Are you ready to take the machine learning world by storm? Then let’s start with nailing your interview! In this blog, we’ll help you unlock the secrets to crushing those tough machine learning questions. Whether you’re a seasoned pro or just starting out, we’ve got you covered. From popular algorithms to tricky evaluation metrics, we’ll break it down and give you the confidence to impress your interviewer. So, get ready to level up your machine learning game and stand out from the crowd

Machine Learning is one of the hottest in-demand job of 2022. According to Ambition box, Machine Learning Engineer salary in India ranges between ₹ 3.5 Lakhs to ₹ 22.0 Lakhs with an average annual salary of ₹ 7.5 Lakhs. Salary estimates are based on 2.3k salaries received from Machine Learning Engineers. Opportunities are immense in the high-paying field of machine learning and companies across different industries are now employing a candidate with relevant subject knowledge and expertise. We have tried to club some of the most important questions asked to a machine learning candidate in top companies. Remember apart from these mentioned machine learning interview questions you need to work on improving your Python skills as well. During a machine learning interview you will be given with a dataset with some problem statements to deal with.

Key Highlights:

Expert insights and guidance on the most common and challenging questions asked during a machine learning job interview.
Learn about a range of topics, from basic machine learning concepts to more advanced algorithms and techniques.
Understand popular evaluation metrics and how to communicate your ideas effectively and thought process during the interview.
Practical tips and strategies to help you showcase your skills and experience in the best possible light.
Get equipped with the knowledge and confidence to excel in your machine learning interview, whether you’re a beginner or an experienced practitioner.

Related blog – How to Become a Machine Learning Expert in 9 Months

Important Machine Learning skills based on Designation:

Job Designation	Important Machine Learning Skills
Data Analyst	Exploratory data analysis, statistical modeling (regression, clustering, decision trees), data visualization, SQL, Python/R programming, knowledge of data governance and security, understanding of big data and cloud computing
Machine Learning Engineer	Supervised and unsupervised learning algorithms, deep learning (CNNs, RNNs, GANs), data preprocessing, feature engineering, model selection and optimization, knowledge of software engineering principles, proficiency in programming languages (Python, Java, C++)
Data Scientist	Statistical modeling (linear regression, logistic regression, Bayesian statistics), machine learning algorithms (random forest, gradient boosting, neural networks), data preprocessing and feature engineering, data visualization, proficiency in programming languages (Python/R), knowledge of big data technologies (Hadoop, Spark, Hive)
AI Researcher	Natural language processing (NLP), computer vision (CNNs, object detection, segmentation), deep learning (GANs, autoencoders), reinforcement learning, probabilistic graphical models, proficiency in programming languages (Python, Java, C++), understanding of cloud computing and distributed systems
Business Intelligence Analyst	Data analysis and visualization, business acumen, storytelling, data warehousing and data mining, SQL, proficiency in data visualization tools (Tableau, Power BI), knowledge of data governance and security, understanding of machine learning concepts
Data Engineer	Data warehousing and ETL, distributed computing (Hadoop, Spark), database management, SQL, data pipeline design and management, proficiency in programming languages (Python, Java, Scala), knowledge of big data technologies (Hive, Pig, Impala)

Must Read – Top 10 Machine Learning Projects for Beginners

Machine Learning Scenario Based Interview Questions

Scenario 1: Building a Recommendation System for E-commerce Platform

You are working for a company that is interested in implementing a recommendation system for its e-commerce platform. The goal is to suggest personalized products to users based on their browsing and purchasing history. The company has collected data on user behavior, including purchase history, search queries, and items added to cart.

Questions Based on the Above Scenario:

What type of recommendation algorithm would you suggest for this scenario and why?
How would you approach the data collection process for building a recommendation system?
Can you explain the concept of collaborative filtering and how it can be used in recommendation systems?
What are the limitations of collaborative filtering and how can you overcome them?
How would you evaluate the performance of a recommendation system and what metrics would you use?
Can you describe the process of feature engineering and how it can be applied to this scenario?
How would you handle cold-start problem in recommendation systems?
What is the difference between content-based and collaborative filtering approaches in recommendation systems?
How would you address the issue of scalability in building a recommendation system for a large e-commerce platform?
How would you use deep learning techniques such as neural networks in building a recommendation system and what benefits do they offer over traditional approaches?

