your enrollment you will get complete lifetime content access in your learning portal. Also this course is self paced course so you can learn according to your time availability.
Step 2. We will assign you personal Mentors over skype chat. They will be available everyday between 6PM to 12midnight to resolve all of your doubts and queries.
Step 3. Structured Tutorial Videos with Practice Assignments will be there so you can start boosting your confidence from Day 1.
Step 4. Capstone End-to-End Projects for your skill enhancement and resume.
Step 5. You will get Topic wise course completion certificate after completing the course.
Step 6. Interview QnA PDF Collection, Job Opening Mails, Sample Resume and Resume review feature will be able for you.
Our course reviews are our motivation.
You will find a lot of success stories and feedback of our course on our website.
Course Link👉🏻 https://ai.cloudyml.com/Mega-Combo
So How Everything Works At CloudyML ?
Step 1. After your enrollment you will get complete lifetime content access in your learning portal. Also this course is self paced course so you can learn according to your time availability.
Step 2. We will assign you personal Mentors over skype chat. They will be available everyday between 6PM to 12midnight to resolve all of your doubts and queries.
Step 3. Structured Tutorial Videos with Practice Assignments will be there so you can start boosting your confidence from Day 1.
Step 4. Capstone End-to-End Projects for your skill enhancement and resume.
Step 5. You will get Topic wise course completion certificate after completing the course.
Step 6. Interview QnA PDF Collection, Job Opening Mails, Sample Resume and Resume review feature will be able for you.
Our course is designed in such a way that anyone from Coding OR Non-coding background can join and become a job ready Data Scientist/Data Analyst/Business Analyst and you will not get all these things anywhere at such affordable price.
Our Platform is chosen and trusted by 8000+ Happy learners and more than 2000 learners have already switched to Data Science domain and a lot of students/freshers got internships and jobs after completing this course. IT professionals are getting Data Science projects after doing the course and also a lot of them successfully switched to Data Science domain.
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Our Platform is chosen and trusted by 8000+ Happy learners and more than 2000 learners have already switched to Data Science domain and a lot of students/freshers got internships and jobs after completing this course. IT professionals are getting Data Science projects after doing the course and also a lot of them successfully switched to Data Science domain.
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🤩1
Date: 25th August, 2022
Company Name: Accenture
Role: Data Scientist
Topic :Machine learning, NLP
1. What do you understand by a random forest model?
It combines multiple models together to get the final output or, to be more precise, it combines multiple decision trees together to get the final output. So, decision trees are the building blocks of the random forest model.
2. How are Data Science and Machine Learning related to each other?
Data Science and Machine Learning are two terms that are closely related but are often misunderstood. Both of them deal with data. Data Science is a broad field that deals with large volumes of data and allows us to draw insights out of this voluminous data. Machine Learning, on the other hand, can be thought of as a sub-field of Data Science. It also deals with data, but here, we are solely focused on learning how to convert the processed data into a functional model, which can be used to map inputs to outputs, e.g., a model that can expect an image as an input and tell us if that image contains a flower as an output.
3. What is a kernel function in SVM?
In the SVM algorithm, a kernel function is a special mathematical function. In simple terms, a kernel function takes data as input and converts it into a required form. This transformation of the data is based on something called a kernel trick, which is what gives the kernel function its name. Using the kernel function, we can transform the data that is not linearly separable (cannot be separated using a straight line) into one that is linearly separable.
4. Explain TF/IDF vectorization.
The expression ‘TF/IDF’ stands for Term Frequency–Inverse Document Frequency. It is a numerical measure that allows us to determine how important a word is to a document in a collection of documents called a corpus. TF/IDF is used often in text mining and information retrieval.
————————————————————-
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Course Link👉🏻 https://ai.cloudyml.com/Mega-Combo
Company Name: Accenture
Role: Data Scientist
Topic :Machine learning, NLP
1. What do you understand by a random forest model?
It combines multiple models together to get the final output or, to be more precise, it combines multiple decision trees together to get the final output. So, decision trees are the building blocks of the random forest model.
2. How are Data Science and Machine Learning related to each other?
Data Science and Machine Learning are two terms that are closely related but are often misunderstood. Both of them deal with data. Data Science is a broad field that deals with large volumes of data and allows us to draw insights out of this voluminous data. Machine Learning, on the other hand, can be thought of as a sub-field of Data Science. It also deals with data, but here, we are solely focused on learning how to convert the processed data into a functional model, which can be used to map inputs to outputs, e.g., a model that can expect an image as an input and tell us if that image contains a flower as an output.
