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Amazon Web Services Machine Learning Essential Training

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Amazon Web Services (AWS) offers a wealth of services and tools that help data scientists leverage machine learning to craft better, more intelligent solutions. In this course, learn about patterns, services, processes, and best practices for designing and implementing machine learning using AWS. Instructor Lynn Langit takes a look at general machine learning concepts, including key machine learning algorithm types. She also examines available service types, such as AWS Machine Learning, Lex, Polly, and Rekognition, which you can use to predict image and video labels. Plus, she steps through how to work with platforms like AWS SageMaker, which includes hosted Jupyter notebooks.


01 - Introduction
  • 01 - Welcome
  • 02 - About using cloud services

  • 02 - 1. Machine Learning on AWS
  • 01 - AWS Machine Learning concepts
  • 02 - Business scenarios for machine learning
  • 03 - Which algorithm should I use
  • 04 - AWS AI servers vs. platforms
  • 05 - AWS AI platforms vs. frameworks
  • 06 - A classifier in action Amazon Macie

  • 03 - 2. Machine Learning API Services
  • 01 - Setup for AWS machine learning APIs
  • 02 - Predict using AWS Comprehend for NLP
  • 03 - Predict using AWS Polly text-to-speech
  • 04 - Predict using AWS Lex for chatbots
  • 05 - Predict using AWS Rekognition for images
  • 06 - Predict using AWS Rekognition for video
  • 07 - Predict using Transcribe and Translate

  • 04 - 3. Machine Learning Platforms
  • 01 - Understanding ML platforms
  • 02 - Understanding and using AWS Machine Learning
  • 03 - Understanding SageMaker
  • 04 - Create Jupyter notebooks with SageMaker
  • 05 - Get data with SageMaker notebook
  • 06 - Train model with SageMaker job
  • 07 - Deploy and host model with SageMaker model
  • 08 - Use model from SageMaker endpoint
  • 09 - Selecting algorithm for model training
  • 10 - Advanced use of SageMaker

  • 05 - 4. Machine Learning Virtual Servers
  • 01 - Understanding ML virtual servers
  • 02 - Understanding deep learning
  • 03 - Work with Gluon for MXNet in SageMaker
  • 04 - Work with MXNet in SageMaker
  • 05 - Databricks on AWS
  • 06 - Work with MXNet in Databricks
  • 07 - Set up the AWS Deep Learning AMIs
  • 08 - Work with the AWS Deep Learning AMI
  • 09 - Work with EMR for machine learning

  • 06 - 5. Machine Learning Architectures
  • 01 - AWS ML APIs for conversational apps
  • 02 - AWS ML service for IoT apps
  • 03 - Spark ML and Databricks AWS for real-time apps
  • 04 - VariantSpark and EMR for genomic research
  • 05 - Best practices for algorithms and architectures

  • 07 - Conclusion
  • 01 - Next steps
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