Q. What type of Recommendation Algorithm would you suggest for this scenario and why?

I would recommend using a hybrid approach that combines both collaborative filtering and content-based filtering algorithms.

Collaborative filtering would be effective in identifying similarities and patterns between users based on their browsing and purchasing behavior. However, it may not be enough to provide accurate recommendations for new or infrequent users who do not have a substantial browsing and purchasing history.

To address this issue, a content-based filtering algorithm could be used in combination with collaborative filtering. This approach would take into account the attributes and characteristics of the products, such as the brand, color, style, and material, and recommend products based on the user’s previous purchases and interests.

By combining these two approaches, the recommendation system can provide accurate and personalized recommendations to both frequent and new users, which can improve customer engagement, increase sales, and enhance the user experience on the e-commerce platform.

Q. How would you approach the data collection process for building a recommendation system?

To build an effective recommendation system, it’s essential to collect relevant and accurate data on user behavior. Here’s how I would approach the data collection process for this scenario:

Identify the data sources: The first step is to identify the data sources available for collecting user behavior data, such as the company’s website, mobile app, or social media pages.
Define the data schema: Once the data sources have been identified, it’s important to define the data schema, which outlines the types of data to be collected and how it will be structured. This includes data on user demographics, purchase history, search queries, and items added to the shopping cart.
Implement data collection mechanisms: Next, data collection mechanisms should be implemented to capture the user behavior data. This can include using tracking pixels, cookies, or APIs to track user activity on the website or app.
Store the data: The collected data should be stored in a secure database or data warehouse that is designed to handle large amounts of data. The data should be regularly backed up and tested to ensure its integrity.
Clean and preprocess the data: The collected data should be cleaned and preprocessed to remove any duplicate or irrelevant data. The data should also be standardized and normalized to ensure consistency.

Q. Can you explain the concept of collaborative filtering and how it can be used in recommendation systems?

Collaborative filtering is a popular technique used in recommendation systems to generate personalized recommendations for users based on their similarities with other users.

The basic idea behind collaborative filtering is to find users who have similar tastes and preferences, and to recommend items that those similar users have liked or purchased. For example, if User A and User B have similar browsing and purchase history, and User A has purchased a product that User B has not yet bought, the system will recommend that product to User B.

Collaborative filtering can be done in two ways:

User-based Collaborative Filtering: This approach focuses on finding similar users based on their behavior, preferences, and purchase history. The system looks for users who have similar browsing and purchase behavior and recommends items that those similar users have liked or purchased. This approach can be computationally expensive, as it requires calculating the similarity between every user in the system.
Item-based Collaborative Filtering: This approach focuses on finding similar items based on their characteristics, such as price, brand, or style. The system looks for items that are frequently purchased or liked together, and recommends those items to users who have shown an interest in one of them. This approach is faster than user-based collaborative filtering, as it requires calculating the similarity between only the items in the system.

Both approaches have their advantages and disadvantages, and a hybrid approach that combines both can be used for better accuracy in recommendations.

Collaborative filtering is an effective technique for generating personalized recommendations for users. It is based on the idea that users with similar tastes and preferences will have similar behavior and will like similar items. Collaborative filtering can be used in many different types of recommendation systems, including e-commerce, social media, and entertainment.

Q. What are the limitations of collaborative filtering and how can you overcome them?

While collaborative filtering is a powerful technique for generating personalized recommendations, it also has some limitations. Some of the limitations of collaborative filtering are:

Cold start problem: Collaborative filtering requires a sufficient amount of user data to generate accurate recommendations. In the case of new users or items, the system may not have enough data to generate accurate recommendations.
Sparsity problem: Collaborative filtering can suffer from sparsity, where there are too many items and too few ratings, making it difficult to find similar items or users.
Popularity bias: Collaborative filtering may recommend popular items more frequently, even if they may not be relevant to the user’s interests.

