Machine Learning Interview Questions & Answers | Machine Learning Interview Preparation |Simplilearn
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This Machine Learning Interview Questions And Answers video will help you prepare for Data Science and Machine learning interviews. This video is ideal for both beginners as well as professionals who are appearing for Machine Learning or Data Science interviews. Learn what are the most important Machine Learning interview questions and answers and know what will set you apart in the interview process. This video is a part of the Machine Learning with Python Series.
Some of the important Machine Learning Interview Questions are listed below:
1. What are the different types of Machine Learning?
2. What is overfitting? And how can you avoid it?
3. What is false positive and false negative and how are they significant?
4. What are the three stages to build a model in Machine Learning?
5. What is Deep Learning?
6. What are the differences between Machine Learning and Deep Learning?
7. What are the applications of supervised Machine Learning in modern businesses?
8. What is semi-supervised Machine Learning?
9. What are the unsupervised Machine Learning techniques?
10. What is the difference between supervised and unsupervised Machine Learning?
11. What is the difference between inductive Machine Learning and deductive Machine Learning?
12. What is 'naive' in the Naive Bayes classifier?
13. What are Support Vector Machines?
14. How is Amazon able to recommend other things to buy? How does it work?
15. When will you use classification over regression?
16. How will you design an email spam filter?
17. What is Random Forest?
18. What is bias and variance in a Machine Learning model?
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Do you have any questions on this topic? Please share your feedback in the comment section below and we'll have our experts answer it for you.
Thanks for watching the video. Cheers!
Nice
We are glad you liked our video, Babu. Do spread the word and subscribe to our channel for more videos.
Happy Learning from Simplilearn team!
Yes, question about stuff you said in 11:34. Are u saying that providing duplicated values makes classifier more accurate?
bias variance diagrams with the size of data set question (05) are switched i believe
Thanks so much, I have an interview tomorrow morning and this helped wrap up a few concepts!
We are glad you found our video helpful, Beltran. Like and share our video with your peers and also do not forget to subscribe to our channel for not missing video updates. We will be coming up with more such videos. Cheers!
Thank You for this video! I'm able to answer all the questions and this boosted my confidence for my upcoming interview. Thanks a lot again.
WooHoo! We are so happy you love our videos. We are glad to have helped. Do show your love by subscribing our channel using this link: czcams.com/users/Simplilearn and don't forget to hit the like button as well. All the best for your career as well! Cheers!
Hello! I really enjoyed watching this video. It was slowly and simply explained, with analogies and keywords. Would really appreciate it if you made a part two with advanced questions covering regression and different models (SVM, KNN... in depth) :)
Thank you!
Glad you enjoyed it! Thank you for the suggestion, and for watching!
Please explain different algos (Logestic Regression ,SVM,Decision Trees, RandomForest) with their parameters and how these parameters affects our models .
Hi Ayush, we are glad that you have shown interest in Machin learning. I'll give you the links below where you can get your required information.
1. Logistic Regression: czcams.com/video/XnOAdxOWXWg/video.html
2. SVM: czcams.com/video/TtKF996oEl8/video.html
3. Decision Tree: czcams.com/video/RmajweUFKvM/video.html
4. Random Forest: czcams.com/video/eM4uJ6XGnSM/video.html
perfect summary before any Machine learning interview... thx
Hi Ahmed, thanks for your kind words. Spread the word by liking, sharing and subscribing to our channel! Cheers :). You can also explore our playlists for more Machine Learning videos- czcams.com/video/7JhjINPwfYQ/video.html.
It was a quick review of main principles in Machine learning and it would be useful to brush up and summarize what you probably learned here and there about Machine Learning.
Hi Mark, thanks for checking out our tutorial and for sharing the information. Do subscribe to our channel to stay posted on upcoming tutorials. Cheers!
Thank you so much for this video. It is really helpful
Hey Shaila, thank you for appreciating our work. We are glad to have helped. Do check out our other tutorial videos and subscribe to us to stay connected. Cheers :)
Thank you Simplilearn!
