How to Start Learning Generative AI?

The
world
is
at
the
crossroads
of
history
with
a
major
development
in
technology
in
the
form
of
AI.

Artificial
intelligence
has
become
one
of
the
essential
tools
for
driving
productive
transformation
across
different
industries.
One
of
the
most
valuable
forms
of
AI
which
has
encouraged
mainstream
adoption
of
artificial
intelligence
points
at

generative
AI.
The
best
example
of

generative
AI
tools
is
visible
in

ChatGPT. 

More
people
want
to
learn
generative
AI
and
its
potential
to
transform
conventional
systems
and
processes
in
business.
According
to
Forbes
Advisor,
almost
97%
of
business
owners
believe
that
ChatGPT
would
introduce
positive
changes
in
their
business
operations.

Generative
AI
tools
such
as
ChatGPT,
Google
Gemini,

Midjourney
and
DALL-E
3
have
been
pushing
AI
into
the
mainstream
market.

Artificial
intelligence
is
a
branch
of
computer
science
that
focuses
on
creating
systems
with
the
ability
to
simulate
human
intelligence.
Generative
AI
is
one
of
the
notable
variants
of
artificial
intelligence
that
focuses
on
generating
output
in
response
to
inputs
or
prompts
by
users.
If
you
are
interested
in
learning
AI,
then
you
can
begin
with
generative
AI
and
its
examples.

On
top
of
it,
you
must
also
find
the
important
resources
that
can
help
you
become
a
generative
AI
expert.
Let
us
discover
the
benefits
of
learning
generative
AI
and
best
practices
for
specializing
in
generative
AI.
You
can
also
discover
the
ideal
resources
to
build
fluency
in
generative
AI.

Identify
new
ways
to
leverage
the
full
potential
of
generative
AI
in
business
use
cases
and
become
an
expert
in
generative
AI
technologies
with Generative
AI
Skill
Path

Importance
of
Generative
AI

Generative
AI
has
gained
significant
amount
of
traction
in
recent
times,
particularly
among
AI
enthusiasts.
The
discussions
about
choosing
an
ideal
generative
AI
learning
path
also
invite
attention
towards
the
importance
of
generative
AI.
The
current
market
capitalization
of
the
global
generative
AI
market
is
$13.71
billion.
The
number
could
increase
to
more
than
$22
billion
by
2025.
Experts
have
also
pointed
out
that
the
generative
AI
market
may
surge
beyond
$110
billion
in
market
capitalization
by
2030.

Apart
from
the
market
growth,
the
usage
of
generative
AI
by
people
from
different
generations
sheds
light
on
its
growing
popularity.
Salesforce
has
reported
that
around
27%
of
millennials,
29%
of
Gen
Z
and
28%
of
Gen
X
have
been
actively
using
generative
AI
tools
in
workplace
environments.
The
interest
in
finding
generative
AI
resources
is
not
limited
to
its
adoption.
Users
feel
more
confident
about
understanding
the
complexities
of
generative
AI.
As
a
matter
of
fact,
around
60%
of
users
have
confidence
in
their
abilities
to
become
generative
AI
experts.

The
next
important
highlight
in
discussions
on
the
importance
of
generative
AI
draws
the
limelight
towards
reasons
for
adopting
generative
AI.
Almost
75%
of
users
leverage
generative
AI
for
automation
of
workplace
tasks,
enhancing
productivity
and
streamlining
processes. 

On
top
of
it,
around
52%
of
Gen
Z
users
trust
generative
AI
as
a
valuable
tool
for
making
crucial
decisions.
Therefore,
generative
AI
is
gradually
emerging
as
an
integral
element
in
decision-making.

Why
Should
You
Learn
Generative
AI?

The
growing
usage
and
adoption
of
generative
AI
provide
an
adequate
reason
for
learning
generative
AI.
With
the
help
of
an
effective
generative
AI
learning
path,
you
can
expand
your
skill
set
and
use
it
for
professional
development.
On
top
of
it,
learning
generative
AI
from
scratch
provides
different
reasons
other
than
learning
new
skills.

Your
skills
and
knowledge
of
generative
AI
can
help
you
with
informed
decision-making
for
organizational
implementation
or
career
development.
Here
are
some
of
the
benefits
of
learning
generative
AI
from
scratch. 

