AI Portfolio Project | I built a MACHINE LEARNING MODEL using AI in 10 MINUTES
Summary
TLDRThe video script showcases the power of AI tools like pcan in simplifying the process of building powerful machine learning models, even without coding experience. Through an interactive conversation with an AI assistant, the host walks through the steps of creating a revenue forecasting model at the customer level, from data upload and analysis to model training and evaluation. The process is remarkably streamlined, demonstrating how AI has lowered the barriers to learning and utilizing advanced technologies. However, the host emphasizes that while AI tools have made model building more accessible, coding skills and technical expertise remain invaluable for fine-tuning and optimizing models.
Takeaways
- ๐ AI-powered tools like pcan have significantly lowered the barrier to building machine learning models, allowing even beginners to create powerful models with minimal coding.
- ๐ The script illustrates a step-by-step process of building a revenue forecasting model at the customer level using pcan, without any coding involved.
- ๐ง pcan's AI assistant guides the user through a series of questions to understand the desired model, data requirements, and prediction targets.
- ๐ pcan automatically generates SQL queries, handles data processing, and constructs the core dataset (coret) required for model training.
- ๐ Users can choose to train their model for faster results or better performance, with pcan's powerful engine enabling highly accurate predictions.
- ๐ The platform provides comprehensive model evaluation metrics, visualizations, and interpretability features to assess model performance and importance of input features.
- ๐ค While AI tools simplify model building, coding skills and technical expertise are still valuable for fine-tuning models and leveraging advanced capabilities.
- ๐งฎ Strong mathematical and statistical knowledge remains essential for developing robust machine learning models.
- โก The rapid advancement of AI tools has significantly accelerated the process of building complex models, making it more accessible to a wider audience.
- ๐ pcan demonstrates the potential of AI-powered tools to democratize machine learning and empower users of all skill levels to create impactful models efficiently.
Q & A
What is the main topic of the video script?
-The script discusses how to build a powerful machine learning model with no actual coding involved, using an AI assistant tool called Pcan.
What kind of machine learning model is being built in the example?
-The example demonstrates building a revenue forecasting model at the customer level, predicting the revenue for each customer on a monthly basis.
What data set is used for building the machine learning model?
-The data set used is a cleaned-up Kaggle competition file, the link for which is provided in the video description.
How does the Pcan AI assistant guide the model building process?
-The Pcan AI assistant asks guiding questions to understand the requirements, recommends data column mappings, generates SQL queries, and constructs the core data set for training the model.
What is the significance of the 'attribute' section in the generated notebook?
-The 'attribute' section contains the data that the model will use to identify and find patterns to produce the correct target values (in this case, revenue predictions).
What are the two training options provided by Pcan, and what is the difference between them?
-The two training options are 'fastest' (which takes 10-30 minutes) and 'production quality' (which takes longer but provides better performance). The script mentions that the production quality option took about an hour to train the model.
How does the script evaluate the performance of the trained machine learning model?
-The script evaluates the model's performance using various metrics and visualizations provided by Pcan, such as model precision, column importance, and model output (predictions for each user and month).
What is the author's view on the importance of coding skills in the age of AI-assisted model building?
-The author acknowledges that while AI tools can simplify model building, coding and technical skills are still very important for fine-tuning models and understanding the underlying math and statistics.
What is the main advantage of using AI tools like Pcan for building machine learning models, according to the script?
-The main advantage highlighted is that AI tools like Pcan lower the barrier to learning and building machine learning models, allowing users to create powerful models quickly, even with limited coding experience.
What is the author's overall impression of the Pcan tool's capabilities?
-The author seems impressed by Pcan's capabilities, describing the process as "AI magic" and expressing amazement at how AI can simplify complex tasks like building strong machine learning models through conversational interactions.
Outlines
๐ค Revolutionizing Machine Learning with AI Assistants
The narrator is amazed by the ability to build powerful machine learning models simply by conversing with an AI assistant, a stark contrast from the difficulties faced earlier in their career. They highlight how AI tools and services have lowered the barrier to learning, allowing for quicker knowledge acquisition. In this video, the narrator aims to demonstrate the simplest way to build a robust machine learning model without coding, using the AI assistant Pcan, which has sponsored the video.
๐ง Step-by-Step Guide to Building a Revenue Forecasting Model
The narrator walks through the process of building a revenue forecasting model using Pcan's AI assistant. They answer guided questions to specify the prediction task, target value, and time horizon. Pcan generates a predictive analytics question and assists in data connection and schema mapping. The narrator explains the concept of the 'coret' (training data) and attributes (data used for pattern recognition). Pcan constructs SQL queries, handles data sampling, and provides options for model training. The narrator evaluates the model's performance using metrics like precision, column importance, and model output.
