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L0-LG_en-KE_L7-LG_en-KE_Learning graph - LLMs - Experience AI foundations unit
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Learning
graph
Experience
AI
Lessons
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.
Concept
Skill
Key:
Links:
Direct
prerequisite
Scaffolding,
not
direct
prerequisite
Describe
the
difference
between
‘data-driven’
and
‘rule-based’
approaches
to
application
development
Name
examples
of
AI
applications
Outline
some
benefits
and
issues
of
using
AI
applications
Define
ML’s
relationship
to
AI
Name
the
three
common
approaches
to
ML
Describe
how
classification
can
be
solved
using
supervised
learning
Explain
the
need
for
both
training
and
test
data
Explain
how
bias
can
influence
the
predictions
generated
by
an
ML
model
Describe
how
decision
trees
are
used
to
build
a
classification
ML
model
Describe
how
training
data
changes
an
ML
model
Explain
why
ML
is
used
to
create
decision
trees
Describe
the
stages
of
the
AI
project
lifecycle
Use
an
ML
tool
to
import
data
and
train
a
model
Produce
a
model
card
to
explain
an
ML
model
Recognise
the
range
of
opportunities
that
exist
in
AI-related
careers
Evaluate
an
ML
model
by
testing
and
reviewing
the
accuracy
of
an
ML
model
Describe
the
difference
between
‘data-driven’
and
‘rule-based’
approaches
to
application
development
Name
examples
of
AI
applications
Outline
some
benefits
and
issues
of
using
AI
applications
Define
ML’s
relationship
to
AI
Name
the
three
common
approaches
to
ML
Describe
how
classification
can
be
solved
using
supervised
learning
Explain
the
need
for
both
training
and
test
data
Explain
how
bias
can
influence
the
predictions
generated
by
an
ML
model
Describe
how
decision
trees
are
used
to
build
a
classification
ML
model
Describe
how
training
data
changes
an
ML
model
Explain
why
ML
is
used
to
create
decision
trees
Describe
the
stages
of
the
AI
project
lifecycle
Use
an
ML
tool
to
import
data
and
train
a
model
Produce
a
model
card
to
explain
an
ML
model
Recognise
the
range
of
opportunities
that
exist
in
AI-related
careers
Evaluate
an
ML
model
by
testing
and
reviewing
the
accuracy
of
an
ML
model
SEAME
framework
levels
Concepts
and
skills
Bloom’s
Taxonomy
Describe
the
difference
between
‘data-driven’
and
‘rule-based’
approaches
to
application
development
Name
examples
of
AI
applications
Outline
some
benefits
and
issues
of
using
AI
applications
Define
ML’s
relationship
to
AI
Name
the
three
common
approaches
to
ML
Describe
how
classification
can
be
solved
using
supervised
learning
Explain
the
need
for
both
training
and
test
data
Explain
how
bias
can
influence
the
predictions
generated
by
an
ML
model
Describe
how
decision
trees
are
used
to
build
a
classification
ML
model
Describe
how
training
data
changes
an
ML
model
Explain
why
ML
is
used
to
create
decision
trees
Describe
the
stages
of
the
AI
project
lifecycle
Use
an
ML
tool
to
import
data
and
train
a
model
Produce
a
model
card
to
explain
an
ML
model
Recognise
the
range
of
opportunities
that
exist
in
AI-related
careers
Evaluate
an
ML
model
by
testing
and
reviewing
the
accuracy
of
an
ML
model
Describe
the
impact
of
data
on
the
accuracy
of
an
ML
model
Describe
the
impact
of
data
on
the
accuracy
of
an
ML
model
Describe
the
impact
of
data
on
the
accuracy
of
an
ML
model
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