Overview - Amazon Web Services (AWS) Tutorial

Contents

• Introduction Introduction

  • Overview

    1m 7s
• 1. Frame Business Problems as Machine Learning Problems 1. Frame Business Problems as Machine Learning Problems

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    Determine when to use and when not to use ML

    1m 53s
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    Know the difference between supervised and unsupervised learning

    2m 23s
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    Select from among classification, regression, forecasting, clustering, recommendation, and more

    1m 58s
• 2. Select the Appropriate Model(s) for a Given Machine Learning Problem 2. Select the Appropriate Model(s) for a Given Machine Learning Problem

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    Select models

    2m 1s
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    SageMaker Canvas demo

    5m 20s
• 3. Train Machine Learning Models 3. Train Machine Learning Models

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    Train validation test split, cross-validation

    1m 57s
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    Optimization

    2m 16s
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    Compute choice

    1m 13s
• 4. Perform Hyperparameter Optimization 4. Perform Hyperparameter Optimization

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    Neural network architecture

    2m 2s
• 5. Evaluate Machine Learning Models 5. Evaluate Machine Learning Models

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    Avoid overfitting and underfitting

    1m 58s
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    Select metrics

    5m 55s
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    Compare models using metrics

    1m 29s
• Conclusion Conclusion

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    Conclusion

    1m 1s