To overcome these limitations, various techniques can be used in the recommendation system:

Content-based filtering: In addition to collaborative filtering, content-based filtering can be used to generate recommendations based on item characteristics, such as product descriptions or reviews. This approach can help overcome the cold start problem, as the system can use item characteristics to generate recommendations for new items.
Matrix factorization: Matrix factorization techniques can be used to reduce the sparsity problem by decomposing the user-item matrix into lower dimensional latent factors and identifying hidden patterns in the data.
Hybrid recommendation systems: A hybrid recommendation system that combines collaborative filtering with other techniques, such as content-based filtering or popularity-based filtering, can help overcome the limitations of each technique.

In our scenario, a possible example of overcoming the limitations of collaborative filtering would be to use a hybrid recommendation system that combines collaborative filtering with content-based filtering. The system can use collaborative filtering to generate personalized recommendations based on the user’s behavior and preferences, and content-based filtering to generate recommendations for new items or items with few ratings. Additionally, matrix factorization techniques can be used to reduce the sparsity problem and identify hidden patterns in the data. By combining multiple techniques, the recommendation system can generate more accurate and relevant recommendations for users.

Also read: Machine Learning Courses on Shiksha Online

Q. How would you evaluate the performance of a Recommendation System and What Metrics would you use?

Evaluating the performance of a recommendation system is essential to ensure that it is generating accurate and relevant recommendations for users. There are several metrics that can be used to evaluate the performance of a recommendation system, depending on the type of recommendation system and the business goals of the company. Some of the commonly used metrics are:

Precision: Precision is the proportion of recommended items that are relevant to the user’s interests. A higher precision indicates that the system is generating more accurate recommendations.
Recall: Recall is the proportion of relevant items that are recommended to the user. A higher recall indicates that the system is recommending more relevant items to the user.
F1 score: F1 score is the harmonic mean of precision and recall and provides a balance between the two metrics. A higher F1 score indicates that the system is generating both accurate and relevant recommendations.
Mean Average Precision (MAP): MAP measures the average precision across all the recommended items for a user. A higher MAP indicates that the system is generating more accurate recommendations for users.
Normalized Discounted Cumulative Gain (NDCG): NDCG measures the relevance of the recommended items by taking into account the position of the relevant items in the recommendation list. A higher NDCG indicates that the system is recommending more relevant items at the top of the list.
Click-through rate (CTR): CTR measures the proportion of users who clicked on the recommended items. A higher CTR indicates that the system is recommending more relevant items to the users.

To evaluate the performance of a recommendation system, a combination of these metrics can be used. The evaluation can be done using a hold-out or cross-validation approach, where a portion of the data is used for training the model and the rest is used for evaluating the performance. The performance can be measured for different groups of users, such as new users, active users, or inactive users, to identify any performance differences across the user groups.

Q. Can you describe the process of feature engineering and how it can be applied to this scenario?

Feature engineering is the process of selecting and transforming relevant data features to improve the performance of a machine learning model. In the context of a recommendation system, feature engineering involves selecting and transforming user and item features to better capture their preferences and characteristics. Some common techniques used in feature engineering include:

One-hot encoding: This technique is used to encode categorical features, such as product categories or user demographics, into binary features. Each category is represented as a binary variable, which can then be used as input to the model.
Feature scaling: Feature scaling is used to normalize numerical features, such as product prices or user ratings, to a common scale. This is important because some machine learning algorithms may be sensitive to the scale of the features.
Text processing: Text processing techniques can be used to extract meaningful features from text data, such as product descriptions or user reviews. This can include techniques such as tokenization, stemming, and sentiment analysis.
Dimensionality reduction: Dimensionality reduction techniques, such as principal component analysis or t-SNE, can be used to reduce the dimensionality of the feature space, which can improve the performance of the model and reduce computational complexity.

