AI Portfolio Project | I built a MACHINE LEARNING MODEL using AI in 10 MINUTES

Mo Chen

22 Jan 202409:58

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

📈 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.
🔑 The script illustrates a step-by-step process of building a revenue forecasting model at the customer level using pcan, without any coding involved.
🧠 pcan's AI assistant guides the user through a series of questions to understand the desired model, data requirements, and prediction targets.
📊 pcan automatically generates SQL queries, handles data processing, and constructs the core dataset (coret) required for model training.
🛠 Users can choose to train their model for faster results or better performance, with pcan's powerful engine enabling highly accurate predictions.
📈 The platform provides comprehensive model evaluation metrics, visualizations, and interpretability features to assess model performance and importance of input features.
🤖 While AI tools simplify model building, coding skills and technical expertise are still valuable for fine-tuning models and leveraging advanced capabilities.
🧮 Strong mathematical and statistical knowledge remains essential for developing robust machine learning models.
⚡ The rapid advancement of AI tools has significantly accelerated the process of building complex models, making it more accessible to a wider audience.
🌟 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

00:00

🤖 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.

05:01

🧠 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

💡Machine Learning

Machine learning is a field of artificial intelligence that enables computer systems to learn and improve from experience without being explicitly programmed. In the context of this video, machine learning refers to the process of building models that can make predictions or decisions based on data. The video demonstrates how an AI assistant can guide users through the process of building a powerful machine learning model for revenue forecasting without the need for extensive coding.

💡AI Assistant

An AI assistant is an artificial intelligence-powered software program that can understand and respond to human queries, commands, or inputs through natural language interactions. In this video, the AI assistant provided by the platform Pcan is used to guide the user through the process of building a machine learning model by asking guiding questions, analyzing data, generating SQL queries, and training the model. The AI assistant simplifies the process of creating machine learning models, making it accessible to users with varying levels of technical expertise.

💡Predictive Analytics

Predictive analytics is a branch of advanced analytics that uses statistical models, machine learning techniques, and data mining to make predictions about future outcomes or events. The video focuses on building a predictive model for revenue forecasting at the customer level, which means predicting the expected revenue for each individual customer in the future. The AI assistant guides the user through defining the predictive question, selecting the target value (revenue), and specifying the time horizon and frequency of predictions.

💡Data Set

A data set is a collection of data, often organized in a structured format such as a spreadsheet or database table. In the video, the user uploads a CSV file containing customer data, which the AI assistant analyzes and utilizes to build the machine learning model. The availability of relevant and high-quality data is crucial for training accurate and reliable predictive models.

💡Notebook

A notebook in the context of machine learning is an interactive environment that allows users to write, run, and document code, as well as visualize results. In the video, the AI assistant generates a predictive notebook that contains the SQL queries, data transformations, and modeling steps required to build the revenue forecasting model. The notebook serves as a central workspace for the user to explore and understand the model's logic and construction.

💡Target Value

The target value, also known as the dependent variable or the variable to be predicted, is the output or outcome that a machine learning model aims to forecast or classify. In the case of this video, the target value is the revenue for each customer, which the model tries to predict based on the available attributes or features in the data set.

💡Attributes

Attributes, also known as features or independent variables, are the input data or characteristics used by a machine learning model to make predictions or classifications. In the video, the AI assistant identifies the relevant attributes from the data set, such as the customer ID, transaction amount, and event time, which the model can use to find patterns and relationships that will help predict the target value (revenue).

💡Model Training

Model training is the process of feeding a machine learning algorithm with data to learn the patterns and relationships that can be used to make accurate predictions or classifications. In the video, the user has the option to choose between a faster training process or a more computationally intensive, production-quality training process that may yield better performance. The AI assistant automates the training process, taking care of data validations and model optimization.

💡Model Evaluation

Model evaluation is the process of assessing the performance and accuracy of a trained machine learning model using various metrics and visualizations. The video showcases the model evaluation tab, which provides insights into the model's precision, column importance (the most influential features), and individual predictions for each customer and month. Evaluating the model's performance is crucial for determining its reliability and identifying areas for improvement.

💡No-Code

No-code refers to the ability to build applications or models without traditional coding or programming. In the context of the video, the AI assistant provided by Pcan allows users to build a powerful machine learning model for revenue forecasting without the need for writing any code. The guided questions, data analysis, query generation, and model training are all handled by the AI assistant, making machine learning more accessible to users with varying technical backgrounds.

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