Top 10 Most Important Interview Question and Answer on Machine Learning.
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2. Q1). Define the term
“Machine Learning”.
It is defined as a subset of
artificial intelligence (AI)
technology which allow systems
to learn and develop from
experience automatically
without being programmed
specifically. The focus of
machine learning is on
designing computer
programmes which can access
and use data to learn for
themselves.
3. Supervised learning requires labelled
training dataset. For instance, to train the
model, firstly it needs to be classified
dataset and then label into labelled
groups. On the other side, unsupervised
learning does not need any labelling data
explicitly.
Q2). Differentiate
between
supervised and
unsupervised
machine learning?
4. Data gathering
Data preparation
Data wrangling
Data analysis
Data selection and verification
Data deployment
The phases of the life cycle are as follows:
Q3). Name the
phases of the life
cycle of machine
learning.
5. It is a supervised Machine Learning algorithm that is use
for predictive analysis to find the linear relationship
between the dependent and the independent variables.
The linear Regression equation is:
y=mX +c
Where y= Dependent variable
x = Independent variable
m = Coefficient of X
c = Intercept point
Q4). What is a
Linear
Regression?
6. There are Three types of Machine
Learning. They are given below.
Q5). What are
the types of
Machine
Learning? Supervised Learning
Unsupervised Learning
Reinforcement Learning
7. There are different kinds of prediction problems in
machine learning that are based on supervised
and unsupervised learning. There are
classification, clustering and association. Here, we
are going to explore classification and regression.
Q6). Differentiate
between
classification and
regression in
Machine Learning.
Classification
Regression
8. Model selection is defined as the
process of selecting models from
various mathematical models that are
used to define the same data set. The
selection of models is applied to
statistics, machine learning and data
mining fields.
Q7). What is
model
selection in
Machine
Learning?
9. Q8). Name the three
stages which are required
to build the hypotheses or
model in machine
learning.
The three stages which are
required to build the
hypotheses or model in
machine learning are as
follows:
Building the model
Testing the model
Implementing the model
10. Q9). What do you mean
by cross-validation in
machine learning?
In Machine Learning, the cross-
validation method enables a
framework to improve the efficiency of
the given Machine Learning algorithm
to which you feed multiple sample
data from the dataset.
It consists of the following techniques:
Holdout method
K-fold cross-validation
Stratified k-fold cross-validation
Leave p-out cross-validation
11. Q10). Explain logistic
regression in detail. The proper regression analysis used
when the dependent variable is
categorical or binary is logistic
regression. Logistic regression is a tool
for predictive analysis, like other
regression analyses. To describe
information and the relationship
between one dependent binary
variable and one or more independent
variables, logistic regression is used.
Also, it is used to estimate the
likelihood of a categorical dependent
variable.