In the given scenario, feature engineering can be applied in several ways. For example:

User and item profiles: User and item profiles can be created by aggregating and summarizing user and item features, such as purchase history, search queries, and item attributes. These profiles can then be used as input to the recommendation algorithm.
User preferences: User preferences can be extracted from user behavior data, such as clicks, views, and purchases, using techniques such as one-hot encoding or feature scaling. This can help the recommendation system better understand user preferences and generate more relevant recommendations.
Item attributes: Item attributes, such as product descriptions, categories, and tags, can be processed using text processing techniques to extract meaningful features. This can help the recommendation system identify relevant items based on their characteristics.
Dimensionality reduction: Dimensionality reduction techniques can be used to reduce the dimensionality of the user-item feature space, which can improve the performance of the model and reduce computational complexity.

By applying feature engineering techniques, the recommendation system can better capture user and item characteristics, and generate more accurate and relevant recommendations for users.

Q. How would you handle cold-start problem in Recommendation Systems?

The cold-start problem in recommendation systems refers to the challenge of generating personalized recommendations for new users or items with limited or no historical data. There are several techniques that can be used to handle this problem, including:

Content-based recommendation: Content-based recommendation relies on the features of items, such as item descriptions or attributes, to generate recommendations. This approach can be used for new items without any historical data, by relying on their features to identify similar items that the user might like.
Popularity-based recommendation: Popularity-based recommendation is based on the idea that popular items are likely to be of interest to new users. This approach can be used for new users without any historical data, by recommending popular items to them.
Hybrid recommendation: Hybrid recommendation combines multiple recommendation techniques, such as collaborative filtering and content-based recommendation, to generate more accurate and diverse recommendations. This approach can be used for new users or items with limited historical data, by incorporating additional features or information to improve the recommendations.
Incentivizing user engagement: To gather more user data and reduce the cold-start problem, recommendation systems can incentivize users to provide more feedback and engage more with the platform. This can include offering rewards for providing feedback or sharing user-generated content.

By using these techniques, recommendation systems can handle the cold-start problem and provide more personalized and relevant recommendations for new users or items.

Q. What are some of the most commonly used Machine Learning algorithms?

Ans. Most commonly used machine learning algorithms based on supervised and unsupervised machine learning are:

You may like – Top 10 Machine Learning Algorithms for Beginners

Q2. How Do You Handle Missing or Corrupted Data in a Dataset?

Ans: Method of dealing with missing data is completely scenario based. Same method cannot be applied to all the datasets. One of the easiest ways to deal with handling missing or corrupted data in a dataset is by simply dropping the row. But simply dropping the multiple rows of data might result in error if the size of data is low.

There are two useful methods in Pandas:

IsNull() and dropna() will help to find the columns/rows with missing data and drop them
Fillna() will replace the wrong values with a fixed value. You can even replace the dataset with the mean value or simply 0

Q3. What are different types of Machine Learning?

Ans: Machine Learning is broadly categorized in four types:

Supervised Learning
Unsupervised Learning
Semi-Supervised Learning
Reinforcement Learning

Supervised Learning: In this type of machine learning the model is trained on the labelled dataset for classification and regression based problem. Some of the algorithm which are a part of supervised learning are Linear Regression, Logistic Regression, Decision Tree, Random Forest, Naive Bayes.

Unsupervised Learning: In this type of machine learning the model is trained for finding patterns, anomalies and clusters in unlabeled dataset. Some of the algorithm which are a part of unsupervised learning are K-Means, C-Means, Hierarchical Clustering.

Semi Supervised Learning: In this type of machine learning the model is trained using both labelled and unlabeled datasets.

Reinforcement Learning: In this type of machine learning the model is left to train on their own using the concept of rewards and penalty. In simple words, there’s an agent with a task to completed with rewards, penalties and many hurdles in between.

Q4. What is Overfitting and how to avoid it?

Overfitting is a situation which occurs when the model learns the training dataset too well. An overfit model will give high accuracy in case of training dataset even 100% accuracy, but the same model will result in low accuracy when it is applied to a new dataset. High accuracy on training data and low accuracy on validation data or new data is the case of a overfit model.

Why does Overfitting occurs?

Size of Training data is too low

Low size of training dataset results in overfitting condition. Model learns each and every data points when the size of training data is too low. In this case the error will be negligible when the model is trained on training data, but when tested on a new data the error rate of the model will be high and the predictions made would be incorrect.

