How To Build A Machine Learning Model With No-Code (Levity X Bubble)

Описание: Looking to build an AI model that can integrate with your Bubble.io app?

In this video, I'll walk you through the step-by-step process to building your own image classification model without writing a single line of code.

Using a combination of both Bubble and Levity, you’ll learn how to create an AI model that can analyse objects within an image.

In our example, we’ll create a car insurance website where customers can upload an image of their damaged car. From there, our AI model will then classify what type of damage the car has received, before labelling it in our database.

🔥 Throughout the full video, you’ll learn:

1. How to download and review a training dataset
2. How to upload and label your training data within Levity’s no-code platform
3. How to create and train a machine learning model in Levity
4. How to test and deploy your AI model
5. How to connect your AI model with Bubble
6. How to filter insurance claims by the type of damage a car has recieved

🚀 Create a Levity account here: https://levity.ai/

📑 Download the customer review training dataset here: https://drive.google.com/file/d/1cqaP...

✍️ Access the working Notion doc here: https://lachlan-kirkwood.notion.site/...

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👨‍💻 About me:

In 2020, I worked directly with the Bubble team to write their 'How-to build' blog series. Each installment shared written instructions on how to recreate popular products using their no-code tool. Since the beginning of 2021, I've focussed on creating my own video series to teach no-code makers how to get the most out of Bubble's platform.

Note: This video series has no direct affiliation with Bubble themselves. This is something I've created on my own.

Disclaimer: This video was created as a paid partnership with Levity.

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⏱ Timeline:

00:00 - Product demo
01:15 - Downloading your dataset
06:00 - Creating a Levity account
08:57 - Importing your dataset
10:06 - Training your machine learning model
18:29 - Connect your AI model to Bubble
34:40 - Testing the AI model
37:33 - Filtering results by AI-generated labels
42:49 - Additional insights