Machine Learning Interview Questions and Answers | Machine Learning Interview Preparation | Edureka
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- čas přidán 16. 05. 2024
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This Machine Learning Interview Questions and Answers video will help you to prepare yourself for Data Science / Machine Learning interviews. This video is ideal for both beginners as well as professionals who want to learn or brush up their concepts in Machine Learning core-concepts, Machine Learning using Python and Machine Learning Scenarios. Below are the topics covered in this tutorial:
1. Machine Learning Core Interview Question
2. Machine Learning using Python Interview Question
3. Machine Learning Scenario based Interview Question
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Data Science is a set of techniques that enables the computers to learn the desired behavior from data without explicitly being programmed. It employs techniques and theories drawn from many fields within the broad areas of mathematics, statistics, information science, and computer science. This course exposes you to different classes of machine learning algorithms like supervised, unsupervised and reinforcement algorithms. This course imparts you the necessary skills like data pre-processing, dimensional reduction, model evaluation and also exposes you to different machine learning algorithms like regression, clustering, decision trees, random forest, Naive Bayes and Q-Learning.
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00:00:00 (00) Introduction to the Video / Speaker / ML / Agenda - (1) Core Concepts (2) Python based (3) Scenario based questions
00:03:07 (01) How can concept of ML can be explained to a school going kid ........
00:04:28 (02) What are the types of ML.......... Supervised / Unsupervised / Reinforcement learning (Hit and try / reward & Penalty) / Semi supervised
00:09:04 (03) What is your favourite algo and its explanation
00:09:46 (04) Difference b/w deep learning and M/L
00:11:29 (05) Difference between Classification and Regression
00:12:46 (06) What do u mean by selection bias
00:13:39 (07) Difference b/w Precision and Recall
00:17:03 (08) Explain True Positive (TP), TN, FP, FN
00:18:42 (09) What is a confusion matrix..........used for summarizing the performance of a classification algo
00:20:47 (10) Difference b/w inductive and deductive learning
00:22:20 (11) Difference b/w KNN and k-means clustering......... supervized vs Unsupervised; K meaning in KNN is neighbours and in K-meana it is no . of clusters
00:23:53 (12) What is ROC curve and what does it represent. ........Receiver operating characteristics Plot of True Positive rate vs False Positive rate
00:26:53 (13) Difference b/w Type-I and Type-II errors.....Type I is False Positive (FP) and Type II False Negative (FN)
00:28:13 (14) Is it better to have too many FP or too many FN
00:30:47 (15) Which is more important to you. Model accuracy or model performance.......model accuracy is part of model performance
00:31:48 (16) Differnce b/w Gini impurity and Entropy in decision tree
00:33:19 (17) Difference b/w Entropy and Information gain .... Information gain getting better as the ndes are getting purer
00:34:40 (18) What is overfitting. how do u ensure you are not overfitting wth a model..... More data .. ensemlbing models ... simpler models .. adding regularizations
00:37:50 (19) Explain ensembling learning tech in ML... Bagging / Boosting
00:41:32 (20) What is Bagging and Boosting in ML
00:44:49 (21) How wud u screen for outliers and how do u handle them
00:47:56 (22) What is collinearity and multi collinearity
00:48:54 (23) What is Eigenvectors and Eigenvalues
00:51:33 (24) What is A/B Testing
00:52:55 (25) What is cluster sampling
00:53:51 (26) Running binary clasification tree is simple. But do u know how the tree decide on whcih variable to split at the root node and its succeeding child nodes
00:56:18 (27) (01) Name a few libraries in python used for data analyss and Scientific computations
00:58:58 (28) (02) Which library wud u prefer for plotting in python: Seaborn or Matplotlib or Bokeh
01:00:32 (29) (03) How are numpy and scipy related to each other
01:01:28 (30) (04) Main differnce b/w Pandas series and single column dataframe in Python
01:02:35 (31) (05) How can u handle duplicate values in a dateset for variable in Python
01:03:16 (32) (06) Write a basic ML progrsm to check the accuracy of the dataset importing any dataset using any classifier
01:07:46 (33) (01) U r given a datset consisting of variables having more than 30% missing values. Let's say out of 50 vars, 8 vars have missing values higher than 30%; How will u deal with them
01:09:42 (34) (02) Write a SQL query that makes recommendations using the pages that ur friends liked. Assume u have two tables: a 2 col table of users and their friends and 2 col table of users and pages they like. It shud not recommend pages u already liked
01:12:00 (35) (03) There is a game where u r asked to roll two fair six sided dice. If the sum of the vals on the dice equls seven, then u win $21. However you must pay $5 to play each time u roll both dice. Do u play the ame. Also, if the player plays it 6 times what is the probability of him making money