Model tries to make predictions on Noisy Data

Overfitting also occurs when the model tries to make predictions on data that is very noisy, which is caused due to an overly complex model having too many parameters. So, due to this, the overfitted model is inaccurate as the trend does not reflect the reality present in the data.

Different ways to deal with the overfitting condition:

Train with more data: Increase the data that you are using to train your model. Low data will mostly result in an overfitting condition.
Data Augmentation: Data augmentation makes a sample data look slightly different every time the model processes it.
Cross Validation: Cross-validation is a powerful measure which can help us deal with overfitting. The initial training data is used to generate multiple mini train-test splits. Use these splits to tune your model.

Q5. What do you understand by ensemble learning?

Ans: Ensemble learning is a machine learning technique that is used to combine different machine learning base models using the concept of bagging and boosting to improve the accuracy of the model.

Q6. What are the different stages of building a model in Machine Learning?

Ans: Different phases of machine learning are:

S.No	Stages of Machine Learning	Python Libraries for each stage
1	Data Acquisition	Beautiful Soup, Selenium, Scrapy, Tweepy, PySQL
2	Data Cleaning	Pandas, Dora, Arrow, Scrubadub, Missingno, Dabl, spacy, NLTK
3	Data Manipulation	Modin, Pandas, Pandas-Profiling, Dask, Polars, Pyspark, featuretools. AutoFeat
4	Data Visualization	Matplotlib, Plotly, Seaborn, Sweetviz, Autoviz
5	Building Machine Learning Models	Scikit-Learn, Pytorch, Tensorflow, Pyspark, MLlib, Weka, Knime, Prophet, MLflow, H2O, Autosklearn, OpenCV, spacy, NLTK, detectron, yolo
6	Model Optimisation	HyperOpt, Optuna
7	Model Deployment	Heroku, Streamlit, Flask, Django, AWS Sagemaker

Q7. What is Cost Function?

Ans: Cost function or loss function is an important parameter which tells us how well a model is performing. The main agenda while training a model is to optimize the cost function. It is the measure of how wrong the model is in estimating the relationship between X(input) and Y(output) Parameter.

Q8. What is Linear Regression in Machine Learning

Ans: Linear Regression is a supervised machine learning algorithm which is trained on labelled dataset and is used to predict continuous data.

Linear Regression models a finds a linear relationship between continuous independent variable (x) and dependent variables (Y). The relationship between the dependent and independent variable is figured out using a straight line equation of Y = mx + c, where m is the slope of line and c is the intercept.

Also Read – Difference between simple linear regression and multiple linear regression

Q9. Name the paradigms of ensemble methods.

Ans: There are two paradigms of ensemble methods, which are –

Bagging
Boosting

To learn about data science, read our blog on – What is data science?

Q10. What is Regularization?

Ans: Regularization is a technique to improve the validation score. Most of the time, it is achieved by reducing the training score.

Q11. What are the full forms of PCA, KPCA, and ICA, and what is their use?

Ans: PCA – Principal Components Analysis

KPCA – Kernel-based Principal Component Analysis

ICA – Independent Component Analysis

These are important feature extraction techniques, which are majorly used for dimensionality reduction.

Q12. Name the components of relational evaluation techniques.

Ans: The main components of relational evaluation techniques are –

Data Acquisition
Ground Truth Acquisition
Cross-Validation Technique
Query Type
Scoring Metric
Significance Test

Q13. What is a Confusion Matrix?

Ans: A confusion matrix is a summary of correct and incorrect predictions and helps visualize the outcomes. Confusion Matrix is a simple technique for checking the performance of a classification model for a given set of test data.

Confusion matrix has two most important parameters: Actual and Predicted values

Let’s understand TP, FP, FN, TN in terms of Coronavirus affected people analogy.

True Positive: Actual values is positive and is correctly predicted.

True Negative: Actual value is negative and it is correctly predicted.

False Negative: Actual value is negative and is incorrectly predicted (Type 2 Error)
False Positive: Actual value is positive and is incorrectly predicted

Q14. What is a ROC curve?

