an AI has probably judged your resume
already hello world it's Suraj and what
does the perfect resume look like we'll
build an AI app to automatically rate
the value of a resume using advances in
natural language processing or NLP the
first generation of hiring systems
required a lot of human effort hiring
teams would need to publish their open
positions to newspapers and cable
networks interested candidates would
then apply to the position by sending in
their resumes these resumes were then
reviewed by the hiring team and the best
candidate was called in for another
round of interviews the second that
generation of hiring systems introduced
the concept of hiring agencies they
offered a solution in which the
candidate submits their resume to an
agency the agency would act as a
middlemen they would screen resumes and
send candidates to suitable positions at
different companies but now we're in the
age of a I we are reaching the third
generation in hiring systems candidates
can upload their resumes to an AI system
that uses natural language processing to
assess the capability for a specific job
a company could then use this tool to
call in the best candidates for further
interviews I've got so many rejections
but look at me now this removes the cost
for companies in hiring an agency and
results in a faster candidate pipeline
it also serves as a great tool for job
hunters rather than trying to use your
own intuition and trying to gauge
whether certain parts of your resume are
optimal you can just use a tool to
analyze your resume for you and tell you
what areas need to be improved on for
the specific job you're applying to
red score is one startup that does that
for candidates they can improve their
chances of landing a specific job by
using their tool to optimize their
resume and on the recruiting side
sovereign does the same for companies
looking to hire candidates so how do we
do this
rule-based parsers are a traditional way
of screening resumes automatically that
means that a developer will line by line
write out a set of if-else statements
that check for certain requirements in a
resume and if enough of those
requirements are met for a given preset
threshold then the resume moves on to
the next stage of the interview process
else it's tossed aside but the problem
is that there's a lot of variability and
ambiguity in the language used in
resumes there are different ways to
write dates and different job titles and
skills appear every month someone's name
could be a company name or even an IT
skill the only way for a resume parser
to deal with all of these edge cases is
to truly understand the context in which
words occur and the relationships
between them no rule is 100% reliable
except the one about Fight Club machine
learning solves this problem by
estimating the quality of these signals
based on annotated data and by combining
the evidence from several signals we can
call these signals features they are the
encoded information that is relevant for
a prediction task lots of different
machine learning techniques require us
to decide what these features are
beforehand in fact two of the most
important aspects of machine learning
models are feature extraction and
feature engineering those features are
what supply relevant information to the
ml models we can represent the word
overfitting
for example using various different
feature representation
NLP tasks like automatic summarization
and topic extraction have generally
required developers to sort their corpus
of text into different feature groups
like diagrams and trigrams if there are
too few features the model will have a
hard time making a useful prediction if
there are too many the model could be
slow and overfit stay balanced with the
force we don't really know what feature
representation is best for a given task
and in the off chance that someone does
it still relies on a human being in the
loop ideally we just want to be able to
give a raw data set to a model and have
it produce an insight without any human
help deep learning though allows these
features to be learned automatically for
image classification for example
handcrafted features were required for
any model like detecting edges with
convolutional neural networks edge
detection is instead learned by the
first convolutional layer the filters
learned by the first layer of CNN's when
given images are very reminiscent of the
bore filters which were used for edge
and texture detection traditionally and
for the next layers the filters begin to
recognize specific patterns and objects
with increasing detail all of these are
learned feature extractions as the
features become further refined as we go
up the model hierarchy the change here
is that architecture engineering became
the new feature engineering rather than
having data scientists figure out the
features for deep learning they have to
figure out the most suitable hyper
parameters for the model like the
learning rate and the filter size in a
paper from last year called domain
adaption for resume classification a
group of students from Finland
Sweden's jealous younger sibling
proposed a method for classifying resume
data of job applications into 27
different job categories using
even though CNN's are normally used for
image related tasks we found that they
can work well for text classification as
well if we think about how we would
assess a resume before anything else we
need to make sure that the resume is a
right fit for the specific role we are
hiring for once we have that we can
assess the amount of experience the
candidate has the quality of the brands
they've worked with in the past and the
quality of their past relationships so
let's start with this basic example of
automatically weeding out resumes that
aren't a fit for our specific role cover
how exactly it works then talk about
ways of improving it
collecting training data for our resume
classification use case isn't easy
resume data is difficult and costly to
obtain because it's considered sensitive
information but data about job
descriptions can be obtained much easier
the two domains constituted by resume
data and job description data however
are intrinsically related both domains
are related to the same job
recommendation task which is to match
applicants to suitable job offers the
resumes of applications have semantic
similarities with job descriptions which
belong to the same job category they
both contain skills education duties as
well as personal characteristics of the
desired candidate so it seems reasonable
to use transfer learning in order to
implement a domain adaption in order to
leverage the information contained in
the huge amount of labeled job
description data in order to classify
resume data that's exactly what they did
they collected 90,000 job description
snippets using the indeed job search API
it enables access to shortened job
summaries given a keyword the keywords
they used were 27 different industrial
job categories like accountant and inch
it's two very boring jobs for testing
the model they use an anonymized data
set of about 500 resume data samples
each sample labelled with one of the 27
categories based on the type of job the
candidate is looking for so the goal was
to leverage easily available job
description data to train a model for
classifying resume summary snippets to
better understand how the two domains
deferred they mapped outward frequencies
of all words appearing at least five
times in both data sets the results
showed that in resumes people were much
more likely to use adjectives describing
themselves as adaptable and polite
whereas job descriptions more often
mentioned roles like director and
coordinator an interesting finding the
training data consisted of lots of words
like lots and lots and lots if we
observe these words with the human eye
were able to tell which words relate to
which other words were able to
categorize certain words that's because
our brains have built an internal
representation of the intrinsic meaning
behind words so we can tell that even if
two words are spelled differently they
both have the same meaning
the way machines can do what we do is to
use vectors as that representation this
is a group of numbers an abstraction
that is learned from learning word
meaning mappings word to Veck is a pre
trained model that did this
it learned word vectors from a huge
collection of generic words using this
model when we give it a word it'll
output its vector turning words into
vectors allows us to perform at
calculations on these words I'm talking
addition subtraction numerically
defining how similar one word is to
another math is my city and converting
our words into vectors then feeding
those vectors into our CNN is an
important pre-processing step as it's a
numerical representation perfect for the
matrix operations will apply to it the
the neural network forward propagation
process so once we use were to Veck to
convert our words to vectors will build
a convolutional net work using Karros
to then learn the mapping between those
word vectors and the job a category
label the model did pretty well and
anybody can train their own on a cloud
training service like Google Cloud or
Floyd up pretty easily given a new
resume it's converted into a vector fed
into the model and the percent
likelihood that it belongs to a certain
job category is output if that percent
is over a certain threshold we
predefined than it valid for our job
this alone greatly increases the
efficiency of the hiring pipeline still
here awesome
three points to remember natural
language processing is a subset of
machine learning that lets us extract
insights from text data rather than
engineering features we can let deep
learning learn relevant features and
focus on tuning model architectures
instead and the hiring pipeline
currently requires lots of unnecessary
costly effort there's a massive massive
opportunity to use machine learning to
make the process more efficient for both
candidates and recruiters it is midterm
time for this course this week's coding
challenge is to write a script that
classifies text using a CNN details are
in the readme github links go in the
comment section and I'll announce the
top two entries next week you made it to
the end hit subscribe to be my friend
and you'll never be lonely again
for now I've got a 10 Google i/o so
thanks for watching