Hello guys, if you are preparing for Machine Learning Interviews and looking for common Machine Learning questions then you have come to the right place. Earlier, I have shared 35 Python Interview Questions and 20 AI Interview Questions and in this article, I am going to share 20 Machine Learning Interview Questions with answers. If you have gone through a Machine learning course or have worked in Machine Learning for a few months then you should know answer to all of these questions but if you struggle to answer them then I suggest you to revise concepts by going through these best Machine Learning courses for beginners. They are great to learn and revise important Machine Learning concepts.
Machine learning is the subset of artificial intelligence (AI) that focuses on building systems that learn or improve performance based on the data they consume. I thought I should remind you that.
Machine learning is the subset of artificial intelligence (AI) that focuses on building systems that learn or improve performance based on the data they consume. I thought I should remind you that.
From now henceforth you are not going to worry anymore about that machine learning interview that is waiting for you. You are going to be a winner because this article is going to prepare you well enough. The following questions are going to help you pass your machine learning interview.
20 Machine Learning Interview Questions with Answers
without wasting anymore of your time, here is a list of popular Machine Learning Interview questions for beginners and intermediate developers. I have tried to cover key Machine Learning concepts and terminology through these questions but if you think something is missing then feel free to suggest in comments. The goal is that you can use this list of Machine Learning questions to quickly revise key Machine learning concepts before interview.
1. What is machine learning?
Answer: machine learning is defined as the study in computer science that deals with making machines intelligent. A machine is only considered as intelligent if it can make its own decisions.
2. How is a machine learning model built?
Answer: machine learning model is built using the following steps:
Answer:
Answer:
Answer: this is a step-by-step process to define hardware and software requirements for machine learning model design.
without wasting anymore of your time, here is a list of popular Machine Learning Interview questions for beginners and intermediate developers. I have tried to cover key Machine Learning concepts and terminology through these questions but if you think something is missing then feel free to suggest in comments. The goal is that you can use this list of Machine Learning questions to quickly revise key Machine learning concepts before interview.
1. What is machine learning?
Answer: machine learning is defined as the study in computer science that deals with making machines intelligent. A machine is only considered as intelligent if it can make its own decisions.
2. How is a machine learning model built?
Answer: machine learning model is built using the following steps:
- Choose an algorithm for the model and train it.
- Test the model by using test data.
- Retrain the model if there are any changes and use the model for real-time projects.
Answer:
- Nearest Neighbor
- Decision Trees
- Neural Networks
- Support vector machines
- Probabilistic networks
Answer:
- Transduction
- Supervised Learning
- Unsupervised Learning
- Semi-supervised Learning
- Reinforcement Learning
- Learning to Learn
Answer: this is a step-by-step process to define hardware and software requirements for machine learning model design.
6. What are the steps involved in Machine Learning system design?
Answer:
- Gather requirements
- Identify the Metrics
- Architecture
- Serve the model to the users
Answer: Classification is used to produce discrete results and classify data into some specific categories while regression deals with continuous data.
8. What is Clustering in Machine Learning?
Answer: Clustering is defined as a technique used in unsupervised learning that involves grouping data points.
9. What is Linear Regression as used in Machine Learning?
Answer: Linear Regression is a supervised Machine Learning algorithm that is used to find the linear relationship between the dependent and independent variables for predictive analysis.
10. What is Epoch in Machine Learning?
Answer: in Machine Learning, Epoch is used to indicate the count of passes in a given training dataset where the Machine Learning algorithm has done its job.
11. What is Variance Inflation Factor?
Answer: Variance Inflation Factor (VIF) is the estimate of the volume of multicollinearity in collection of many regression variables.
12. What is Logistic Regression?
Answer: Logistic Regression is the proper regression analysis used when the dependent variable is categorical or binary. It is used to explain data and the relationship between one dependent binary variable and one or more independent variables.
13. What are test data and training data?
Answer: Training Data in Machine Learning is as important as a Machine Algorithm itself. As the name says, a training dataset is data to train the machine. The machine learns from the training data. The training data is labeled dataset. It means the output variable is mapped to one or more input variables. Test data is data used to check the accuracy of the machine. The machine output should have minimal error.
14. What is Lazy Learning?
Answer: Lazy Learning is a machine learning method where the data is not generalized until the query is made to it. In other words, such learning defers the processing until the request for information is received. An example of a Lazy learning technique is KNN, where the data is just stored. It is processed only when the query is made to it.
15. What is Backpropagation Technique?
Answer: The backpropagation Method is an artificial neural network training method for machine learning. It is an iterative process for the reduction of error and makes the artificial neural network model more reliable and accurate.
16. Explain the Confusion Matrix with Respect to Machine Learning Algorithms.
Answer: A confusion matrix (or error matrix) is a specific table that is used to measure the performance of an algorithm. It is mostly used in supervised learning; in unsupervised learning, it’s called the matching matrix. The confusion matrix has two parameters: Actual and Predicted. It also has identical sets of features in both of these dimensions.
17. When will you use Classification over Regression?
Answer: Classification is used when your target is categorical, while regression is used when your target variable is continuous. Both classification and regression belong to the category of supervised machine learning algorithms.
Examples of classification problems include:
- Predicting yes or no
- Estimating gender
- Breed of an animal
- Type of color
- Estimating sales and price of a product
- Predicting the score of a team
- Predicting the amount of rainfall
Answer: Principal Component Analysis or PCA is a multivariate statistical technique that is used for analyzing quantitative data. The objective of PCA is to reduce higher dimensional data to lower dimensions, remove noise, and extract crucial information such as features and attributes from large amounts of data.
19. How does the Support Vector Machine algorithm handle self-learning?
Answer: The SVM algorithm has a learning rate and expansion rate which takes care of self-learning. The learning rate compensates or penalizes the hyperplanes for making all the incorrect moves while the expansion rate handles finding the maximum separation area between different classes.
20. What are the assumptions you need to take before starting with linear regression?
Answer: There are primarily 5 assumptions for a Linear Regression model:
- Multivariate normality
- No auto-correlation
- Homoscedasticity
- Linear relationship
- No or little multicollinearity
Answer: Applications of supervised machine learning include:
- Email Spam Detection - Here we train the model using historical data that consists of emails categorized as spam or not spam. This labeled information is fed as input to the model.
- Healthcare Diagnosis - By providing images regarding a disease, a model can be trained to detect if a person is suffering from the disease or not.
- Sentiment Analysis - This refers to the process of using algorithms to mine documents and determine whether they’re positive, neutral, or negative in sentiment.
- Fraud Detection - By training the model to identify suspicious patterns, we can detect instances of possible fraud.
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