Ans: It is a Receiver Operating Characteristic (ROC curve), a fundamental tool for diagnostic test evaluation. ROC curve is a plot of Sensitivity against Specificity for probable cut-off points of a diagnostic test. It is the graphical representation of the contrast between true positive rates and the false positive rate at different thresholds.

Read more: Difference between ROC and AUC

Q15. Can you name some libraries in Python used for Data Analysis and Scientific Computations?

Ans: Python is among the most discussed topics in machine learning interview questions.

Some of the key Python libraries used in Data Analysis include –

Bokeh
Matplotlib
NumPy
Pandas
SciKit
SciPy
Seaborn

Interesting read – Powerful Python Libraries for Data Science and Machine Learning

Q16. What is the difference between supervised and unsupervised machine learning.

Ans: Supervised learning is all about training labeled data for tasks like data classification, while unsupervised learning does not require explicitly labeling data.

Supervised Learning	Unsupervised Learning
The input data is labeled.	The input data is not labeled.
The data is classified based on the training dataset.	Assigns properties of the given data to categorize it.
Supervised algorithms have a training phase to learn the mapping between input and output.	Unsupervised algorithms have no training phase.
Used for prediction problems.	Used for detecting anomalies and clusters in the dataset.
Supervised algorithms include Classification and Regression.	Unsupervised algorithms include Clustering and Association.
Algorithms used – Linear Regression, Logistic Regression, Decision Tree, Random Forest, etc.	Algorithms used – K-Means, C- Means, Hierarchical Clustering, etc.

Q17. Name different methods to solve Sequential Supervised Learning problems.

Ans: Some of the most popular methods to solve Sequential Supervised Learning problems include –

Sliding-window methods
Recurrent sliding windows
Hidden Markov models
Maximum entropy Markov models
Conditional random fields
Graph transformer networks

Q18. What is the use of Box-Cox transformation?

Ans: The Box-Cox transformation is a generalized “power transformation” that ensures normal data transformation and distribution. It is used to eliminate heteroscedasticity.

Q19. What is a Fourier transform?

Ans: It is a generic method to breaks a waveform into an alternate representation, mainly characterized by sine and cosines.

Q20. What is PAC Learning?

Ans: It is an abbreviation for Probably Approximately Correct. This learning framework analyzes learning algorithms and statistical efficiency.

Q21. What are the different machine learning approaches?

Ans. The different machine learning approaches are –

Concept Vs. Classification Learning
Symbolic Vs. Statistical Learning
Inductive Vs. Analytical Learning

Q22. What is Gradient Descent?

Ans: Gradient Descent is a popular algorithm used for training Machine Learning models. It is also used to find the values of parameters of a function (f) to minimize a cost function.

Q23. What is a Hash Table?

Ans: A Hash Table is a data structure that produces an associative array, and is used for database indexing.

Q24. What is the difference between Causation and Correlation?

Ans: Causation denotes any causal relationship between two events and represents its cause and effects.
Correlation determines the relationship between two or more variables.
Causation necessarily denotes the presence of correlation, but correlation does not necessarily denote causation.

Q25. What is the difference between a Validation Set and a Test Set?

Ans: The validation set is used to minimize overfitting. This is used in parameter selection, which means that it helps to verify any accuracy improvement over the training data set. Test Set is used to test and evaluate the performance of a trained Machine Learning model.

Q26. What is a Boltzmann Machine?

Ans: Boltzmann Machines have a simple learning algorithm that helps to discover exciting features in training data. These were among the first neural networks to learn internal representations and are capable of solving severe combinatory problems.

Q27. What are Recommender Systems?

Ans: Recommender systems are information filtering systems that predict which products will attract customers, but these systems are not ideal for every business situation. These systems are used in movies, news, research articles, products, etc. These systems are content and collaborative filtering-based.

Q28. What is Deep Learning?

Ans: Deep Learning is an artificial intelligence function used in decision-making. It is among the most important functions of machine learning and among the most commonly asked machine learning interview questions.

Deep Learning imitates the human brain’s functioning to process the data and create the patterns used in decision-making. Deep learning is a key technology behind automated driving, automated machine translation, automated game playing, object classification in photographs, and automated handwriting generation, among others.