3. What is a kernel function in SVM?
In the SVM algorithm, a kernel function is a special mathematical function. In simple terms, a kernel function takes data as input and converts it into a required form. This transformation of the data is based on something called a kernel trick, which is what gives the kernel function its name. Using the kernel function, we can transform the data that is not linearly separable (cannot be separated using a straight line) into one that is linearly separable.
4. Explain TF/IDF vectorization.
The expression ‘TF/IDF’ stands for Term Frequency–Inverse Document Frequency. It is a numerical measure that allows us to determine how important a word is to a document in a collection of documents called a corpus. TF/IDF is used often in text mining and information retrieval.
————————————————————-
🚨MAKE YOUR CAREER IN DATA SCIENCE AND ANALYTICS WITH CLOUDYML FROM SCRATCH. TRUSTED BY 8000+ HAPPY LEARNERS
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Course Link👉🏻 https://ai.cloudyml.com/Mega-Combo
#Quiz
What will be the output of the following code snippet?
a = [1, 2, 3]
a = tuple(a)
a[0] = 2
print(a)
What will be the output of the following code snippet?
a = [1, 2, 3]
a = tuple(a)
a[0] = 2
print(a)
Crack Aptitude Round of Accenture Like A Pro 😎
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Date: 26th August, 2022
Company : Cognizant
Job role: Data analyst
Topic: Excel, Tableau, Statistics, SQL
1. What are the common problems that data analysts encounter during analysis?
The common problems steps involved in any analytics project are:
Handling duplicate data
Collecting the meaningful right data at the right time
Handling data purging and storage problems
Making data secure and dealing with compliance issues
2. Explain the Type I and Type II errors in Statistics?
In Hypothesis testing, a Type I error occurs when the null hypothesis is rejected even if it is true. It is also known as a false positive.
A Type II error occurs when the null hypothesis is not rejected, even if it is false. It is also known as a false negative.
3. How do you make a dropdown list in MS Excel?
First, click on the Data tab that is present in the ribbon.
Under the Data Tools group, select Data Validation.
Then navigate to Settings > Allow > List.
Select the source you want to provide as a list array.
4. How do you subset or filter data in SQL?
To subset or filter data in SQL, we use WHERE and HAVING clauses which give us an option of including only the data matching certain conditions.
5. What is a Gantt Chart in Tableau?
A Gantt chart in Tableau depicts the progress of value over the period, i.e., it shows the duration of events. It consists of bars along with the time axis. The Gantt chart is mostly used as a project management tool where each bar is a measure of a task in the project
————————————————————-
🚨MAKE YOUR CAREER IN DATA SCIENCE AND ANALYTICS WITH CLOUDYML FROM SCRATCH. TRUSTED BY 8000+ HAPPY LEARNERS
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Course Link👉🏻 https://ai.cloudyml.com/Mega-Combo
Company : Cognizant
Job role: Data analyst
Topic: Excel, Tableau, Statistics, SQL
1. What are the common problems that data analysts encounter during analysis?
The common problems steps involved in any analytics project are:
Handling duplicate data
Collecting the meaningful right data at the right time
Handling data purging and storage problems
Making data secure and dealing with compliance issues
2. Explain the Type I and Type II errors in Statistics?
In Hypothesis testing, a Type I error occurs when the null hypothesis is rejected even if it is true. It is also known as a false positive.
A Type II error occurs when the null hypothesis is not rejected, even if it is false. It is also known as a false negative.
3. How do you make a dropdown list in MS Excel?
First, click on the Data tab that is present in the ribbon.
Under the Data Tools group, select Data Validation.
Then navigate to Settings > Allow > List.
Select the source you want to provide as a list array.
4. How do you subset or filter data in SQL?
To subset or filter data in SQL, we use WHERE and HAVING clauses which give us an option of including only the data matching certain conditions.
5. What is a Gantt Chart in Tableau?
A Gantt chart in Tableau depicts the progress of value over the period, i.e., it shows the duration of events. It consists of bars along with the time axis. The Gantt chart is mostly used as a project management tool where each bar is a measure of a task in the project
————————————————————-
🚨MAKE YOUR CAREER IN DATA SCIENCE AND ANALYTICS WITH CLOUDYML FROM SCRATCH. TRUSTED BY 8000+ HAPPY LEARNERS
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Course Link👉🏻 https://ai.cloudyml.com/Mega-Combo
Check SQL Interview Question asked by Zendrive company in our latest YouTube Video. Get explained solution. Link below.
