Applied Machine Learning: Value Estimation

Contents

• Introduction Introduction

  • Worth a look: The power of value estimation

    47s
  • What you should know

    43s
  • How to use Codespaces

    3m 38s
• 1. Introducing Value Estimation with Machine Learning (ML) 1. Introducing Value Estimation with Machine Learning (ML)

  • Overview of value estimation

    32s
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    Data exploration and cleaning

    12m 11s
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    Challenge: Load, explore, and clean data

    41s
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    Solution: Load, explore, and clean data

    9m 52s
• 2. Building a Linear Regression Model 2. Building a Linear Regression Model

  • Overview of linear regression models

    1m 57s
  • Train model: Linear regression

    11m 19s
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    Evaluate model: Linear regression

    8m 51s
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    Make predictions: Linear regression

    2m 44s
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    Challenge: Implement a linear regression model

    1m 8s
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    Solution: Implement a linear regression model

    13m 36s
• 3. Building an XGBoost Model 3. Building an XGBoost Model

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    Overview of XGBoost models

    2m 59s
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    Train a model: XGBoost

    3m 20s
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    Evaluate a model: XGBoost

    9m 36s
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    Make predictions: XGBoost

    47s
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    Challenge: Implement an XGBoost model

    22s
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    Solution: Implement an XGBoost model

    10m 47s
• 4. Deployment 4. Deployment

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    MLflow overview

    1m 48s
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    Serving the model

    3m 33s
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    Challenge: Deploy the XGBoost model

    37s
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    Solution: Deploy the XGBoost model

    9m 42s
• Conclusion Conclusion

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    Next steps in your machine learning value estimation journey

    52s