Learn more – What is Deep Learning?

Q29. What are imbalanced datasets?

Ans: Imbalanced datasets refer to the different numbers of data points available for different classes.

Q30. How would you handle imbalanced datasets?

Ans: We can handle imbalanced datasets in the following ways –

Oversampling/Undersampling – We can use oversampling or undersampling instead of sampling with a uniform distribution from the training dataset. This will help to see a more balanced dataset.

Data augmentation – We can modify the existing data in a controlled way by adding data in the less frequent categories.

Use of appropriate metrics – Usage of metrics like precision, recall, and F-score can help to describe the model accuracy in a better way if an imbalanced dataset is being used.

Q31. What is Pattern Recognition?

Ans: Pattern recognition is the process of data classification by recognizing patterns and data regularities. This methodology involves the use of machine learning algorithms.

Q32. Where can you use Pattern Recognition?

Ans: Pattern Recognition can be used in

Bio-Informatics
Computer Vision
Data Mining
Informal Retrieval
Statistics
Speech Recognition

Explore Deep Learning and Neural Networks Online Courses

Q33. What is Data augmentation? Can you give an example?

Ans: Data augmentation is a machine learning strategy that enables the users to increase the data diversity for training models remarkably from internal and external sources within an enterprise. This does not require any new data collection.

Modification in images is one of the most helpful examples of data augmentation. We can easily perform the following activities on an image and modify it –

Resizing the image
Flipping it horizontally or vertically
Adding noise
Deforming
Modifying colors

Q34. Mention the differences between Type I and Type II errors.

Ans: The most significant differences between Type I and Type II errors are –

Type I Error	Type II Error
False-positive error	False-negative error
Claims something when nothing has happened	Claims nothing when something has happened
It is the probability of rejecting a true null hypothesis	It is the probability of failing to reject a false null hypothesis

Q35. How will you perform static analysis in a Python application?

Ans: PyChecker can be helpful as a static analyzer to identify the bugs in the Python project. This also helps to find out the complexity-related bugs. Pylint is another tool that is helpful in checking if the Python module is at par with the coding standards.

Learn more about Python

Q36. What is Genetic Programming?

Ans: Genetic Programming is a type of Evolutionary Algorithm (EA). It can be used to solve problems across different fields, including optimization, automatic programming, and machine learning. Genetic Programming is inspired by biological evolution. This system implements algorithms that use random mutation, crossover, fitness functions, and multiple generations of evolution, which altogether contribute to solving user-defined tasks.

Q37. What are the different types of Genetic Programming?

Ans: Different types of Genetic Programming are –

Cartesian Genetic Programming (CGP)
Extended Compact Genetic Programming (ECGP)
Genetic Improvement of Software for Multiple Objectives (GISMO)
Grammatical Evolution
Linear Genetic Programming (LGP)
Probabilistic Incremental Program Evolution (PIPE)
Stack-based Genetic Programming
Strongly Typed Genetic Programming (STGP)
Tree-based Genetic Programming

Q38. What is the Model Selection?

Ans: It is one of the most important machine learning interview questions.

Model Selection refers to a process of selecting models from different mathematical models for describing the same data set. The model selection has its applications across various fields, including statistics, machine learning as well as data mining.

Q39. Which classification methods can be handled by Support Vector Machines?

Ans: SVMs can handle two classification methods –

Combining binary classifiers
Modifying binary to incorporate multiclass learning

Classification in Data Mining – A Beginner’s Guide

Q40. In how many groups can SVM models be classified?

Ans: SVM models are classified into four distinct groups:

Classification SVM Type 1 (also called C-SVM classification)
Classification SVM Type 2 (also called nu-SVM classification)
Regression SVM Type 1 (also called epsilon-SVM regression)
Regression SVM Type 2 (also called nu-SVM regression)

Q41. High variance in data – is it good or bad?

It is bad. Higher variance in the data suggests that the spread of data is bigger and the dataset is not presenting a very accurate or representative picture of the relationship between the inputs and predicted output.

Q42. If your dataset has the issue of high variance, how would you handle it?