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Thank you so much, I really enjoy this class and learned so much.
You're very welcome! Thank you for watching!
Thank you so much. truly appreciated!!
You are very welcome!
This is an excellent set of ML interview questions, however few more practical based questions should have been more helpful. I did enjoy the session very much as the explanation was given in a precise manner and easy to understand.
Hey Dinen, thank you for appreciating our work. We are glad to have helped. Do check out our other tutorial videos and subscribe to us to stay connected. Cheers :)
Great video, really useful ! Thanks a lot
We are glad you found our video helpful, Mau. Like and share our video with your peers and also do not forget to subscribe to our channel for not missing video updates. We will be coming up with more such videos. Cheers!
Thank you for this great content. minute 59 : Images don't match the text: if you have big dataset then you are Ok with high variance so you can pick a model with high variance low bias
"Hi Mohammed,
Aplogoies for the error in the answer to that question."
thank you bagging concept is also important to add here.
Hey Saurabh, thank you for appreciating our work. We are glad to have helped. Do check out our other tutorial videos and subscribe to us to stay connected. Cheers :)
Question 5, at 17:45 bullet points and the pictorial view are in contradiction. Say for small data.. high bias & low variance are given in explanation whereas picture says algorithm with low bias/high variance...
I agree,
These are real basic an super easy questions... i doubt any machine learning interviews will consist many of the questions from these... maybe 1-2 just for warm up. Be prepared to face stats and prob questions... master operations with matrices...
Yes, Interview always end up with practical application problems. However, these questions would be of great help to clear the basic questions and makes you confident enough for the next round. We highly appreciate your input and don't forget to subscribe to our channel. Cheers!
Very clear and detailed explaination. Thanks.
Hello, thank you for watching our video. We are glad that you liked our video. Do subscribe and stay connected with us. Cheers :)
Thx for the video.Please make a video on how to consider selected variables among a vast number of variables when making a regression model.What are the parameters we should look in those variables such as correlation,domain knowledge...and how should we prioritize them.
Hey Sagn, thank you for watching our video. We will definitely look into your suggestions and bring up new videos in future. Do subscribe and stay tuned for updates on our channel. Cheers :)
Very informative sir. Thank u
its like bonus when you complete your ML basic concepts :)
We hope you found our video very helpful. Do subscribe to our channel and get our new video updates directly into your email.
Amazing video, vast questions put well together.
Glad you enjoyed it
Thanks !!
It is our pleasure! Check out czcams.com/video/7JhjINPwfYQ/video.html for more in-depth tutorials! Happy learning!
I really enjoyed the session can you please come up with Deep learning concepts session.
Hey Piyush, thank you for appreciating our work. We are glad to have helped. Do check out our other tutorial videos and subscribe to us to stay connected. Cheers :)
Great job!
Nice .Very informative
Hi Sidrah, We are glad that you found our video informative. If you are thinking of signing up for the machine learning course, please click on the link for more information: www.simplilearn.com/big-data-and-analytics/machine-learning-certification-training-course.
useful set of knowledge and content is impressive.
Hi Gopal, thank you for watching our video. We are glad that you liked our video. Do subscribe and stay connected with us. Cheers :)
thankyou so much
Very welcome!
Good Questions With excellent explaination but need more elaboration on some topics like Logistic regression and decision tree,support vector machine
Hi Satyendra, thanks for checking out our tutorial and for sharing the information. We will definitely look into your suggestion. Do subscribe to our channel to stay posted on upcoming tutorials. Cheers!
very informative sir.plz give us real time examples with data mining sir
Hi Radhika, we are glad you found our video helpful and informative. You can check out this link to know the real time examples with data mining: www.matillion.com/insights/5-real-life-applications-of-data-mining-and-business-intelligence/.
In this video, I see most of the questions are on classification. can you come up with questions on regression please? Also, this video is very nice.
