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IT Professional Curricula Internet and Network Technologies Solution Area Cloud Computing AWS Certified Machine Learning

Amazon SageMaker is a comprehensive machine learning (ML) toolkit that provides a broad range of functions and can be used for multiple use cases and tasks, making it an ultimate package for ML.
Dive deeper into SageMakerâs built-in algorithms for solving problems, such as time series forecast, clustering, and anomaly detection through this course. Examine various functionalities available in Amazon SageMaker and learn how to implement different ML algorithms.
Once you have completed this course, you'll be able to use SageMaker's machine learning algorithms for your business case and be a step further in preparing for the AWS Certified Machine Learning â Specialty certification exam.

Asset ID	it_clawsml_12_enus
Course Type	Video Course
Course Category	Internet and Network Technologies Solution Area

Objectives
AWS Certified Machine Learning: ML Algorithms in SageMaker discover the key concepts covered in this course describe SageMaker seq2seq algorithm that takes in a sequence and generates a sequence suitable for a range of tasks work with BlazingText in SageMaker to solve NLP problems, such as text classification and sentiment analysis describe how to use SageMakerâs Object2Vec algorithm that learns low dimensional embeddings of high dimensional objects outline how supervised algorithms can be used to forecast time series based on past data implement an anomaly detection system using Random Cut Forest in SageMaker outline the basics of SageMaker's Neural Topic Model and Latent Dirichlet Allocation algorithms and list their primary use cases describe the methodology behind principal component analysis (PCA) and the next level of linear learner recognize how to complete clustering tasks in SageMaker outline how to use SageMaker's most simple classification/regression algorithm named K-NN and an unsupervised algorithm to find IP usage patterns work with SageMaker to implement PCA and K-means algorithm for image clustering describe the basics and importance of reinforcement learning and Q-learning practice reinforcement learning workflow with SageMaker work with Amazon CloudWatch to analyze real-time model performance by viewing training graphs of several performance metrics summarize the key concepts covered in this course

Objectives

AWS Certified Machine Learning: ML Algorithms in SageMaker

discover the key concepts covered in this course
describe SageMaker seq2seq algorithm that takes in a sequence and generates a sequence suitable for a range of tasks
work with BlazingText in SageMaker to solve NLP problems, such as text classification and sentiment analysis
describe how to use SageMakerâs Object2Vec algorithm that learns low dimensional embeddings of high dimensional objects
outline how supervised algorithms can be used to forecast time series based on past data
implement an anomaly detection system using Random Cut Forest in SageMaker
outline the basics of SageMaker's Neural Topic Model and Latent Dirichlet Allocation algorithms and list their primary use cases
describe the methodology behind principal component analysis (PCA) and the next level of linear learner
recognize how to complete clustering tasks in SageMaker
outline how to use SageMaker's most simple classification/regression algorithm named K-NN and an unsupervised algorithm to find IP usage patterns
work with SageMaker to implement PCA and K-means algorithm for image clustering
describe the basics and importance of reinforcement learning and Q-learning
practice reinforcement learning workflow with SageMaker
work with Amazon CloudWatch to analyze real-time model performance by viewing training graphs of several performance metrics
summarize the key concepts covered in this course