Ans: We can use a bagging algorithm to handle the high variance in datasets. These algorithms split the data into subgroups with sampling replicated from random data. After the data is split, we can use random data to create rules using a training algorithm. We can then use the polling technique to combine all the predicted outcomes of the dataset.

Q43. What knowledge do you need to have to extract the predicted information from the raw data?

Ans: To extract the predicted information from the raw data, one must understand mathematics, statistics, computer science, machine learning, data visualization, cluster analysis, and data modelling.

Q44. What is logistic regression?

Ans. Logistic regression is a statistical technique used to predict a binary result that is zero or one, or a yes or a no.

Q45. Why is data cleansing important in data analysis?

Ans. Data is accumulated from a variety of sources. It is important to ensure that the data collected is good enough for analysis. Data cleaning or erasure ensures that data is complete and accurate, and does not contain redundant or irrelevant components.

Data Cleaning In Data Mining – Stages, Usage, and Importance

Q46. What does the A/B test aim to accomplish?

Ans. It is a statistical hypothesis test used to detect any changes to the website so that measures can be taken to maximize the possibility of the desired result.

Q47. Python or R – Which is the best for machine learning?

Ans. In machine learning projects, both R and Python come with their own advantages. However, Python is more useful in data manipulation and repetitive tasks, making it the right choice if you plan to build a digital product based on machine learning. Moreover, to develop a tool for ad-hoc analysis at an early stage of the project, R is more suitable.

Q48. What is TF / IDF vectorization?

Ans: TF-IDF stands for Reverse Document Frequency. It is a numerical statistic is used to determine the importance of a word in a document of a collection or corpus.

Q49. What are tensors?

Ans. Tensors are similar to matrices in programming languages, but here they are larger. Tensors can be considered as a generalization of matrices that form a matrix of n dimensions. TensorFlow provides methods that can be used to easily create tensor functions and calculate their derivatives. This is what distinguishes tensors from NumPy matrices.

Q50. What are the benefits of using TensorFlow?

Ans. TensorFlow has numerous advantages, which is why it is the most widely used framework for machine learning. Some of which include –

Platform independence
GPU use for distributed computing
Self-differentiation capacity
Open source and a great community
Highly customizable according to requirements
Support for asynchronous calculations

Must Read – Data Science Interview Questions and Answers

Q51. Are there any limitations to using TensorFlow?

Ans. Although TensorFlow offers numerous benefits, it has a caveat or two in current versions:

No support for OpenCL (Open Computing Language) yet
GPU memory conflicts when used with Theano
It can be overwhelming for beginners to start

Q52. Can we capture the correlation between continuous and categorical variables?

Ans: Yes, we can establish the correlation between continuous and categorical variables by using the Analysis of Covariance or ANCOVA technique. ANCOVA controls the effects of selected other continuous variables, which co-vary with the dependent.

Q53. What is selection bias?

Ans: A statistical error that leads to a bias in the sampling portion of an experiment is called selection bias. If the selection bias remains unidentified, it may lead to a wrong conclusion.

Q54. What is PCA? Why is it used?

Ans: Principal component analysis (PCA) is one of the most popular statistical analysis methods used in dimension reduction. PCA is mainly used to summarize the data structure while acquiring factors that are not correlated with each other.

Q55. Explain Features vs. Labels.

Ans. Features are the input information and are independent variables. Labels are the output information for a mode and are dependent variables. Features are one column of the data in your input set and are used in prediction. Labels are the information that gets predicted.

Q56. What is Bias in Machine Learning?

Ans. Data bias in machine learning is a type of error and suggests that there is some inconsistency in data. This error is usually an indication that certain elements of a dataset are more heavily weighted than others. The inconsistencies are not mutually exclusive.

Q56. What is an OOB error?

Ans. OOB or Out Of Bag (OOB) error is the average error for each calculated sample using predictions from the trees that do not contain in their respective bootstrap sample. OOB error is calculated to get an unbiased measure of the accuracy of the model over test data.

Hope this list of machine learning interview questions helps you to grab your next machine learning interview. All the best!

50+ Machine Learning Interview Questions and Answers