Mindmap
Keywords
๐กMachine Learning
๐กAI Assistant
๐กPredictive Analytics
๐กData Set
๐กNotebook
๐กTarget Value
๐กAttributes
๐กModel Training
๐กModel Evaluation
๐กNo-Code
Highlights
I can, build a powerful machine learning model, nowadays just by chatting to an AI, assistant
With AI, tools and services on the rise the, barrier to learning anything really has, been lowered or at the very least you, can learn whatever you want quicker if, you know what tools to use and how to, use them in the most efficient way
I'll show you the simplest, way I know to build a strong machine, learning model with no actual coding, involved
The way the, assistant works is that it asked me some, guiding questions to help me build my, machine learning model
Pcan generated the Predictive Analytics, question for me all I had to do was to, confirm that everything still looked, good and I moved on to connecting to my, data
Pcan is um capable of, processing lots and lots of information, as it uses data bricks so you should, feel confident about loading massive, data sets
The AI assistant, looked at the schema of the file and, recommended the column mappings of the, data set based on the schema
Pcan, did all the heavy lifting for me in the, background so now all I had to do was to, click on generate notebook and then go, to it to see what's actually in there
Think of the predictive notebook that I, just generated as the brains or the, control room of the entire model, building process
Pcan's AI, assistant constructed the core set for, me by using the answers I provided to, the guided questions
An attribute is the, data that my model will use to identify, and find the patterns that will then, tell my my model what the predictions, will be
Pcan also has, the option to add more attributes if I, wanted to so I could have easily added, more by adding more data
I hit run all which ran all of, my SQL queries, then I trained my model
Pcan quickly rans some data, valid ations to check that my data was, actually fit for building a predictive, model and that was it I sat back and, waited for about an hour and I had my, Model results with some cool metrics and, visualizations
It is still, mindboggling to me that it allows me to, do such difficult tasks like building a, strong machine learning model just by, interacting with an AI chat assistant
Transcripts
I truly think it's insane that I can
build a powerful machine learning model
nowadays just by chatting to an AI
assistant I remember when I first
started my career doing Predictive
Analytics was hard really hard with AI
tools and services on the rise the
barrier to learning anything really has
been lowered or at the very least you
can learn whatever you want quicker if
you know what tools to use and how to
use them in the most efficient way in
today's video I'll show you the simplest
way I know to build a strong machine
learning model with no actual coding
involved I used pcan who have kindly
also sponsored this video but as always
all opinions thoughts and Reflections on
the tool will of course be absolutely of
my own whether you're a machine learning
newbie or have some or maybe lots and
lots of experience training your own
models I'd encourage you to stick around
as you'll get to see how simple and
effective it can be to utilize the
latest Technologies to build build your
own machine learning models regardless
of your experience and I say regardless
of experience because even though you
can build precise models with zero
coding involved you can of course adjust
your code within pcan I'll show you
later how so I'll walk you through how I
built a revenue forecasting model but
you could obviously predict other things
like LTV customer turn or winback
marketing mix or how to up and cross
sell you can try and build your own
model the same way by the way ass
signing up to pcan is completely free
I'll put the link in the description
below along with the link to the exact
data set that I used so after I logged
in straight away I got to interact with
the pcan AI assistant the way the
assistant works is that it asked me some
guiding questions to help me build my
machine learning model so I answered the
first question and told the AI assistant
that I would like a revenue forecast
model at the customer level this simply
means that I want to predict what the
revenue is for each and every customer
the assistant is pretty smart as it
recommended the subject of my prediction
which was the customer of course and my
target value which was the revenue and
all I had to do was to confirm that all
was good so far then I got another
guiding question asking me how far into
the future I'd like to predict the
revenue for each customer you can choose
whatever you want really next week month
quarter or even year but I went with
probably the most popular future time
Horizon a month year then the tool asked
me on what recurring basis I'd like to
make these Revenue predictions again I
went with something simplistic here so I
said that I'd like the frequency of the
predictions to be monthly I also had the
options to trigger my predictions based
on specific events for example when a
customer makes a purchase but for this
exercise for this model I chose to go
with predicting on a monthly basis then
pcan generated the Predictive Analytics
question for me all I had to do was to
confirm that everything still looked
good and I moved on to connecting to my
data I uploaded my CSV file which is
actually a kaggle competition file that
was cleaned up a bit and as I said
before the link is in the description
below so you can go ahead and download
it and build your own machine learning
model now of course pcon has a bunch of
connectors when it comes to connecting
to your data sets so if you have
something sitting on SQL servers or with
popular Cloud providers that's not a
problem at all pcan is um capable of
