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PRACTICAL AWS SAGEMAKER | 6 REAL-WORLD CASE STUDIES

PRACTICAL AWS SAGEMAKER | 6 REAL-WORLD CASE STUDIES
Udemy Course PRACTICAL AWS SAGEMAKER | 6 REAL-WORLD CASE STUDIES  | NED

Machine Learning On Amazon Web Services (AWS), Learn all the skills you need to leverage Amazon's powerful platform for your predictive analytics needs. NEW Created by Awser Tribe English [Auto-generated] Preview this Course
by Dr. Ryan Ahmed, Ph.D., MBA, Kirill Eremenko, Hadelin de Ponteves, SuperDataScience Team

What you'll learn
  • Train and deploy AI/ML models using AWS SageMaker
  • Optimize model parameters using hyperparameters optimization search.
  • Develop, train, test and deploy linear regression model to make predictions.
  • Deploy production level multi-polynomial regression model to predict store sales based on the given features.
  • Develop a deploy deep learning-based model to perform image classification.
  • Develop time series forecasting models to predict future product prices using DeepAR.
  • Develop and deploy sentiment analysis model using SageMaker.
  • Deploy trained NLP model and interact/make predictions using secure API.
  • Train and evaluate Object Detection model using SageMaker built-in algorithms.
 Description

Machine and Deep Learning are the hottest tech fields to master right now! Machine/Deep Learning techniques are widely adopted in many fields such as banking, healthcare, transportation and technology. AWS is the one of the most widely used cloud computing platforms in the world and several companies depend on AWS for their cloud computing purposes. AWS SageMaker is a fully managed service offered by AWS that allows data scientist and AI practitioners to train, test, and deploy AI/ML models quickly and efficiently.

In this course, students will use real-world datasets to create production level AI/ML models using AWS SageMaker. Projects will cover various topics from business, healthcare, and Tech. In this course, students will be able to master many topics in a practical way such as:
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