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  /  Data Science   /  20 Essential Interview Questions On Machine Learning

20 Essential Interview Questions On Machine Learning

  1. What is Machine Learning?

Machine Learning is a scientific study in which a computer performs explicit tasks with the help of descriptive statistics and algorithms. It is an application of Artificial Intelligence that gives the ability to a machine to perform explicitly.

  1. Can you Name some Real-Life Applications of Machine Learning?
  • Face Recognition
  • Speech Recognition
  • Learning Associations
  • Prediction
  • Financial Services
  1. What are the ways a Machine Learns?
  • Supervised Learning
  • Unsupervised Learning
  • Reinforcement Learning


  1. What is Supervised Learning?

It is a kind of Machine Learning where the machine learns using labelled data. It is like there is a teacher and you are learning under his guidance.

  1. What is Unsupervised Machine Learning?

It is a method where the machine is trained on unlabelled data and it can run without any guidance.

  1. What is Reinforced Learning?

This kind of learning involves an agent that interacts with the circumstances. It produces actions and error both.

  1. What is Selection Bias?

It is a statistical error in Big Data. It causes a bias in the sampling portion of an experiment. Selection bias may produce an inaccurate conclusion.

  1. What is Inductive and Deductive Learning?
  • INDUCTIVE MACHINE LEARNING: it is the process of using observation to get a conclusion.
  • DEDUCTIVE MACHINE LEARNING: it is the process of using conclusions to form observation
  1. 9.  What is Cluster Sampling?

Cluster sampling is the process of randomly selecting groups within a defined population. It is a probability sampling. 

  1. Can you Name a Few Libraries which Python USe for Data Analysis?
  1. Are NUMPY And SCIPY related to each other?

Yes. NumPy is a part of SciPy in the field of Machine Learning. NumPy defines arrays along with basic numerical functions and SciPy implements compilation such as numerical integration, optimization, etc.

  1. What is the Difference Between Data Mining and Machine Learning

Machine Learning gives a computer the ability to work autonomously without being explicitly programmed.

Data Mining is a process in which unstructured data tries to extract knowledge or make patterns from Big Data.

  1. When does Overfitting  Machine Learning Happen?

If the criteria used to train the model are not the same as the criteria used to judge the efficiency of a model, overfitting happens.

  1. What are the Five Popular Algorithms of Machine Learning ?
  1. What are the Stages a Model is Made in Machine Learning ?
  • Model Building
  • Model Testing
  • Applying the Model
  1. What is the Basic Difference Between Machine Learning and Artificial Intelligence

In Machine Learning, a computer is given tasks which it can perform autonomously without being explicitly performed. 

Artificial Intelligence is defined as an acquisition of knowledge intelligence. AI has the ability to acquire and apply knowledge. It is also called Machine Intelligence.

  1. What is Genetic Programming?

It is one of the techniques used in the field of Big Data and Data Science. This model is based on testing and selecting the best choice when there is a set of results.

  1. What is the Method Used to Prevent Outfitting in Machine Learning?

ISONIC REGRESSION is used to prevent outfitting in Machine Learning.

  1. What are the essential Data Scientist’s Skill-Set?
  • He should know the descriptive statistics, logic, and mathematics well.
  • Python for Data Science is highly recommended and he should also know several computer programing languages like R, SQL, JAVA, etc.
  • He should have the ability to take instant decision when a problem is detected.
  • He should possess in-depth knowledge of AI and Machine Learning
  • Above all Data Scientist’s Job requires communication power to interact with the team members properly
  1. What is Perceptron in Machine Learning?

It is an algorithm for supervised classification of the inputs. It helps to transform those inputs into several possible non-binary outputs.



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