Learning
generative
AI
from
scratch
helps
in
building
foundational
mathematical
skills
with
in-depth
understanding
of
algorithms.
It
helps
in
developing
comprehensive
understanding
of
the
operations
and
performance
of
algorithms.
Your
mathematical
skills
are
useful
for
learning
about
intricate
algorithms
such
as
probabilistic
models,
GANs
and
autoencoders,
which
are
crucial
elements
for
developing
generative
AI
models. 

The
curiosity
of
learners
about
how
to
start
learning
artificial
intelligence
also
draws
them
towards
the
fundamentals
of
programming.
With
fluency
in
a
programming
language,
learners
can
develop
a
better
understanding
of
best
practices
for
the
implementation
of
diverse
algorithms.
It
can
offer
valuable
insights
regarding
the
functions
of
generative
AI
models,
thereby
ensuring
efficiency
and
optimization
in
model
development.

Another
important
reason
to
learn
generative
AI
from
scratch
points
to
the
development
of
agility
to
stay
updated
with
emerging
trends.
Learners
can
develop
readiness
to
adapt
to
new
developments
in
the
domain
of
generative
AI.
It
can
help
learners
stay
ahead
of
the
curve
and
maintain
relevancy
in
a
continuously
evolving
technological
field.

Enroll
now
in
the ChatGPT
Fundamentals
Course
and
dive
into
the
world
of
prompt
engineering
with
practical
demonstrations.

Step-by-Step
Process
to
Learn
Generative
AI
from
Scratch

The
journey
of
developing
your
expertise
in
generative
AI
follows
an
iterative
and
systematic
path.
You
have
to
follow
multiple
steps
that
are
related
to
each
other
and
that
help
you
develop
an
in-depth
overall
understanding
of
generative
AI
and
practical
expertise.
Here
are
the
essential
steps
that
you
must
follow
to
build
your
fluency
in
generative
AI.

• 
  Develop
  the
  Essential
  Prerequisite
  Skills

The
first
step
recommended
in
a
generative
AI
learning
path
focuses
on
developing
important
prerequisite
skills.
The
essential
skills
that
you
need
to
learn
generative
AI
include
basic
mathematics,
basic
statistics,
and
dedication
to
learning.
AI
depends
on
mathematical
concepts,
particularly
in
the
domains
of

deep
learning
and

machine
learning.

You
would
need
a
basic
understanding
of
concepts
in
linear
algebra,
probability
and
calculus.
On
top
of
it,
learners
must
also
prioritize
statistics
as
it
helps
in
the
effective
interpretation
of
data
and
extraction
of
insights.
Some
of
the
important
statistics
concepts
that
you
would
need
in
the
domain
of
generative
AI
include
distribution,
regression,
and
statistical
significance.  

• 
  Familiarize
  yourself
  with
  Fundamentals
  of
  Machine
  Learning 

The
next
step
in
the
journey
to
develop
your
expertise
in
generative
AI
involves
learning
about
machine
learning
fundamentals.
You
must
have
in-depth
fluency
in
the
fundamentals
of
machine
learning
to
dive
deeper
into
the
workings
of
generative
AI.
Anyone
who
wants
to
learn
artificial
intelligence
must
start
with
the
fundamental
concepts
of
ML
and
the
techniques
used
for
developing

ML
algorithms.

Learners
must
familiarize
themselves
with
techniques
such
as
supervised
and
unsupervised
learning
alongside
the
intricacies
of
regression
and
classification
models.
In
addition,
it
is
also
important
to
learn
about
the
nuances
of
testing
and
training
data
sets
for
machine
learning.
You
can
use
online
courses
on
machine
learning
alongside
interactive
projects
to
improve
your
expertise
in
machine
learning
fundamentals.

Want
to
understand
the
importance
of
ethics
in
AI,
ethical
frameworks,
principles,
and
challenges?
Enroll
now
in
the

Ethics
Of
Artificial
Intelligence
(AI)
Course

• 
  Enhance
  Your
  Skills
  in
  Python
  Programming 

Another
important
requirement
for
anyone
who
wants
to
develop
their
skills
in
generative
AI
points
to
the
Python
programming
language.
Python
is
an
essential
prerequisite
for
learning
AI
as
it
is
one
of
the
most
popular
languages
in
the
AI
and
machine
learning
landscape.
Fluency
in
Python
programming
is
a
must-have
requirement
for
the
implementation
of
machine
learning
models
alongside
learning
more
about
generative
AI.
Therefore,
you
must
develop
an
in-depth
understanding
of
Python
syntax,
libraries,
and
data
structures.

