Machine learning interview questions are one of the most important part in data science interview.There are some answers to go along with them so you don’t get stumped. You’ll be able to tackle basic questions on machine learning in any job interview after reading through this piece.

##
What is Target Function?

## What is Machine Learning?

Machine learning is a field of study that gives computers ability to learn without being explicitly programmed.

or

A computer is said to learn from experience E with respect to some task T and some performance measure P, if it's performance on T as measured by P increases with E.

## What are the types of Machine Learning?

-Supervised learning

-Unsupervised learning

-Reinforcement learning

-Recommendation system

## What do you mean by training sample?

A
training sample is a data point

*x*in an available training set that we use for tackling a predictive modeling task. For example, if we are interested in classifying emails, one email in our data set would be one training sample. Sometimes, people also use the synonymous terms*training instance*or*training exampl**e*.## What do you understand by the term Regression?

A technique for determining the statistical
relationship between two or more variables where a change in a dependent
variable is associated with, and depends on, a change in one or more
independent variables.

##
What is Target Function?

In predictive modeling, we
are typically interested in modeling a particular process; we want to learn or
approximate a particular function that, for example, let's us distinguish spam
from non-spam email. The

*target function**f(x) = y*is the true function*f*that we want to model.## What is Hypothesis?

A hypothesis is a certain
function that we believe (or hope) is similar to the true function, the

*target function*that we want to model. In context of email spam classification, it would be the*rule*we came up with that allows us to separate spam from non-spam emails.
OR

A hypothesis is an assumption about a particular situation of the
world that is testable.

## What do you mean by NULL hypothesis?

An example. I am accused of
murder. The null hypothesis is that I am not guilty, because the legal
principle is that I am innocent until I am proven guilty. Since the null
hypothesis must be constructed before you examine the evidence it must be that
I am not guilty. The alternative hypothesis is that I am guilty. If the
evidence is sufficiently persuasive, you will shift your belief from the null
hypothesis to the alternative hypothesis.

## What is a Machine Learning Model?

In machine learning field, the terms hypothesis and model are
often used interchangeably. In other sciences, they can have different
meanings, i.e., the hypothesis would be the "educated guess" by the
scientist, and the model would be the manifestation of
this guess that can be used to test the hypothesis.

## What do you mean by Learning Algorithm?

Again, our goal is to find
or approximate the target function, and the learning algorithm is a
set of instructions that tries to model the target function using
our training data-set. A learning algorithm comes with a hypothesis
space, the set of possible hypotheses it can come up with in order to model
the unknown target function by formulating the final hypothesis

## How will you define term Classifier?

A classifier is a special
case of a hypothesis (nowadays, often learned by a machine
learning algorithm). A classifier is a hypothesis or discrete-valued
function that is used to assign (categorical) class labels to
particular data points. In the email classification example, this classifier
could be a hypothesis for labeling emails as spam or non-spam. However, a hypothesis must
not necessarily be synonymous to a classifier. In a different
application, our hypothesis could
be a function for mapping study time and educational backgrounds of students to
their future SAT scores.

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