01:15:06 (36) (04) We have 2 options for seving ads with newsfeed: (1) Out of every 25 stories 1 will be an ad (2) every story has a 4% chance of being an ad. For each option, wat is the xpected numbers of ads shown in 100 news stores. If we go with optin 2, what is the chance the user wiull be shown a single ad in 100 stories. Wat abt no ads at all
01:18:31 (37) (05) How wud u predict who will renew their subscription next month? What data would u need to solve this. What analysis would u do? Wud u build predictive models. If so which
01:22:04 (38) (06) How do u map nicknames to real names
01:23:34 (39) (07) A jar has 1000 coins of which 999 are fair and 1 is double headed. Pick a coin at random and toss it 10 times. Given that u see 10 heads, wat is the probability that the next toss of that coin s
01:28:02 (40) (08) Suppose u r given a data set which has missing values spread along 1 SD from the median. What % of data would remain unaffected and why
01:28:53 (41) (09) U r given a cancer detection data set. Let u suppose when u build a classification model u achieved an accuracy of 96%. Why shud not u be happy with ur model performance. What can u do about it
01:31:48 (42) (10) U r working on a time series dataset. Ur manager has asked you to build a high accuract model. U start with the tree algo asince u know it works faily well on all kinds of data. Later u tried a time series regression model and got higher accuracy than the earlier model. Can this happen.
01:33:16 (43) (11) Suppose u found that ur model is suffering from low bias and high variance. Which algo u think cud tackle the situation and why
01:36:02 (44) (12) U r given a dataset. The dataset contains many variables, some of which are high correlated and u know abt it. Ur manager has asked u to run PCA. Wud u remove correlated vars first
01:37:21 (45) (13) U r asked to build a multiple regressioon model but ur model R-square isnot as good as u want it to be. For improvement, u remove the intercept term, now ur model r-square becomes 0.8 from 0.3. Is it posssible. how
01:39:10 (46) (14) U r asked to build a random forest model with 1000 trees. During its training u got training error as 0.00. But on testing the validation error was 34.23. What is going on. Have not u trained the model perfectly
2 hours to go my interview...hope this work.
This is really helpful for preparing and facing the job interviews. Thank you for the video.
Have Ml interview tomorrow, great help, thx
Very helpful and interesting video and covers almost all the areas with clear explanations. Thank you so much for the efforts
amazing explaination all the best sir ji ......
Thank You, instructor, for this awesome video. You explained very thing in quite simple and easily understandable manner
Hey, we are glad you loved the video. Do subscribe to the channel and hit the bell icon to never miss an update from us in the future. Cheers!
Much gratitude for this informative lecture
Recall Example is Really Superb....I will never forget that question in my Life.
All concepts are explained in very well manner.
We are super happy that Edureka is helping you learn better. Your support means a lot to us and it motivated us to create even better learning content and courses experience for you . Do subscribe the channel for more updates : ) Hit the bell icon to never miss an update from our channel : )
Excellent questions! Thanks
OMG!! Such awesome day to day life examples given for perfect understanding.. completely mesmerized 😊🙏
Hi : ) We really are glad to hear this ! Truly feels good that our team is delivering and making your learning easier :) Keep learning with us .Stay connected with our channel and team :) . Do subscribe the channel for more updates : ) Hit the bell icon to never miss an update from our channel : )
Thanks for this Video. Very neatly calmly Explained the major ML terms.
Really this helps, thanks much!
That did really helped me during my interview (:
Glad it was helpful!
Excellent thank you sir
Thanks very much.
Amazing help thanks edu😊😊😊
Thank you for the excellent video for ML.
Thank you so much for your review on our channel Great to hear that Edureka is helping you learn better . We’ll strive to make even better learning contents/courses in the future ! Do subscribe the channel for more updates : )
Awesome video, really made interesting and useful !! Thanks :)
This is awesome
Excellent video....
Thank you sooooooo much
This is a life safer
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Thank you!
nice explanation and good content for the realtime based questions
Thank you, Veera! We are glad you loved the video. Do subscribe, like and share to stay connected with us.
Loved d video
Very good video
Amazing!!!!
Good Work 😀
Thank u for this awesome video
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Thanks
thank you so much ..this is very helpful .can I get these questions answers in pdf form?
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good
I got the interview, I owe a thanks.💌
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Informative video
Glad you think so!
nice.
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Can you share ppt? Good video!!
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Thank you!
You are welcome👍