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Date: 27th August, 2022
Company Name: Tiger Analytics
Role: ML Engineer
1. How is KNN different from k-means?
KNN or K nearest neighbors is a supervised algorithm which is used for classification and regression purpose. In KNN, a test sample is given as the class of the majority of its nearest neighbors. On the other side, K-means is an unsupervised algorithm which is mainly used for clustering. In k-means clustering, it needs a set of unlabeled points and a threshold only. The algorithm further takes unlabeled data and learns how to cluster it into groups by computing the mean of the distance between different unlabeled points.
2. What do you understand by ILP?
ILP stands for Inductive Logic Programming. It is a part of machine learning which uses logic programming. It aims at searching patterns in data which can be used to build predictive models. In this process, the logic programs are assumed as a hypothesis.
3. What do you understand by Cluster Sampling?
Cluster Sampling is a process of randomly selecting intact groups within a defined population, sharing similar characteristics. Cluster sample is a probability where each sampling unit is a collection or cluster of elements.
For example, if we are clustering the total number of managers in a set of companies, in that case, managers (sample) will represent elements and companies will represent clusters.
4. What is Regularization? What kind of problems does regularization solve?
A regularization is a form of regression, which constrains/ regularizes or shrinks the coefficient estimates towards zero. In other words, it discourages learning a more complex or flexible model to avoid the risk of overfitting. It reduces the variance of the model, without a substantial increase in its bias.
Regularization is used to address overfitting problems as it penalizes the loss function by adding a multiple of an L1 (LASSO) or an L2 (Ridge) norm of weights vector w.
Company Name: Tiger Analytics
Role: ML Engineer
1. How is KNN different from k-means?
KNN or K nearest neighbors is a supervised algorithm which is used for classification and regression purpose. In KNN, a test sample is given as the class of the majority of its nearest neighbors. On the other side, K-means is an unsupervised algorithm which is mainly used for clustering. In k-means clustering, it needs a set of unlabeled points and a threshold only. The algorithm further takes unlabeled data and learns how to cluster it into groups by computing the mean of the distance between different unlabeled points.
2. What do you understand by ILP?
ILP stands for Inductive Logic Programming. It is a part of machine learning which uses logic programming. It aims at searching patterns in data which can be used to build predictive models. In this process, the logic programs are assumed as a hypothesis.
3. What do you understand by Cluster Sampling?
Cluster Sampling is a process of randomly selecting intact groups within a defined population, sharing similar characteristics. Cluster sample is a probability where each sampling unit is a collection or cluster of elements.
For example, if we are clustering the total number of managers in a set of companies, in that case, managers (sample) will represent elements and companies will represent clusters.
4. What is Regularization? What kind of problems does regularization solve?
A regularization is a form of regression, which constrains/ regularizes or shrinks the coefficient estimates towards zero. In other words, it discourages learning a more complex or flexible model to avoid the risk of overfitting. It reduces the variance of the model, without a substantial increase in its bias.
Regularization is used to address overfitting problems as it penalizes the loss function by adding a multiple of an L1 (LASSO) or an L2 (Ridge) norm of weights vector w.
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Date: 28-08-2022
Company name: Accenture
Job role: Data Scientist
Topic : deep learning, SQL
1. What is batch normalization?
Batch normalization is a technique through which attempts could be made to improve the performance and stability of the neural network. This can be done by normalizing the inputs in each layer so that the mean output activation remains 0 with the standard deviation at 1.
2. What is GAN?
The Generative Adversarial Network takes inputs from the noise vector and sends them forward to the Generator, and then to Discriminator, to identify and differentiate unique and fake inputs.
3. What is the case when in SQL Server?
The CASE statement is used to construct logic in which one column’s value is determined by the values of other columns.
At least one set of WHEN and THEN commands makes up the SQL Server CASE Statement. The condition to be tested is specified by the WHEN statement. If the WHEN condition returns TRUE, the THEN sentence explains what to do.
When none of the WHEN conditions return true, the ELSE statement is executed. The END keyword brings the CASE statement to a close.
4. What is pickling and unpickling?
Ans: Pickle module accepts any Python object and converts it into a string representation and dumps it into a file by using dump function, this process is called pickling. While the process of retrieving original Python objects from the stored string representation is called unpickling.
————————————————————-
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Course Link👉🏻 https://ai.cloudyml.com/Mega-Combo
Company name: Accenture
Job role: Data Scientist
Topic : deep learning, SQL
1. What is batch normalization?
Batch normalization is a technique through which attempts could be made to improve the performance and stability of the neural network. This can be done by normalizing the inputs in each layer so that the mean output activation remains 0 with the standard deviation at 1.
