Machine Learning (ML) is an important branch of Artificial Intelligence (AI) that aims at providing computers and computer-based systems with the ability to think and learn like human beings from their experience. This eliminates the need for any explicit external command to the system as there are intelligent algorithms to offer the cognitive ability to them. Though the use of machine learning technology is becoming prevalent in many fields, it is still in its preliminary stage of development and has a long way to go to reach anywhere near the human cognitive ability. Having said that, machine learning has surely contributed in making our lives easy, especially in the data management and analysis field. This field possesses an extensive scope for research and development, hence it offers many exciting and lucrative career opportunities for those aspiring.
If you are also seeking a career in this field and going to face a machine learning interview soon, the following top ten machine learning interview questions and answers would prove to be of great help:
Q1. How will you explain machine learning to a layperson in an easily comprehensible manner?
Ans. Machine learning is a kind of technology that enables the computer-based machines and systems to make decisions based on prior experience with an activity, with the intent of improving its performance continuously. This can be understood through multiple examples, such as:
- Imagine about a curious kid who sticks his palm
- You have observed that the obese people are more prone to heart diseases than the thinner people; thus, you decided that you will try to remain slim to prevent the risk of a heart disease. You have gone through a lot of information on this topic and then, come up with a general rule of classification.
- Suppose, you are playing blackjack and based on the sequence of the cards you see, you decide whether to hit or not. In this case, based on the prior experience you have and by looking at what happens, you decide on your course of action.
The same way the machines also lear with the aid of technology.
Q2. Differentiate between inductive and deductive machine learning.
Ans. In inductive machine learning, the model learns through examples obtained from a set of observed instances to draw generalized conclusions, whereas in deductive machine learning certain statements are combined in a logical order as per some predefined rules to obtain new statements. Basically, inductive learning is instruction based and deductive learning is experience based.
Q3. What do you mean by parametric models? Also, give some examples of them.
Ans. Parametric models are the models having a limited number of parameters. In order to predict new data, you only need to know the parameters of the model. The examples of such models include logistic regression, linear regression, and linear SVMs.
Q4. What is the “Curse of Dimensionality?”
Ans. The term, “Curse of Dimensionality” refers to the difficulty of searching through a space with multiple dimensions; more the dimensions, more the difficulty. If talk of this term, particularly in context of machine learning, it has to do with the difficulty associated with non-intuitive properties of data observed when working in a high-dimensional space.
Q5. Can you name some popular machine learning algorithms?
Ans. Yes, they are:
- Nearest Neighbour
- Neural Networks
- Decision Trees
- Support vector machines
Q6. How will you choose the most appropriate machine learning algorithm for your classification problem?
Ans. If accuracy has to be given priority in deciding a machine learning algorithm, then the best way to go about it is to test a couple of different algorithms (try different parameters within each algorithm ) and choose the one that best meets the requirement. As a rule of thumb, choose a machine learning algorithm for your classification based on the size of your training set. If the training set is small, then using low variance/high bias classifiers like Naïve Bayes is beneficial, while in the case of large training sets high variance/low bias classifiers like k-nearest would serve the purpose best.
Q7. What do you understand by decision tree classification?
Ans. Decision tree classification in machine learning refers to a tree-like classification model where the data is continuously split as per certain parameters. There are two primary entities in this model, namely decision nodes and leaves. The leaves denote the final outcome of decisions, while the nodes signify the point where the data is split. A decision tree classification greatly facilitates a visual and explicit representation of the decisions and decision making process.
Q8. What is a neural network and what are some advantages and disadvantages of such a network?
Ans. In the information technology field, a neural network is basically a system of hardware and/or software akin to the pattern of neurons in the human brain; it constitutes an important part of deep learning. The greatest advantage of neural networks is that they lead to the performance breakthroughs for unstructured datasets like audio, video, and images. Their high flexibility enables them to learn patterns that no other ML algorithm would be unable to manage. However, the disadvantage of neural networks is that need a huge volume of training data to work effectively. Also, there is difficulty in picking the right architecture for these networks due to their incomprehensible internal layers.
Q9. What is the difference between supervised and unsupervised learning?
Ans. In the supervised learning process, outputs are fed back into a computer system so that the software can learn from it and produce more accurate results in the successive occurrences; it is a kind of initial training for a system. On the other hand, unsupervised learning is a machine learning algorithm that draws inferences on its own from the unlabeled data set, without any external aid or input.
Q10. What kind problems are solved by regularization?
Ans. In machine learning, regularization is basically a process of introducing additional information with the purpose of solving an ill-posed problem or to avoid over fitting. It is basically a form of regression, which regularizes or constrains the coefficient estimates to zero. The technique of regularization prevents learning a more complex or flexible model in order to avoid the over fitting risk. Read More