BUILD YOUR MACHINE LEARNING Web-App IN MINUTES - Part 9
🚀 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...
Support us:
https://paypal.com/donate/?hosted_but...
Music Credit: Free Piano Music For YouTube - "Purpose" by Jonny Easton
Part 9: In this section of our tutorial series, we'll dive into model fitting and evaluation metrics such as precision, recall, and accuracy. Model fitting involves tuning the parameters of our machine learning algorithms to best fit our data. We'll explore how to fine-tune our models for optimal performance and learn how to interpret the results using evaluation metrics like precision, recall, and accuracy. These metrics help us assess the quality of our models and understand how well they're performing on our dataset. By the end of this section, you'll have a solid grasp of model evaluation techniques and be able to assess the performance of your own machine learning models effectively.
Relevant files:
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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