2. What is GAN?
The Generative Adversarial Network takes inputs from the noise vector and sends them forward to the Generator, and then to Discriminator, to identify and differentiate unique and fake inputs.
3. What is the case when in SQL Server?
The CASE statement is used to construct logic in which one column’s value is determined by the values of other columns.
At least one set of WHEN and THEN commands makes up the SQL Server CASE Statement. The condition to be tested is specified by the WHEN statement. If the WHEN condition returns TRUE, the THEN sentence explains what to do.
When none of the WHEN conditions return true, the ELSE statement is executed. The END keyword brings the CASE statement to a close.
4. What is pickling and unpickling?
Ans: Pickle module accepts any Python object and converts it into a string representation and dumps it into a file by using dump function, this process is called pickling. While the process of retrieving original Python objects from the stored string representation is called unpickling.
————————————————————-
🚨MAKE YOUR CAREER IN DATA SCIENCE AND ANALYTICS WITH CLOUDYML FROM SCRATCH. TRUSTED BY 8000+ HAPPY LEARNERS
This course package, you are getting Python, SQL, Statistics, Machine Learning, Deep Learning, AWS Sagemaker, Model Deployment, Tableau, PowerBi, Amazon QuickSight, Google Data Studio, Excel, Case studies, Interview QnAs PDF, Capstone End-to-End Projects of ML and DL with 1-1 Live Doubt Clearance Support over Skype chat everyday. Check the course NOW!
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🚨JOB OPENING ALERT🚨
Role: Data Analyst
Company: Media.Net
Exp: 0-1 yr
Link: https://ai.cloudyml.com/3PVxtZm
Role: Business Operations Analyst
Company: Squadrun, Inc
Exp: 0-2 yr
Link: https://ai.cloudyml.com/3Tr5Yd5
Role: Business Analyst
Company: Josh Software
Exp: 0-3 yr
Link: https://ai.cloudyml.com/3To21Wv
Role: Business Analyst
Company: Value Chain Solutions
Exp: 0-1 yr
Link: https://ai.cloudyml.com/3AqIe01
All the best!
Role: Data Analyst
Company: Media.Net
Exp: 0-1 yr
Link: https://ai.cloudyml.com/3PVxtZm
Role: Business Operations Analyst
Company: Squadrun, Inc
Exp: 0-2 yr
Link: https://ai.cloudyml.com/3Tr5Yd5
Role: Business Analyst
Company: Josh Software
Exp: 0-3 yr
Link: https://ai.cloudyml.com/3To21Wv
Role: Business Analyst
Company: Value Chain Solutions
Exp: 0-1 yr
Link: https://ai.cloudyml.com/3AqIe01
All the best!
Date: 29th August, 2022
Company: PWC
Role: Data Analyst
Topics: Tableau, Excel
1. What do Tableau's sets and groups mean?
Data is grouped using sets and groups according to predefined criteria. The primary distinction between the two is that although a set can have only two options—either in or out—a group can divide the dataset into several groups. A user should decide which group or sets to apply based on the conditions.
2.What in Excel is a macro?
An Excel macro is an algorithm or a group of steps that helps automate an operation by capturing and replaying the steps needed to finish it. Once the steps have been saved, you may construct a Macro that the user can alter and replay as often as they like.
Macro is excellent for routine work because it also gets rid of mistakes. Consider the scenario when an account manager needs to share reports about staff members who owe the company money. If so, it can be automated by utilising a macro and making small adjustments each month as necessary.
3.Gantt chart in Tableau
A Tableau Gantt chart illustrates the duration of events as well as the progression of value across the period. Along with the time axis, it has bars. The Gantt chart is primarily used as a project management tool, with each bar representing a project job.
4.In Microsoft Excel, how do you create a drop-down list?
Start by selecting the Data tab from the ribbon.
Select Data Validation from the Data Tools group.
Go to Settings > Allow > List next.
Choose the source you want to offer in the form of a list array.
Company: PWC
Role: Data Analyst
Topics: Tableau, Excel
1. What do Tableau's sets and groups mean?
Data is grouped using sets and groups according to predefined criteria. The primary distinction between the two is that although a set can have only two options—either in or out—a group can divide the dataset into several groups. A user should decide which group or sets to apply based on the conditions.
2.What in Excel is a macro?
An Excel macro is an algorithm or a group of steps that helps automate an operation by capturing and replaying the steps needed to finish it. Once the steps have been saved, you may construct a Macro that the user can alter and replay as often as they like.
