How Machine Learning Works Step by Step?

Machine
learning
is
one
of
the
most
promising
branches
of
science
that
has
come
into
existence
in
recent
times.
Although
the
concept
of
machine
learning
is
in
its
initial
phase,
it
has
shown
immense
potential
as
well
as
promise.
Today,
machine
learning
has
made
its
presence
in
diverse
areas
such
as
image
recognition,
fraud
detection,
self-driving
cars,
recommender
systems,
and
many
more.
If
you
wish
to
understand
the
true
potential
of
machine
learning,
you
need
to
focus
on
machine
learning
work
that
takes
place
at
a
deeper
level.

Are
you
curious
to
know
how
machine
learning
work?
If
yes,
you
will
get
an
answer
to
the
question
pretty
soon.
However,
before
learning
about
the
working
of
machine
learning,
you
should
have
a
clear
understanding
of
machine
learning.
It
can
help
to
strengthen
your
foundation
on
the
subject.
In
case
you
are
feeling
overwhelmed
by
learning
about
machine
learning,
you
do
not
have
to
feel
nervous.
The
simple
steps
will
help
you
grasp
the
machine-learning
concept
and
the
underlying
machine-learning
workflow
in
an
effective
manner.

An
insight
into
Machine
Learning

Machine
learning
(ML)
is
a
branch
of

Artificial
Intelligence
(AI)
technology.
It
fundamentally
focuses
on
the
creation
of
computer
algorithms
that
improve
via
experiences
as
well
as
the
application
of
data.
The
ML
technology
allows
computer
systems
to
learn
from
data.
Moreover,
Machine
Learning
enables
them
to
make
predictions
or
decisions
without
having
to
explicitly
program
them. 

In
the
central
position
of
ML,
the
key
is
to
create
and
implement
algorithms.
These
algorithms
are
responsible
for
facilitating
decisions
and
predictions.
The
role
of
the
algorithms
is
of
high
importance
since
they
enhance
their
performance
with
time.
Hence,
as
time
passes
and
more
data
is
processed,
the
accuracy
and
precision
of
decisions
and
predictions
get
better.
In
current
times,
when
technology
is
evolving
rapidly,
the
function
of
machine
learning
is
considered
to
be
of
immense
value.
By
imitating
the
learning
process
in
human
beings’
machine
learning,
the
level
of
accuracy
is
strengthened.

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Flashcards

Chief
Machine
Learning
Methods 

Before
diving
into
the
steps
that
arise
in
the
context
of
Machine
Learning,
you
need
to
learn
about
the
main
machine
learning
methods.
The
insight
will
help
you
at
a
later
stage,
especially
while
guiding
your
ML
model.
There
are
two
different
machine
learning
methods
that
you
can
use:
supervised
learning
and
unsupervised
learning.

• 
  Supervised
  learning 

In
the
machine
learning
context,
supervised
learning
is
among
the
most
common
machine
learning
methods.
This
is
because
it
deals
with
tasks
and
activities
that
are
straightforward.
The
labeling
of
data
inputs
is
done
with
the
answers
that
the
algorithm
has
to
arrive
at.
Hence,
the
machine
is
able
to
pick
patterns
gradually,
which
supports
better
data
differentiation
and
assists
in
making
predictions.
It
is
possible
to
compare
this
method
to
a
technique
where
a
test
is
given
to
an
individual,
and
they
are
also
provided
with
an
answer
key.

• 
  Unsupervised
  learning 

Unsupervised
learning
is
the
machine
learning
method
in
which
a
model
gets
to
learn
something
organically.
This
is
possible
because
there
is
no
explicit
instruction
that
the
model
can
follow
in
order
to
arrive
at
the
outcome.
As
a
result,
the
model
automatically
attempts
to
locate
structure
and
pattern
in
raw
data
by
conducting
analysis
as
well
as
interpretations. 

It
is
true
that
supervised
learning
is
simpler
and
easier
than
unsupervised
learning.
However,
one
may
not
have
access
to
comprehensive
data
to
train
the
algorithm.
In
such
a
scenario,
one
has
to
rely
on
unsupervised
learning.
Now
that
you
have
a
better
understanding
of
machine
learning
and
different
machine
learning
methods,
it
is
time
to
answer
the
question
–
how
does
machine
learning
work?

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Key
Steps
in
Machine
Learning 

In
order
to
understand
how
does
machine
learning
work,
it
is
essential
to
adopt
a
methodical
approach;
the
step-by-step
will
help
you
to
understand
each
of
the
processes
in
a
detailed
manner.
The
entire
machine
learning
work
process
has
been
broken
down
into
simple
steps
so
that
you
can
grasp
it
in
detail.
Follow
the
simple
steps
to
become
proficient
in
how
Machine
Learning
technology
works:

Step
1
–
Data
Collection
Process
  

The
basic
step
in
machine
learning
involves
the
collection
of
data.
Undoubtedly,
the
data
collection
process
is
the
core
of
the
machine
learning
workflow.
Moreover,
while
collecting
data,
it
is
essential
to
prioritize
the
quality
as
well
as
the
quantity
of
data.
This
is
because
these
characteristics
of
data
can
have
a
direct
implication
on
the
performance
of
the
machine
learning
model.
It
is
a
key
process
that
is
the
foundation
for
the
subsequent
processes.

