INTERVIEW QUESTIONS AND ANSWERS  MACHINE LEARNING INTERVIEW QUESTIONS What is Machine Learning? Machine learning is the branch of computer science which explores the study and construction of algos that can learn from and make predictions on data. ML majorly deals with system programming in order to automatically learn the things. For Example: Robots. What are the five popular algorithms of Machine Learning? The five popular algos of machine Learning are:

• Decision Trees
• Probabilistic Networks
• Neural Networks
• Nearest Neighbors
• Support Vector Machines

What are the different Algorithm techniques in ML? The different types of machine Learning techniques are:

• Supervised Learning
• Semi-supervised Learning
• Unsupervised Learning
• Reinforcement Learning
• Learning to Learn
• Transduction

Name the three stages to build the hypotheses or model in machine learning? The three stages to build the hypotheses or model in machine learning are:

• Model building
• Model testing
• Applying the model

What is the standard approach to supervised learning? The standard approach to supervised learning is to split the set of example into the training set and the test. Name the various approaches for machine learning? The list of approaches are:

• Concept Vs Classification Learning
• Inductive Vs Analytical Learning
• Symbolic Vs Statistical Learning

What is not Machine Learning?

• Artificial Intelligence
• Rule based inference

Explain what are the functions of 'Supervised Learning'? The functions are:

• Classifications
• Regression
• Predict Time Series
• Speech Recognition
• Annotate Strings

What are the functions of 'Unsupervised Learning'? The functions are:

• To find the clusters of the data
• To find interesting directions in data
• To find novel observations/ database cleaning
• To find low-dimensional representations of the data
• Interesting coordinates and correlations

What is algorithm independent machine learning? Algorithm independent machine learning is that where mathematical foundations is independent of any particular classifier or learning algos. What is Perceptron in Machine Learning? Perceptron in machine learning is an algorithm for supervised classification of the input into one of several possible non-binary outputs. What are Bayesian Networks (BN)? To represent the graphical model for probability relationship among a set of variables the Bayesian Networks is used. Why and When ensemble learning is used? Ensemble learning is used to improve the classification, prediction, function approximation, etc., of a model. It is used when you want to build component classifiers that are more accurate and independent from each other. What is PCA, KPCA and ICA used for? These are the features of extraction techniques used for dimensionality reduction.

• PCA - Principal Components Analysis
• KPCA - Kernel based Principal Components Analysis
• ICA - Independent Component Analysis

What is the difference between artificial intelligence and machine learning? Machine Learning and Artificial Intelligence are both the terms of computer science. When machine can learn by its own without being programmed then it is known as Machine Learning. Whereas Artificial Intelligence is an addition to ML(Machine Learning) that covers various aspects like knowledge representation, natural language processing, planning, robotics etc.