Machine learning short Question for Viva

Here,  given most important some machine learning question , this is most help full for viva exam and interview .

Q.1) What is Machine learning?

Answer:-  Machine learning is a branch of computer science which deals with system programming in order to automatically learn and improve with experience.  For example: Robots are programed so that they can perform the task based on data they gather from sensors. It automatically learns programs from data.

Q.2) What is difference between  machine learning and artificial intelligence?

Answer:-

Artificial Intelligence	Machine learning
Artificial intelligence is a technology which  enables a machine to simulate human behavior.	Machine learning is a subset of AI which allows a machine to automatically learn from past data without programming explicitly.
The goal of AI is to make a smart computer  system like humans to solve complex problems.	The goal of ML is to allow machines to learn from data so that they can give  accurate output.
In AI, we make intelligent systems to perform any task like a human.	In ML, we teach machines with data  to perform a particular task and give an accurate result.
Machine learning and deep learning are the two main subsets of AI.	Deep learning is a main subset of machine  learning.
AI has a very wide range of scope.	Machine learning has a limited scope.
AI is working to create an intelligent system  which can perform various complex tasks.	Machine learning is working to create machines that can perform only those specific tasks for which they are trained.
AI system is concerned about maximizing the chances of success.	Machine learning is mainly concerned  about accuracy and patterns.
The main applications of AI are Siri, customer  support using catboats, Expert System,  Online game playing, intelligent humanoid  robot, etc.	The main applications of machine learning  are Online recommender system,  Google search algorithms, Facebook  auto friend tagging suggestions, etc.
On the basis of capabilities, AI can be divided  into three types, which are, Weak AI, General AI, and Strong AI.	Machine learning can also be divided into mainly three types that are  Supervised,learning,Unsupervised,learning, and Reinforcement learning.

Q.3) Mention the difference between Data Mining and Machine learning?

Machine learning relates with the study, design and development of the algorithms that give computers the capability to learn without being explicitly programmed.  While, data mining can be defined as the process in which the unstructured data tries to extract knowledge or unknown interesting patterns.  During this process machine, learning algorithms are used.

Q.4) What is ‘Overfitting’ in Machine learning?In machine learning, when a statistical model describes random error or noise instead of underlying relationship ‘overfitting’ occurs.  When a model is excessively complex, overfitting is normally observed, because of having too many parameters with respect to the number of training data types. The model exhibits poor performance which has been overfit.

Q.5) Why overfitting happens?

The possibility of overfitting exists as the criteria used for training the model is not the same as the criteria used to judge the efficacy of a model.7) What are the five popular algorithms of Machine Learning?

• Decision Trees
• Neural Networks (back propagation)
• Probabilistic networks
• Nearest Neighbor
• Support vector machines

Q.6) What are the different Algorithm techniques in Machine Learning?

The different types of techniques in Machine Learning are

• Supervised Learning(define: Supervised learning, also known as supervised machine learning, is a subcategory of machine learning and artificial intelligence. It is defined by its use of labeled datasets to train algorithms that to classify data or predict outcomes accurately. As input data is fed into the model, it adjusts its weights until the model has been fitted appropriately.) 
• Unsupervised Learning(define:Unsupervised machine learning and supervised machine learning are frequently discussed together. Unlike supervised learning, unsupervised learning uses unlabeled data. From that data, it discovers patterns that help solve for clustering or association problems. This is particularly useful when subject matter experts are unsure of common properties within a data set. Common clustering algorithms are hierarchical, k-means, and Gaussian mixture models.)
• Semi-supervised Learning(define:Semi-supervised learning is a learning problem that involves a small number of labeled examples and a large number of unlabeled examples.
  Learning problems of this type are challenging as neither supervised nor unsupervised learning algorithms are able to make effective use of the mixtures of labeled and untellable data. As such, specialized semis-supervised learning algorithms are required.)
• Reinforcement Learning(define:Reinforcement learning is an area of Machine Learning. It is about taking suitable action to maximize reward in a particular situation. It is employed by various software and machines to find the best possible behavior or path it should take in a specific situation. Reinforcement learning differs from the supervised learning in a way that in supervised learning the training data has the answer key with it so the model is trained with the correct answer itself whereas in reinforcement learning, there is no answer but the reinforcement agent decides what to do to perform the given task. In the absence of a training dataset, it is bound to learn from its experience.)

Q.7) What are the three stages to build the hypotheses or model in machine learning?

• Model building
• Model testing
• Applying the model

Q.8) 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.

Q.9) What is algorithm independent machine learning?

Machine learning in where mathematical foundations is independent of any particular classifier or learning algorithm is referred as algorithm independent machine learning?

Q.10) 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.

Q.11) What is Inductive Logic Programming in Machine Learning?

Inductive Logic Programming (ILP) is a subfield of machine learning which uses logical programming representing background knowledge and examples.

Q.12) What is ensemble learning?

To solve a particular computational program, multiple models such as classifiers or experts are strategically generated and combined. This process is known as ensemble learning.

Q.13) What are support vector machines?

Support vector machines are supervised learning algorithms used for classification and regression analysis.

Q.14) What are two techniques of Machine Learning ?

The two techniques of Machine Learning are

• Genetic Programming
• Inductive Learning

Q.15) Explain what is the function of ‘Supervised Learning’?

• Classifications
• Speech recognition
• Regression
• Predict time series
• Annotate strings

Q.16) Machine learning applications in daily life?

Google Maps,

 Google assistant,

 Alexa.

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