You
can
use
a
number
of
sources
to
collect
data.
Some
of
the
common
sources
are
text
files,
databases,
audio
files,
images,
and
many
more.
After
data
gathering
is
complete,
you
need
to
shift
your
attention
towards
preparing
it
for
ML.  

Step
2
–
Preprocessing
of
Data 

The
second
stage
in
machine
learning
workflow
involves
the
preprocessing
of
data.
It
is
one
of
the
most
important
steps,
and
it
involves
data
cleaning,
handling
of
missing
data,
and
normalizing
of
data.
Data
preprocessing
is
necessary
since
it
can
enhance
the
quality
of
data.
Moreover,
it
can
make
sure
that
your
machine
learning
model
is
able
to
make
interpretations
accurately.

Step
3
–
Selection
of
the
Appropriate
Model 

When
it
comes
to
the
working
of
machine
learning,
one
of
the
most
crucial
steps
is
the
selection
of
the
model.
There
is
a
broad
range
of

machine
learning
models
that
you
can
choose
from,
such
as
linear
regression,
neural
networks,
decision
trees,
and
many
more,

The
model
that
you
ultimately
choose
depends
on
the
nature
of
the
data
that
you
have
gathered.
The
problem
that
you
plan
to
solve
may
also
come
into
play
and
influence
the
model
that
you
choose.
Choosing
the
appropriate
model,
you
need
to
focus
on
factors
such
as
the
data
type,
the
data
size,
and
the
complex
nature
of
the
problem. 

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Step
4
–
Need
to
Train
the
Model

Once
you
have
chosen
the
machine
learning
model,
you
need
to
focus
on
training
the
model.
Training
fundamentally
involves
feeding
the
data
into
the
machine
learning
model.
Furthermore,
it
also
involves
making
adjustments
to
the
internal
parameters
so
that
better
predictions
can
be
made. 

Step
5
–
Evaluation
of
the
Model 

When
it
comes
to
machine
learning
work,
one
of
the
key
things
to
keep
in
mind
revolves
around
the
evaluation
of
the
model.
After
the
training
is
complete,
you
need
to
evaluate
it.
This
step
is
crucial
to
check
its
performance
before
its
actual
deployment.
Basically,
you
need
to
test
the
model
by
using
new
data.
You
can
use
certain
metrics
to
check
its
performance,
such
as
accuracy
and
precision.

Step
6
–
Tuning
of
the
parameters

When
it
comes
to
the
working
of
machine
learning,
it
is
essential
to
focus
on
proper
tuning
of
the
parameters.
It
is
important
to
make
adjustments
to
the
parameters
of
the
model.
It
can
directly
impact
the
function
of
machine
learning.
This
process
is
commonly
known
as
parameter
tuning
in
machine
learning.
You
can
use
a
number
of
techniques
for
the
same,
such
as
cross-validation
and
grid
search.

Step
7
–
Making
Predictions
and
Deployment 

The
final
step
that
you
need
to
know
how
does
machine
learning
work
involves
making
predictions
and
deployment.
After
all
the
steps
have
been
correctly
executed,
the
model
should
be
able
to
make
predictions
correctly.
You
need
to
feed
new
data
into
the
ML
model
to
see
how
well
it
can
make
predictions.
It
is
a
crucial
step
that
you
cannot
ignore
at
any
cost.
Once
you
are
certain
about
the
accuracy
of
the
predictions,
it
is
ready
for
the
final
deployment.

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Conclusion  

Machine
learning
is
one
of
the
most
promising
technologies
today.
If
you
want
to
learn
about
the
function
of
machine
learning,
it
is
essential
to
understand
how
machine
learning
works.
The
step-by-step
guide
will
help
you
gain
comprehensive
insight
into
ML
and
its
underlying
processes.

The
chief
processes
and
steps
that
have
been
presented
are
the
data
collection
process,
preprocessing
of
data,
selection
of
the
appropriate
model,
training
the
model,
evaluating
the
model,
tuning
the
parameters,
and
making
predictions
and
deployment.
The
role
of
each of
the
processes
is
essential
in
the
ML
context.

Furthermore,
it
is
also
important
for
you
to
understand
the
different
machine
learning
methods.
The
insight
can
definitely
help
you
while
training
the
model.
The
systematic
presentation
of
the
information
can
definitely
help
you
broaden
your
knowledge
of
Machine
Learning
in
a
simple
and
effective
manner. 

You
need
to
understand
that
a
solid
understanding
of
the
ML
concept
is
integral
before
you
get
into
the
underlying
processes
and
steps.
By
following
the
roadmap,
you
will
surely
be
able
to
capture
the
intricate
details
relating
to
machine
learning,
which
is
taking
the
world
by
storm.
As
Machine
Learning
technology
is
full
of
new
possibilities,
it
is
high
time
to
comprehend
the
steps
that
make
the
technology
work
and
imitate
human
learning.