Machine learning (ML) is a technological wonder of 2020. It is a byproduct of artificial intelligence. Machine learning allows systems to learn and improve automatically through experience. The systems do not need any specific program means machine learning projects can be performed by beginners with no voice knowledge of specific programming languages. To be precise, machine learning permits systems to access data and use it to enhance their knowledge.
Data analysts who work using artificial intelligence are in demand. Top companies offer a lucrative salary to people who are willing to work with machine learning. So, freshers are often curious to know what type of questions related to machine learning they will have to answer in job interviews. If they are aware of the kind of problems, they can prepare accordingly.
So, a comprehensive list of questions that candidates may face in machine learning interviews is given below. Their answers are provided, too, so that the potential candidates are ready for some intense discussions.
Top Machine Learning Interview Questions
1.What is Machine Learning?
Machine learning means the application of artificial intelligence which enables systems to learn automatically. It also allows improvement through the experience without any specific programming. It focuses on developing computer programs for accessing data and utilize it for self-learning.
2.What are the different types of machine learning?
There are three different types of machine learning:-
In supervised learning, the machine takes relevant decisions based on labeled data.
In unsupervised learning, the machine identifies patterns and discrepancies in the input data. It doesn’t have access to labeled data.
Reinforcement learning allows machines to learn from the rewards it received for earlier actions.
3.What is overfitting? How can it be avoided?
Sometimes, the machine picks up the training set better than required.Then, it takes up the random fluctuating of the training data as concepts. It affects the capability of the model to generalize. So, it does not apply to new data. When a model gets training data, it is shown as 100% correct. However, when the users use test data. It may be less ineffectiveness. It is known as overfitting.
We may avoid overfitting mainly 2 ways mentioned below:
Simplification: We need to prepare a simple model. The variance may be lessened when we use lesser variables as well as parameters.
Regularization: Overfitting has a cost term for features that are involved with objective functions. In case specific model parameters cause overfitting, methods like Lasso might be used to penalize the settings.
4.What do you mean by “test set “and “training set” in a machine learning model?
We follow three steps to create a model:
- Give training to the model
- Test it
- Utilize the model
|Test Set||Training Set|
|The test set is used to test the accuracy the hypothesis generated by the model|| The training set is examples |
given to the model to analyze and learn
|Remaining 30% is taken as testing |
|70% of the total data is typically taken as the training dataset|
|We test without labeled data and then verify results with labels||This is labeled data used to train the model|
5.How can someone handle data which is corrupted or missing data in a dataset?
The most convenient way to handle corrupted or missing data is to remove the columns and rows. Then, they may be replaced with a different value.
6.How can a person choose a classifier based on a training set data size?
If the training set is tiny, a model with low variance and correct bias work better. They have lesser chances of overfitting. Such models also perform better with complicated relationships.
7.Could you explain the confusion matrix concerning machine learning algorithms?
A particular table is used to check the performance of an algorithm. This table is known as the confusion matrix. It is also known as an error matrix. We find it mostly in supervised learning. In unsupervised learning, the confusion matrix is known as the matching matrix. It has two parameters known as actual and predicted.
8.What do you mean by deep learning?
A particular type of machine learning involves systems that use artificial neural networks to think and learn. This specific type of machine learning is also known as deep learning.We use the term “deep” for it because it gives us different coatings of neural networks mainly perceptron which is the part of deep learning .
The main difference is that machine learning allows feature learning to be done manually. In the case of deep learning, the model’s neural networks automatically decide which features need to be used.
9.In the case of machine learning, what are the three stages of building a model?
A machine-building model has three stages. They are as follows:
- Building a model: A suitable algorithm is chosen for the model. It is trained as per the requirement.
- Testing the model: Test data needs to be used for checking the accuracy of the model.
- Applying model: After the necessary model is tested, it becomes easy to make the required changes. The final model is used for real-time projects.
10.What do you mean by a false positive and false negative?
Some cases are mistakenly classified as valid. They are false. Such cases are known as false positives.
In the confusion matrix, the word “positive” means the “yes” row of the predicted value. The term “False positive” means that its real value is negative. However, the system has identified its value as positive.
On the other hand, some cases are classified as “False” by mistake. They are true. Such cases are known as “False negative” The word “Negative” refers to the “no” column in the confusion matrix. The full term of “false negative” means the actual value of the case is positive. However, the system has identified it as harmful.
11.How can you differentiate between machine learning and deep learning?
|Machine Learning||Deep Learning|
|Enables machines to take decisions on their own, based on past data||Enables machines to take decisions with the help of artificial neural networks|
|It needs only a small amount of data for training||It needs a large amount of training data|
|Works well on the low-end system, so you don’t need large machines||Needs high-end machines because it requires a lot of computing power|
|Most features need to be identified in advance and manually coded||The machine learns the features from the data it is provided|
|The problem is divided into two parts and solved individually and then combined||The problem is solved in an end-to-end manner|
12.What do you mean by the term semi-supervised in machine learning?
There are certain situations when the training data has an excellent quantity of unlabeled data and a lesser quantity of labeled data. It is known as semi-supervised learning.
13.Could you point out specific techniques for unsupervised machine learning?
Unsupervised learning has two techniques: association and clustering.These two techniques need to be explained in detail.
Association: Here, we recognize the patterns of association among the different variables. E.g., Some people frequently shop through e-commerce sites. When the regular customers log in to the e-commerce site, it shows them articles based on their previous shopping list or their wishlist.
Clustering: It divides data to be divided into different subsets. These subsets are also known as clusters. They have data that are similar to each other. Different groups express different information about the object in question.
14.How can you differentiate between supervised and unsupervised machine learning?