Learners
must
pay
special
attention
to
Python
libraries
such
as

TensorFlow,
NumPy
and
Pandas
and
their
uses
in
machine
learning.
Experts
suggest
that
you
use
project-based
learning
and
interactive
coding
platforms
to
develop
practical
fluency
in
Python
for
generative
AI.

• 
  Dive
  into
  Data
  Science
  and
  Deep
  Learning 

The
next
important
step
in
learning
generative
AI
is
developing
your
expertise
in
data
science
and
deep
learning.
Learners
must
look
for
generative
AI
course
materials
that
help
them
familiarize
themselves
with
data
preprocessing,
visualization
techniques
and
feature
engineering.
These
techniques
are
important
requirements
for
effective
understanding
and
management
of
complex
datasets.

You
should
also
explore
the
concepts
of
neural
networks,
recurrent
neural
networks
and
convolutional
neural
networks
alongside
other
deep
learning
architectures.
On
top
of
that,
you
must
also
develop
a
better
understanding
of
their
applications
in

natural
language
processing,
sequential
data
analysis,
and
image
generation.

Excited
to
learn
the
fundamentals
of
AI
applications
in
business?
Enroll
now
in
the AI
For
Business
Course

• 
  Start
  with
  Fundamentals
  of
  Generative
  AI

With
all
the
fundamental
skills,
you
are
now
ready
to
take
the
first
step
to
learning
generative
AI.
You
can
learn
fundamental
concepts
of
generative
AI
and
its
diverse
applications,
such
as
style
transfer,
image
generation,
and
text-to-image
conversion.
Learners
must
also
invest
efforts
in
learning
about
the
important
principles
underlying
the
working
of
Generative
Adversarial
Networks
or
GANs
and
Variational
Autoencoders
or
VAEs.
You
should
also
learn
about
other
popular
generative
AI
models
and
their
functions.
The
best
way
to
strengthen
your
foundational
understanding
of
generative
AI
involves
the
use
of
research
papers,
special
training
courses
and
online
lectures.

• 
  Try
  Out
  Hands-on
  Exercises
  and
  Challenges

Learning
the
theory
of
generative
AI
would
only
help
you
get
to
a
certain
extent
in
your
career
in
generative
AI.
You
should
focus
on
how
to
start
learning
artificial
intelligence
with
hands-on
exercises.
The
most
noticeable
advantage
of
hands-on
exercises
is
the
assurance
of
developing
practical
expertise.

You
must
develop
generative
AI
models
for
generating
art,
producing
music
or
synthesizing
images.
It
is
also
important
to
work
with
existing
datasets
and
optimize
generative
AI
models
to
explore
intricacies
in
their
behaviour
and
limitations.
You
can
rely
on
platforms
such
as
GitHub
and
Kaggle
alongside
other
open-source
repositories
to
access
a
collection
of
datasets
and
project
ideas
for
implementing
and
showcasing
your
skills.

Become
a
master
of
generative
AI
applications
by
developing
expert-level
skills
in
prompt
engineering
with Prompt
Engineer
Career
Path

Where
Can
You
Find
Resources
to
Learn
Generative
AI?

The
most
important
question
for
beginners
interested
in
learning
generative
AI
revolves
around
finding
the
ideal
training
resources.
You
can
use
generative
AI
resources
from
reliable
platforms
such
as
a
learning
path
by
Google
or
training
courses
on
platforms
such
as
LinkedIn
Learning.

You
can
explore
Google’s
learning
path
on
generative
AI
to
learn
about
the
fundamentals
of
generative
AI,

LLMs,
and
responsible
AI.
Subsequently,
you
can
dive
deeper
into
generative
AI
development
topics
such
as
attention
mechanisms,
transformer
models,
encoder-decoder
architecture
and
the
development
of
image
captioning
models.

Final
Words

The
review
of
the
important
steps
to
learn
generative
AI
reveals
that
anyone
can
start
learning
generative
AI
from
scratch.
Most
important
of
all,
you
don’t
have
to
be
an
expert
in
AI
to
improve
your
fluency
in

generative
AI.
As
the
world
embraces
generative
AI
in
different
forms,
it
is
important
to
think
of
the
advantages
of
learning
generative
AI.

Interestingly,
you
can
find
many
platforms
that
offer
trusted
and
comprehensive
training
resources
on
generative
AI.
Learners
should
refine
their
understanding
of
generative
AI
fundamentals
under
expert
instructors
and
develop
practical
skills
in
generative
AI.
Find
the
best
courses
on
the
fundamentals
of
generative
AI
right
away.