Hey Bhaskaruni, thank you for watching our video. We will definitely look into your suggestions. Do subscribe and stay tuned for updates on our channel. Cheers :)
the figure at 37:50 also explains the procedure wrong. non-labeled + labeled is not processed in training step..
Your explanation is so good
We're so glad that you enjoyed your time learning with us! If you're interested in continuing your education and developing new skills, take a look at our course offerings in the description box. We're confident that you'll find something that piques your interest!
I like your presentation theme and font. Is it custom made? Cheers.
Hey Omer, thank you for appreciating our work. We are glad to have helped. Do check out our other tutorial videos and subscribe to us to stay connected. Cheers :)
Nice video,very use full .
Glad you enjoyed our video! We have a ton more videos like this on czcams.com/video/ukzFI9rgwfU/video.html. We hope you will join our community!
@@SimplilearnOfficial OK sir
Welldone.. Nice class👏🏻👏🏻
WooHoo! We are so happy you love our videos. Please do keep checking back in. We put up new videos every day on all your favourite topics Have a good day!
Thankyou so much
Most welcome 😊
good efforts dear friend. keep it up.
Thank you! Cheers!
KNN can also be used for regression.
Yes, it is possible. KNN regression is sometimes powerful, simple algorithm and should be used only when your dataset is small, clean and not wide.
Thank you
You're welcome
your voice is going up and down its difficult to listen at any volume level
Thank you so much for bringing this to our attention. We reported this right away to the relevant department.
I need face telephonic interview on 3 jan for data science so please tell me what the best away to get over
Hi Ravi, we cannot guarantee your outcome in your interview. However, you can go through this Data Science Interview questions which will be a good heads start for your preparation: czcams.com/video/5JZsSNLXXuE/video.html
Great Vid about the topic Machine Learning. Can I have your Presentation to download that you used in your Video? I need it to learn the different Kinds of Machine learning for my Modul in my University. I will write an exam about this. Your Videos is a great summary about the Difference of Machine Learning which I should learn for it.
Hi, you can check out the slides for this video from this link: www.slideshare.net/Simplilearn/machine-learning-interview-questions-and-answers-data-science-interview-questions-simplilearn.
Question 19 (52:50) bullet points and the graphic are in contradiction
Thank you so much for bringing this to our attention. We reported this right away to the relevant department.
@@SimplilearnOfficial Hi, also the slides at 18:16, the graphics and explanation are contradictory
Helpful. 52:13 graphic and text is not in sync
Good eye! Thank you so much for bringing this to our attention. We reported this right away to the relevant department.
There's a mistake at 52.55. Check the info is given and the below picture text are reversed low bia high variance..
"Hi Toral,
Thanks for pointing out the correction. Yes, the images have been altered for large and small data."
Do they also ask Data Structures and Algorithm questions during Machine Learning Engineer Interview?
There is a good possibility of asking these questions since Machine learning and Data science are related to one another. Hope that helps!
yes they do.. Data science is all about managing data... in real time u will face problems where u require knowledge of data sets... like wat to do if a given array exceeds main memory.. or how to increase the speed of your code... Data structures are the basic building blocks of any coding task
what is the model of the cluster
Hi Ravi, it would be great if you could elaborate on your question. Thanks!
Sir what are the scope for freshers in field of machine learning
Hi Sharda, machine learning is one of the booming industry in the world. Machine Learning jobs for freshers may vary between ₹ 699,807- 891,326. With a sound knowledge of data analysis and algorithms and a few years of experience, you may expect a salary of ₹ 1,759,777 monthly or ₹ 9, 00,000 per annum.
If you interested in getting certified in Machine Learning, check this link: www.simplilearn.com/big-data-and-analytics/machine-learning-certification-training-course.
is logistic regression a non linear classifier?
Logistic Regression can be carried out not only in Python but with other tools like R, SAS, Excel, SPSS, Gretl, etc. It is generally used for classification and to predict the outcome of a binary target variable.
It's a linear classification model
Thanks for your input!
