Machine Learning Interview Questions and Answers | Machine Learning Interview Preparation | Edureka



** Machine Learning Training with Python: https://www.edureka.co/data-science-python-certification-course **
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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About the Course

Edureka’s Machine Learning Course using Python is designed to make you grab the concepts of Machine Learning. The Machine Learning training will provide deep understanding of Machine Learning and its mechanism. As a Data Scientist, you will be learning the importance of Machine Learning and its implementation in python programming language. Furthermore, you will be taught Reinforcement Learning which in turn is an important aspect of Artificial Intelligence. You will be able to automate real life scenarios using Machine Learning Algorithms. Towards the end of the course, we will be discussing various practical use cases of Machine Learning in python programming language to enhance your learning experience.

After completing this Machine Learning Certification Training using Python, you should be able to:
Gain insight into the ‘Roles’ played by a Machine Learning Engineer
Automate data analysis using python
Describe Machine Learning
Work with real-time data
Learn tools and techniques for predictive modeling
Discuss Machine Learning algorithms and their implementation
Validate Machine Learning algorithms
Explain Time Series and it’s related concepts
Gain expertise to handle business in future, living the present

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Why learn Machine Learning with Python?

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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24 thoughts on “Machine Learning Interview Questions and Answers | Machine Learning Interview Preparation | Edureka”
  1. Got a question on the topic? Please share it in the comment section below and our experts will answer it for you. For Edureka Python Machine Learning Course curriculum, Visit our Website: http://bit.ly/2OpzQWw

  2. Recall Example is Really Superb….I will never forget that question in my Life.
    All concepts are explained in very well manner.

  3. OMG!! Such awesome day to day life examples given for perfect understanding.. completely mesmerized 😊🙏

  4. Very helpful and interesting video and covers almost all the areas with clear explanations. Thank you so much for the efforts

  5. This is really helpful for preparing and facing the job interviews. Thank you for the video.

  6. 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

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