If you’re looking to build a career in machine learning, you must be aware that a machine learning interview is a rigorous process. Candidates are asked machine learning interview questions from various aspects, be it technical or programming skills, or the knowledge of basic concepts. Before heading into the interview, it is important to prepare yourself for what is to come and equip you with the top machine learning interview questions that will help you crack the interview. Machine learning interviews comprise of many rounds, which begin with a screening test. This comprises solving questions either on the white-board or solving it on online platforms like HackerRank, LeetCode etc. With the help of this blog, if you aspire to apply for machine learning jobs, it is crucial to know what kind of machine learning interview questions generally recruiters and hiring managers may ask.
Top Machine 5 Learning Interview Questions for Beginners
Question1. What are the different types of machine learning/ learning models?
Answer 1: ML models or machine learning can be classified into three main categories. They are Supervised Learning, Unsupervised Learning, and Reinforcement Learning. Let us understand these three types of learning in brief.
- Supervised Learning (Target is present): In the case of supervised learning, a machine uses labeled data to start learning. An existing data set is used to train the model before it starts making decisions with the help of new data. The target variable can be continuous or categorical. In case the target variable is continuous, the models can be polynomial regression models, quadratic regression, linear regression. In case the target variable is categorical, the models can be naive bayes, KNN, SVM, decision trees, gradient boosting, ADA boosting, bagging, logistic regression, random forest.
- Unsupervised Learning (Target is absent): In case a machine is trained on data that is unlabelled and does not have any proper guidance, it can be known as unsupervised learning. In the case of unsupervised learning, a pattern is automatically inferred by creating clusters. In this case, the machine learning model learns by observing and deducing the structures in the data. Examples of unsupervised learning are PCA or principal component analysis, singular value decomposition, factor analysis, etc.
- Reinforcement learning: In the case of reinforcement learning, a machine learning model follows a trial and error method. There is an agent available that interacts with the environment and creates actions that can then help in discovering the errors or rewards of the action performed.
Question 2. How can you select which variables are important while working with datasets?
Answer 2: If we wish to select which variables are important while working with datasets, there are various methods to do so. Let us take a look at them.
- You can select the variables based on the ‘p’ value in a linear regression model
- Lasso Regression
- You can follow the forward, backward and stepwise selection methods
- You can identify and discard the correlated variables before you finalize which variables are important
- Work on selecting the top features based on the information available for the set of features and variables
- Random forest and plot variable charts
Question 3. Is a higher variance in data good or bad?
Answer 3: High variance in data is not seen as good quality. This is because a higher variance means that the data is spread across a large area and the feature has a variety of data.
Question 4. What are the key differences between machine learning and deep learning?
In the case of machine learning, the algorithms typically learn from a pattern of data that is applied to decision making. In the case of deep learning, it learns on its own through processing data and is very similar to a human brain. Deep learning identifies, analyses, and makes a decision. If we talk about the key differences between machine learning and deep learning, they are:
- Machine Learning requires a more structured approach and structured data. Whereas, deep learning relies on layers of artificial neural networks.
- The way the data is presented to the system is different in machine learning and deep learning.
Question 5. How can we apply machine learning to hardware?
Answer 5: To apply machine learning to hardware, we will first have to build an ML algorithm in the system Verilog. System Verilog is a hardware development language. After this, we can program it into FPGA, thus allowing us to apply machine learning to hardware.
This brings us to the end of the blog on machine learning interview questions. We hope that you were able to gain a basic understanding of the types of questions that you can expect during the interview process and you are now better equipped to crack your interviews. Happy Learning!