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Top 20 Machine Learning Interview Questions & Answers

  1. What is Machine learning?

Machine Learning is a field of computer science that deals with system programming to learn and improve with experience.

For example: Robots are coded so that they can perform the task based on data they collect from sensors. It robotically learns programs from data.

  1. Mention the difference between Data Mining and Machine learning?

Data mining: It is defined as the process in which the unstructured data tries to abstract knowledge or unknown interesting patterns.  During this machine process, learning algorithms are used.

Machine learning: It relates with the study, design and development of the algorithms that give processors the ability to learn without being openly programmed.

  1. What is ‘Training set’ and ‘Test set’?

Training set: It is a set of data is used to discover the potentially predictive relationship in various areas of information science like machine learning. It is an example given to the learner.

Test set: It is used to test the accuracy of the hypotheses generated by the learner, and it is the set of example held back from the learner.

  1. List down various approaches for machine learning?

The different approaches in Machine Learning are:

  • Concept Vs Classification Learning
  • Symbolic Vs Statistical Learning
  • Inductive Vs Analytical Learning
  1. Explain what is the function of ‘Unsupervised Learning’?
  • Find clusters of the data
  • Find low-dimensional representations of the data
  • Find interesting directions in data
  • Interesting coordinates and correlations
  • Find novel observations/ database cleaning
  1. Explain what is the function of ‘Supervised Learning’?

Functions of Supervised Learning are:

  • Classifications
  • Regression
  • Predict time series
  • Speech recognition
  • Annotate strings
  1. What is Genetic Programming?

Genetic programming is one of the two techniques used in machine learning. The model is based on the testing and selecting the best choice among a set of results.

  1. What is the difference between artificial learning and machine learning?

Machine Learning: Designing and developing algorithms according to the behaviors based on empirical data are known as Machine Learning.

Artificial intelligence: in addition to machine learning, it also covers other aspects like knowledge representation, natural language processing, planning, robotics etc.

  1. What is classifier in machine learning?

A classifier in a Machine Learning is a system that inputs a vector of discrete or continuous feature values and outputs a single discrete value, the class.

  1. What is ‘Overfitting’ in Machine learning?

In machine learning, when a statistical model defines random error of underlying relationship ‘overfitting’ occurs.  When a model is exceptionally complex, overfitting is generally observed, because of having too many factors with respect to the number of training data types. The model shows poor performance which has been overfit.

  1. Why overfitting happens?

The possibility of overfitting happens as the criteria used for training the model is not the same as the criteria used to judge the efficiency of a model.

  1. What is inductive machine learning?

The inductive machine learning implicates the process of learning by examples, where a system, from a set of observed instances tries to induce a general rule.

  1. What are the different Algorithm techniques in Machine Learning?

The different types of techniques in Machine Learning are:

  • Supervised Learning
  • Semi-supervised Learning
  • Unsupervised Learning
  • Transduction
  • Reinforcement Learning
  1. What is the standard approach to supervised learning?

Split the set of example into the training set and the test is the standard approach to supervised learning is.

  1. What is not Machine Learning?
  • Rule based inference
  • Artificial Intelligence
  1. In what areas Pattern Recognition is used?

Pattern Recognition can be used in the following areas:

  • Computer Vision
  • Data Mining
  • Speech Recognition
  • Informal Retrieval
  • Statistics
  • Bio-Informatics
  1. What is ensemble learning?

To solve a specific computational program, numerous models such as classifiers are strategically made and combined. This process is known as ensemble learning.

  1. Which method is frequently used to prevent overfitting?

Isotonic Regression is used to prevent an overfitting problem.

  1. What is Model Selection in Machine Learning?

The process of choosing models among diverse mathematical models, which are used to define the same data set is known as Model Selection. It is applied to the fields of statistics, data mining and machine learning.

  1. How can you avoid overfitting?

By using a lot of data overfitting can be avoided, overfitting happens relatively as you have a small dataset, and you try to learn from it. But if you have a small database and you are forced to come with a model based on that. In such situation, you can use a technique known as cross validation. In this method the dataset splits into two section, testing and training datasets, the testing dataset will only test the model while, in training dataset, the data points will come up with the model.

In this technique, a model is usually given a dataset of a known data on which training (training data set) is run and a dataset of unknown data against which the model is tested. The idea of cross validation is to define a dataset to “test” the model in the training phase.

This post first appeared on Top Five Industrial Training Courses For Mechanical Engineering Students | Multisoft Systems, please read the originial post: here

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Top 20 Machine Learning Interview Questions & Answers


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