Supervised learning gathers information through labeled data. Based on such information it has collected, it makes a future prophesy as the output, based on the labeled data.
Unsupervised learning, the model acquires information through unlabeled input data. Then, it permits the algorithm to take steps according to the report without any instruction.
15.How will you differentiate between inductive machine learning and deductive machine learning?
Inductive learning follows occurrences based on principles that are well-defined to conclude. For example, we show a video of the fire, causing some damage to a child. Our purpose is to make the child understand why he or she needs to avoid burning through the video.
Deductive learning concludes experiences. For example, The parents allow a child to play with fire. In case the child gets burnt, he learns how dangerous it is. So, he never plays with light in the future.
16.How do you compare K-means with KNN algorithms?
K-means is unsupervised. KNN is supervised. The points in each cluster of K-means are similar to each other. Each of the groups differs from the clusters near it. KNN classifies all unlabeled observations based on its K.
17.What do you mean by naïve in the Naïve Byers classification?
The Naïve-Byers classification is known as naïve because it assumes whether it is correct or not.The algorithm assumes that the presence of one class feature is not linked to the presence of some other function, given the class variable. E.g., A fruit might be considered an orange based on its color and shape, without any regard for the other features.
18.How can a system play chess using reinforcement learning?
Reinforcement has an agent that performs some actions for achieving a particular goal. It gets rewarded each time it does something to progress towards the goal. Each time it does something that takes it away from the target, it gets penalized. The agent learns while playing the game. So, specific rules are not required here. It makes a move. This move is the decision. Then, it checks whether it is the right move. This way, it gets feedback. It memorizes this feedback before taking the next step. This memorization is its learning. It gets rewarded for the right move and punished for wrong moves.
19.How can you decide which machine learning algorithm to choose for the classification problem?
We cannot apply a fixed machine learning algorithm to solve a classification problem.However, different guidelines help us choose the classification problem.
Different algorithms may be tested and cross-validated for accuracy. Models with high bias and low variance may be chosen in case of a small training dataset. On the other hand, models with little inclination and high variance may be used in the extensive training dataset.
20.What do you mean by variance and bias in a machine learning model?
Often, the predicted values in a model are very different from actual costs. This difference is when bias occurs. The amount to which the target model changes when trained with different training data is known as a variance. The variance needs to be minimum in case of a good model.
Machine Learning Applications Based Questions
21.How can Amazon Able to recommend other things to buy? How does the recommendation engine work here?
Amazon stores the purchase data of regular customers for future reference. It helps Amazon find related products for the customer with the help of an association algorithm. This association algorithm identifies the patterns of a given dataset.
22.How will you design a spam filter for an email?
There is a specific procedure for designing a spam filter of an email. The process is given below:
- Many emails are recorded in the email spam filter.
- Each of them is labeled spam or not spam.
- The algorithm for supervised machine learning uses specific keywords like the lottery, full refund, or no money to determine which type of emails are marked spam.
- The next time the user gets an email, the spam filter uses algorithms like a decision tree to decide whether it is spam
- It is used as accurately as possible.
- The spam emails won’t enter the inbox
23.What do you mean by the Random forest?
“Random Forest” is used toclassify problems. It comes under a supervised machine learning algorithm. During the training phase, it constructs many decision trees. “Random Forest” upholds the decision taken by the majority of the trees as the final decision.
24.If you get a long list of machine learning algorithms, how will you decide which of them to use?
We cannot use a commonplace algorithm for all the situations. We need to ask a few questions to choose the correct algorithm. The items are as follows:
- How much data is available?
- Is the information continuous or categorical?
- What is the goal of the algorithm?
25.What are precision and recall?
Precision is the ratio of many events that may be recalled to the total number of games that can be remembered.It is a blend of right and wrong recalls. The recall is the ratio of the number of events that may be recalled to total games.
26.What do you mean by the classification of the decision tree?
A decision tree may handle numerical data as well as categorical data. The decision tree builds classification models like a tree structure. In the case of a decision tree classification, the datasets are broken into even tinier subsets. They form a structure resembling a tree, which has nodes and branches.
27.What do you mean by pruning in decision tree?
A technique that decreases the size of a decision tree is known as pruning. It makes the final classifier less complicated. As a result, it reduces overfitting. As a result, predictive accuracy is increased.
28.What is the difference between variance and bias?
Algorithms that have high variance, but low bias are used for training accurate, but inconsistent models. Algorithms that have a little variation, but a high inclination, train inaccurate, but consistent models.
29.Give a short explanation of Logistic regression?
Logistic regression is a classification algorithm used for predicting a binary result for a given group of independent variables.
30.What do you mean by reduced error pruning?
Reduced error pruning is an accepted pruning algorithm. It is a fast and straightforward version of pruning. It starts working at the leaves. Gradually, it replaces each node with its most favored class.
31.What do you mean by a recommendation system?
Basically, a recommendation system is used for filtering information. It predicts what the user might want to see or hear. This prediction is based on his or her choice patterns.
32. Discuss some methods for reducing dimensionality?
We may reduce dimensionality in several ways. They are as follows:
- Collinear features are removed
- Algorithmic dimensionality reduction is used.
33.Explain Kernel SVM?
The full form of Kernel SVM is the Kernel Support Vector Machine. They are a category of algorithms for explaining patterns.
34.Explain the algorithm named K nearest neighbor?
It is a classification algorithm. It works in a way that the new data point assigned to a neighboring group to which it is closest.
Conclusion : – Organizations dealing with machine learning ask the questions mentioned above frequently during interviews. However, the candidates have to keep themselves updated. Technology is not stagnant. As technology progresses, we may expect more development in the field of machine learning. So, the candidates have to update their knowledge as time advances.
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