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Machine Learning Interview Questions and Answers

Machine Learning Interview Questions and Answers

Question - 1 : - What do you understand by Machine learning?

Answer - 1 : -

Machine learning is the form of Artificial Intelligence that deals with system programming and automates data analysis to enable computers to learn and act through experiences without being explicitly programmed.

For example, Robots are coded in such a way that they can perform the tasks based on data they collect from sensors. They automatically learn programs from data and improve with experiences.

Question - 2 : - Differentiate between inductive learning and deductive learning?

Answer - 2 : -

In inductive learning, the model learns by examples from a set of observed instances to draw a generalized conclusion. On the other side, in deductive learning, the model first applies the conclusion, and then the conclusion is drawn.
  • Inductive learning is the method of using observations to draw conclusions.
  • Deductive learning is the method of using conclusions to form observations.

For example, if we have to explain to a kid that playing with fire can cause burns. There are two ways we can explain this to a kid; we can show training examples of various fire accidents or images of burnt people and label them as "Hazardous". In this case, a kid will understand with the help of examples and not play with the fire. It is the form of Inductive machine learning. The other way to teach the same thing is to let the kid play with the fire and wait to see what happens. If the kid gets a burn, it will teach the kid not to play with fire and avoid going near it. It is the form of deductive learning.

Question - 3 : -
What is the difference between Data Mining and Machine Learning?

Answer - 3 : -

Data mining can be described as the process in which the structured data tries to abstract knowledge or interesting unknown patterns. During this process, machine learning algorithms are used.

Machine learning represents the study, design, and development of the algorithms which provide the ability to the processors to learn without being explicitly programmed.

Question - 4 : - What is the meaning of Overfitting in Machine learning?

Answer - 4 : -

Overfitting can be seen in machine learning when a statistical model describes random error or noise instead of the underlying relationship. Overfitting is usually observed when a model is excessively complex. It happens because of having too many parameters concerning the number of training data types. The model displays poor performance, which has been overfitted.

Question - 5 : - Why overfitting occurs?

Answer - 5 : -

The possibility of overfitting occurs when the criteria used for training the model is not as per the criteria used to judge the efficiency of a model.

Question - 6 : - What are the different types of Algorithm methods in Machine Learning?

Answer - 6 : -

The different types of algorithm methods in machine earning are:
  • Supervised Learning
  • Semi-supervised Learning
  • Unsupervised Learning
  • Transduction
  • Reinforcement Learning

Question - 7 : - How is KNN different from k-means?

Answer - 7 : -

KNN or K nearest neighbors is a supervised algorithm which is used for classification purpose. In KNN, a test sample is given as the class of the majority of its nearest neighbors. On the other side, K-means is an unsupervised algorithm which is mainly used for clustering. In k-means clustering, it needs a set of unlabeled points and a threshold only. The algorithm further takes unlabeled data and learns how to cluster it into groups by computing the mean of the distance between different unlabeled points.

Question - 8 : - How does Machine Learning differ from Deep Learning?

Answer - 8 : -

  1. Machine learning is all about algorithms which are used to parse data, learn from that data, and then apply whatever they have learned to make informed decisions.
  2. Deep learning is a part of machine learning, which is inspired by the structure of the human brain and is particularly useful in feature detection.

Question - 9 : - Differentiate supervised and unsupervised machine learning.

Answer - 9 : -

  • In supervised machine learning, the machine is trained using labeled data. Then a new dataset is given into the learning model so that the algorithm provides a positive outcome by analyzing the labeled data. For example, we first require to label the data which is necessary to train the model while performing classification.
  • In the unsupervised machine learning, the machine is not trained using labeled data and let the algorithms make the decisions without any corresponding output variables.

Question - 10 : - What is the method to avoid overfitting?

Answer - 10 : -

Overfitting occurs when we have a small dataset, and a model is trying to learn from it. By using a large amount of data, overfitting can be avoided. But if we have a small database and are forced to build a model based on that, then we can use a technique known as cross-validation. In this method, a model is usually given a dataset of a known data on which training data set is run and dataset of unknown data against which the model is tested. The primary aim of cross-validation is to define a dataset to "test" the model in the training phase. If there is sufficient data, 'Isotonic Regression' is used to prevent overfitting.

NCERT Solutions


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