IT Professional Certifications     Amazon     AWS Certified Machine Learning – Specialty     AWS Certified Machine Learning – Specialty

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Amazon SageMaker provides broad-set capabilities for machine learning (ML) as it helps to prepare, train, and quickly deploy ML models. Use this course to learn more about the basic capabilities of SageMaker and work with it to implement solutions to various machine learning problems.
Explore features and functionalities of SageMaker through practical demos and discover how to implement hyperparameter tuning. This course will also help you explore algorithms in SageMaker, such as linear learner, XGBoost, object detection, and semantic segmentation.
After completing this course, you'll be able to train and tune a range of algorithms in order to solve simple classification tasks for natural language processing (NLP) and computer vision.

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Objectives

AWS Certified Machine Learning: Machine Learning in SageMaker discover the key concepts covered in this course describe the features and capabilities of Amazon SageMaker work with the basic features of Amazon SageMaker work with common SageMaker Studio tasks, such as clone a git repository, upload files, and stop training jobs build machine learning (ML) solutions by selecting existing resources and launch them with a single click in SageMaker Studio outline how to use Linear Learner and XGBoost (eXtreme Gradient Boosting) for classification and regression problems build and train an image classification model in SageMaker describe how object detection algorithms built on top of VGG and ResNet work to predict the objects present in the image and their confident score recognize the use of SageMaker’s semantic segmentation algorithm to predict the class of each pixel in an image and get shapes of objects describe hyperparameter tuning jobs in SageMaker and name recommended practices create a SageMaker notebook to train and finetune an object detection algorithm summarize the key concepts covered in this course