BUILD YOUR MACHINE LEARNING Web-App IN MINUTES - Part 7
🚀 Welcome to our epic series! Get ready to dive into building a machine learning web app, no matter your skill level! We'll harness the power of Amazon SageMaker, AWS Amplify, and more to make our app shine bright! Our mission? To guide you through the essentials of ML-focused AWS tools integration and web development, step by step. Ready to start your journey? Let's go! 🔥 Here are few key links:
https://aws.amazon.com
https://www.anaconda.com/download
https://www.kaggle.com/datasets/bsugi...
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Part 7: In this segment of our YouTube lecture series, we'll focus on writing Python code in a SageMaker notebook. SageMaker notebooks offer a user-friendly interface for developing and testing machine learning models in the cloud. With SageMaker, you can write and execute Python code seamlessly, allowing you to preprocess data, train models, and evaluate their performance all within a single environment. Join us as we dive into the functionalities of SageMaker notebooks and learn how to harness their capabilities to build powerful machine learning applications.
Below is the complete code and raw data, necessary for constructing the application:
Loan Data file:
https://drive.google.com/drive/folder...
Python program for model test in local machine:
https://drive.google.com/drive/folder...
SageMaker notebook:
https://drive.google.com/drive/folder...
Lambda function file including JSON text file:
https://drive.google.com/drive/folder...
Amplify HTML file:
https://drive.google.com/drive/folder...
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