This course teaches learners how to build, train, and deploy machine learning models using AWS SageMaker. By the end of the course, students will be able to create a notebook instance, set up S3 buckets, upload files, train Xgboost ML algorithms, deploy ML models, and make predictions on test data endpoints. The teaching method involves a series of tutorials demonstrating each step in the process. This course is intended for individuals interested in machine learning and deep learning, particularly those looking to utilize AWS SageMaker for their projects.
Overview
Syllabus
AWS SageMaker For ML And DL Tutorial Playlist- What Will We Learn In This Playlist?.
Tutorial 2-Build,Train, Deploy Machine Learning Model In AWS SageMaker- Creating Notebook Instance.
Tutorial 3-Build,Train, Deploy Machine Learning Model In AWS SageMaker- Creating S3 Buckets.
Tutorial 4-Build,Train, Deploy Machine Learning Model In AWS SageMaker- Uploading Files In S3 Bucket.
Tutorial 5-Build,Train, Deploy Machine Learning Model In AWS SageMaker-Training Xgboost ML ALgo.
Tutorial 6-Build,Train,Deploy Machine Learning Model AWS SageMaker- Deployment Of Xgboost ML Model.
Tutorial 7-Build,Train,Deploy Machine Learning Model AWS SageMaker- Predicting Test Data Endpoints.
Taught by
Krish Naik
