BUILD YOUR MACHINE LEARNING Web-App IN MINUTES - Part 8
🚀 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: paypal.com/donate/?hosted_button_id=6ABQ3FCM9CRF2
Part 8: In this segment of our YouTube lecture series, we'll focus on training an XGBoost model using SageMaker. XGBoost is a powerful machine learning algorithm that excels in predictive modeling tasks. With SageMaker, we can easily train XGBoost models on large datasets using distributed computing power in the cloud. Join us as we walk through the process of preparing our data, configuring the XGBoost algorithm, and training our model in SageMaker. By the end of this segment, you'll have a solid understanding of how to leverage SageMaker for training XGBoost models and be ready to apply this knowledge to your own machine learning projects.
To follow along, you’ll need:
*A text editor (such as Notepad or Notepad++)
*An AWS account and access to the Console. Note: You'll need administrator permissions.
*Some basic knowledge of AWS is preferable, but you can still follow along if you’re an absolute newbie.
*Additionally, Microsoft Excel and access to an Anaconda environment for python editor are required.
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 evaluation 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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