AWS Certified Machine Learning: ML Algorithms in SageMaker

Amazon Web Services 2021    |    Intermediate
  • 15 Videos | 1h 43m 11s
  • Includes Assessment
  • Earns a Badge
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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 SageMaker’s 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 – Specialty certification exam.

WHAT YOU WILL LEARN

  • 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 SageMaker’s 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

IN THIS COURSE

  • Playable
    1. 
    Course Overview
    1m 52s
    UP NEXT
  • Playable
    2. 
    SageMaker Sequence-to-sequence Algorithm
    5m 11s
  • Locked
    3. 
    Working with BlazingText in SageMaker
    8m 48s
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    4. 
    Object to Vector (Object2Vec) in SageMaker
    6m 20s
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    5. 
    DeepAR Forecasting in SageMaker
    7m 26s
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    6. 
    Working with Random Cut Forest (RCF) in SageMaker
    7m 23s
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    7. 
    Topic Modelling in SageMaker
    7m 7s
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    8. 
    PCA and Factorization Machine in SageMaker
    6m 58s
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    9. 
    K-means Clustering in SageMaker
    4m 22s
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    10. 
    K-NN and IP Insights in SageMaker
    9m 49s
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    11. 
    Using Image Clustering in SageMaker
    7m 10s
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    12. 
    Fundamentals of Reinforcement Learning
    7m 41s
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    13. 
    Implementing Reinforcement Learning in SageMaker
    9m 13s
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    14. 
    Monitoring & Analyzing Training Jobs using Metrics
    6m 14s
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    15. 
    Course Summary
    1m 7s

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