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LinkedIn Learning

Amazon Web Services Machine Learning Essential Training

via LinkedIn Learning

Overview

Learn about patterns, services, processes, and best practices for designing and implementing machine learning using Amazon Web Services.

Syllabus

Introduction
  • Welcome
  • About using cloud services
1. Machine Learning on AWS
  • AWS Machine Learning concepts
  • Business scenarios for machine learning
  • Which algorithm should I use?
  • AWS AI servers vs. platforms
  • AWS AI platforms vs. frameworks
  • A classifier in action: Amazon Macie
2. Machine Learning API Services
  • Setup for AWS machine learning APIs
  • Predict using AWS Comprehend for NLP
  • Predict using AWS Polly text-to-speech
  • Predict using AWS Lex for chatbots
  • Predict using AWS Rekognition for images
  • Predict using AWS Rekognition for video
  • Predict using Transcribe and Translate
3. Machine Learning Platforms
  • Understanding ML platforms
  • Understanding and using AWS Machine Learning
  • Understanding SageMaker
  • Create Jupyter notebooks with SageMaker
  • Get data with SageMaker notebook
  • Train model with SageMaker job
  • Deploy and host model with SageMaker model
  • Use model from SageMaker endpoint
  • Selecting algorithm for model training
  • Advanced use of SageMaker
4. Machine Learning Virtual Servers
  • Understanding ML virtual servers
  • Understanding deep learning
  • Work with Gluon for MXNet in SageMaker
  • Work with MXNet in SageMaker
  • Databricks on AWS
  • Work with MXNet in Databricks
  • Set up the AWS Deep Learning AMIs
  • Work with the AWS Deep Learning AMI
  • Work with EMR for machine learning
5. Machine Learning Architectures
  • AWS ML APIs for conversational apps
  • AWS ML service for IoT apps
  • Spark ML and Databricks AWS for real-time apps
  • VariantSpark and EMR for genomic research
  • Best practices for algorithms and architectures
Conclusion
  • Next steps

Taught by

Lynn Langit

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4.6 rating at LinkedIn Learning based on 287 ratings

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