Macro is excellent for routine work because it also gets rid of mistakes. Consider the scenario when an account manager needs to share reports about staff members who owe the company money. If so, it can be automated by utilising a macro and making small adjustments each month as necessary.
3.Gantt chart in Tableau
A Tableau Gantt chart illustrates the duration of events as well as the progression of value across the period. Along with the time axis, it has bars. The Gantt chart is primarily used as a project management tool, with each bar representing a project job.
4.In Microsoft Excel, how do you create a drop-down list?
Start by selecting the Data tab from the ribbon.
Select Data Validation from the Data Tools group.
Go to Settings > Allow > List next.
Choose the source you want to offer in the form of a list array.
Anonymous Quiz
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Date: 30th August, 2022
Company name: IBM
role: ML engineer
Topic : Machine learning
1. Explain how a ROC curve works.
Answer: The ROC curve is a graphical representation of the contrast between true positive rates and the false positive rate at various thresholds. It’s often used as a proxy for the trade-off between the sensitivity of the model (true positives) vs the fall-out or the probability it will trigger a false alarm (false positives).
2. What’s the difference between a generative and discriminative model?
Answer: A generative model will learn categories of data while a discriminative model will simply learn the distinction between different categories of data. Discriminative models will generally outperform generative models on classification tasks.
3. When should you use classification over regression?
Answer: Classification produces discrete values and dataset to strict categories, while regression gives you continuous results that allow you to better distinguish differences between individual points. You would use classification over regression if you wanted your results to reflect the belongingness of data points in your dataset to certain explicit categories (ex: If you wanted to know whether a name was male or female rather than just how correlated they were with male and female names.)
4. How do you think Google is training data for self-driving cars?
Answer: Google is currently using recaptcha to source labeled data on storefronts and traffic signs. They are also building on training data collected by Sebastian Thrun at GoogleX—some of which was obtained by his grad students driving buggies on desert dunes!
————————————————————-
🚨MAKE YOUR CAREER IN DATA SCIENCE AND ANALYTICS WITH CLOUDYML FROM SCRATCH. TRUSTED BY 8000+ HAPPY LEARNERS
This course package, you are getting Python, SQL, Statistics, Machine Learning, Deep Learning, AWS Sagemaker, Model Deployment, Tableau, PowerBi, Amazon QuickSight, Google Data Studio, Excel, Case studies, Interview QnAs PDF, Capstone End-to-End Projects of ML and DL with 1-1 Live Doubt Clearance Support over Skype chat everyday. Check the course NOW!
Course Link👉🏻 https://ai.cloudyml.com/Mega-Combo
Company name: IBM
role: ML engineer
Topic : Machine learning
1. Explain how a ROC curve works.
Answer: The ROC curve is a graphical representation of the contrast between true positive rates and the false positive rate at various thresholds. It’s often used as a proxy for the trade-off between the sensitivity of the model (true positives) vs the fall-out or the probability it will trigger a false alarm (false positives).
2. What’s the difference between a generative and discriminative model?
Answer: A generative model will learn categories of data while a discriminative model will simply learn the distinction between different categories of data. Discriminative models will generally outperform generative models on classification tasks.
3. When should you use classification over regression?
Answer: Classification produces discrete values and dataset to strict categories, while regression gives you continuous results that allow you to better distinguish differences between individual points. You would use classification over regression if you wanted your results to reflect the belongingness of data points in your dataset to certain explicit categories (ex: If you wanted to know whether a name was male or female rather than just how correlated they were with male and female names.)
4. How do you think Google is training data for self-driving cars?
Answer: Google is currently using recaptcha to source labeled data on storefronts and traffic signs. They are also building on training data collected by Sebastian Thrun at GoogleX—some of which was obtained by his grad students driving buggies on desert dunes!
————————————————————-
🚨MAKE YOUR CAREER IN DATA SCIENCE AND ANALYTICS WITH CLOUDYML FROM SCRATCH. TRUSTED BY 8000+ HAPPY LEARNERS
This course package, you are getting Python, SQL, Statistics, Machine Learning, Deep Learning, AWS Sagemaker, Model Deployment, Tableau, PowerBi, Amazon QuickSight, Google Data Studio, Excel, Case studies, Interview QnAs PDF, Capstone End-to-End Projects of ML and DL with 1-1 Live Doubt Clearance Support over Skype chat everyday. Check the course NOW!
Course Link👉🏻 https://ai.cloudyml.com/Mega-Combo