processing lots and lots of information
as it uses data bricks so you should
feel confident about loading massive
data sets after the file upload was
complete I simply sent it to the chat
and let the tool do its work with
analyzing the data the AI assistant
looked at the schema of the file and
recommended the column mappings of the
data set based on the schema for each
column type I quickly saw what the data
type was I confirmed that everything
looked good and moved on this time the
tool actually figured it out that the
user ID column was the customer ID the
amount represented the revenue for each
transaction and the event time was the
date or timestamp that I needed again I
confirmed that everything looked good
and then I got a summary with my
predictive question the schema my Target
table Target value column Target table
date column and the target entity pcan
did all the heavy lifting for me in the
background so now all I had to do was to
click on generate notebook and then go
to it to see what's actually in there
think of the predictive notebook that I
just generated as the brains or the
control room of the entire model
building process I had my SQL queries in
here that the AI assistant built for me
you can also see that all of the steps
were clearly explained each query had a
name and was actually saved kind of like
a view so then the next query or sell
could utilize the data from the query
above for example the sampled customers
table could utilize the information from
the monthly sampling table so Pan's AI
assistant constructed the core set for
me by using the answers I provided to
the guided questions now what do I mean
by a coret it is the data that allowed
my model to to know what it needs to
learn and where it needs to learn from
so the coret was a final table that
consisted of all users sample dates and
the target value which in my case was
the revenue within 1 month after the
sample date now the last thing in my
notebook was the attribute section and
if you have no idea what this is don't
worry as I'm going to explain this in a
very simple way and attribute is the
data that my model will use to identify
and find the patterns that will then
tell my my model what the predictions
will be so when the model is training
it's learning the patterns to produce
the correct Target values long story
short use the training set to predict
your Target on an ongoing basis then use
your model in real life pcan also has
the option to add more attributes if I
wanted to so I could have easily added
more by adding more data whether it was
just me uploading some files or
connecting to one of the many databases
that are supported now of of course if I
added more data I could have just used
the AI assistant to replace my already
existing query so that it runs on my new
data set again no coding involved just
pure AI magic I wanted to keep my model
nice and light so I chose not to add any
more attributes I just went with the one
I already had my notebook had everything
I needed to train my model using the
data set I uploaded into the chat
earlier I hit run all which ran all of
my SQL queries
then I trained my model now I had two
options fastest which is by no surprise
fast as it only takes about 10 to 30
minutes to train a model and production
quality which provides better
performance but takes a little longer to
train I know it says several hours here
but my model was actually built in about
an hour or so the time it takes will of
course depend on how complex your
machine learning model is so once I hit
train model P can quickly rans some data
valid ations to check that my data was
actually fit for building a predictive
model and that was it I sat back and
waited for about an hour and I had my
Model results with some cool metrics and
visualizations that helped me to gauge
how good my model actually was I could
see on the model evaluation tab under
model performance that my model was very
precise meaning the model was very close
to the actual values the pan platform
has a really powerful engine behind it
hence the really good result results
clearly their data teams know what
they're doing now the model evaluation
tab has a bunch of other metrics as well
and I won't go through all of them but I
will highlight column importance which
was very useful to know in my model I
could see that the amount column was by
far the top contributing column to the
model predictions which makes sense
another helpful tab I used for
interpreting my Model results was the
model output tab I could quickly see
what the prediction was for each user
for each month I'm getting more and more
used to how much and how fast AI can do
things but at times it is still
mindboggling to me that it allows me to
do such difficult tasks like building a
strong machine learning model just by
interacting with an AI chat assistant
now does this mean that coding is
useless absolutely not the ability to
read write understand and interpret code
is still very very important in my
opinion just think of this who can
develop the better machine learning
model in a shorter time frame the person
who knows how to use the latest AI tools
but has no real technical expertise or
the person who also knows how to use the
latest AI tools and has extremely strong
technical skills surely the person with
the strong technical skills will be able
to fine-tune the model better and faster
using the same AI tools compared to the
person with little technical expertise
not to mention all of the underlying
math and statistical knowledge that is
of course also essential to building
good machine learning models anyway I'm
going to end this video here if you
enjoy content like this make sure to
check out some of my other videos right
here thank you so much for watching and
I shall see you in the next
[Music]
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