Here is a quick summary:
Any algorithm will be considered as linear one if it produces a linear decision boundary , which is the result of additivity of of the parameters.
1. Logistic Regression produces linear decision boundary because of additivity : z = w_1 * x_1 + w_2 * x_2. Although it has non-linear activation function(sigmoid), but the decision boundary is still linear. To see the math :
"stats.stackexchange.com/questions/93569/why-is-logistic-regression-a-linear-classifier"
2. Whereas Neural networks are non-linear classifier , because it produces non-linear decision boundary since there are two layers of sigmoid functions in the neural network: one in each of the output nodes plus an additional sigmoid function to combine and threshold the results of each output node.
3. Linear SVM is a linear classifier bcz it has a linear decision boundary . Linear kernel used.
4. Kernel SVM is a nonlinear classifier bcz of non-linear decision boundary, which is a result of mapping from lower dimension to higher dimension (bcz in higher dimension data becomes linearly separable). Gaussian kernel used.
5. Decision tree and random forest both are non-linear as well .
Thanks for your valuable input!
The left and right figures at 17:45 should be swapped I think.. "small data sets, the algorithm with low bias/high variance" is wrong
Machine Learning Engineer here. It is true
What is the scope for machine learning jobs in India and how much salaries we can expect?
Forbes predicts the theoretical research in ML will gradually pave the way for business problem-solving. So the year 2018 will see a sharp increase in demand for professionals with skills in emerging technologies such as Artificial Intelligence (AI) and machine learning.
According to custom salary report, the average salary for a Data Scientist, IT with Machine Learning skills is Rs 874,691 per year. Experience strongly influences pay for this job.
can I have the cupcakes vs muffins dataset please?
Hi Narsimhu, thanks for watching our video. We have sent the requested dataset to your mail ID. Do subscribe and stay tuned for updates on our channel. Cheers :)
What is the output of Machine Learning?
Given that a coin is tossed 100 times what is the expected value of getting Head?
Thank You.
Hey, Jallepalli! We could say that tossing a coin 100 times doesn't exactly involve Machine Learning. A true unbiased coin will always have 50% chances of landing head each time.
Nonetheless, if a coin is biased, then we can build a simple Machine Learning model taking in features such as the weight distribution of the coin, starting position before the toss and the force applied in each toss to correlate it with the output. Then, we would, in theory, be able to correctly determine the result of the toss even before the coin lands. Cheers!
Please work on the sound quality!
Thanks for the honest feedback. We will look into it.
In Question No:7 that table explanation is wrong I think so.....
True Positive :12
True Negative: 3
False Positive :1
False Negative :9
17:45 image is misplaced
Good eye! Our mistake :
Cmon man, is an interviewer really going to ask such a basic question as what is training and test data?
Hi, this video made for beginner which includes both questions from beginners to intermediate level. Thanks.
The figure is wrong in 50:51 of the video
sorry 52:14
Good eye! Our mistake :
Audio speed 1.15
Thanks, me later
Thanks for watching our video. Do subscribe to our channel and do not forget to hit the bell icon for never miss another update. Cheers :)
Sound quality is very bad
We are sorry about that Vishnu, we will share the feedback with the relevant department
Worst audio quality I ever heard.
We are sorry about that xxx, we will share the feedback with the relevant department.
0:10 Your voice is a bit creepy to say the least!
Thank you for letting us know know about this. Your feedback helps us get better. We are looking into this issue and hope to resolve it promptly and accurately.
11:34 Pass the same stuff multiple times? The same 800 records over and over again? It doesn't make sense. Expected something better than that from 100k subs channel.
There are common algorithms that use multi-passes on the training set in order to converge. I.E. take a look at SGD.
Bootstrapping.
Epochs for linear regression and neural networks
poor voice quality
We are sorry about that Rajat, we will share the feedback with the relevant department
Very bad quality of voice
Thank you for bringing this to our attention. We’re sorry you had a bad experience. We’ll strive to do better