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LinkedIn Learning

Amazon Web Services Machine Learning Essential Training

via LinkedIn Learning

Overview

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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

Reviews

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