Using BigML: Building Supervised Learning Models

Machine Learning 2020
  • 14 Videos | 1h 36m
  • Includes Assessment
  • Earns a Badge
The versatility of BigML allows you to build supervised learning models without much complexity. In this course, you'll practice constructing a selection of supervised learning models using BigML. You'll start by building an ensemble of decision trees to perform binary classification. Next, you'll build a linear regression model to predict the values of homes in a particular region. You'll then train and evaluate a logistic regression model to illustrate how it can be used to solve similar problems to those solved using ensemble methods. Another BigML capability you'll explore is building a time series plot to make various forecasts. In each demonstration, you'll delve into some optional configurations for the model being trained. Lastly, you'll use the OptiML feature to find the optimal model for your data.

WHAT YOU WILL LEARN

  • discover the key concepts covered in this course
    use BigML to build an ensemble of decision trees to solve a classification problem
    recognize the properties of ensemble models configured in BigML
    compare the performance of a small ensemble with a larger one
    prepare a dataset for use in a linear regression model
    build a linear regression model and identify the relationships it uncovers between the input variables and the output
    recognize the different factors involved in evaluating a linear regression model
  • describe the process of preparing a dataset for logistic regression
    train a logistic regression model to predict an output based on probability of occurrence
    check the performance of a logistic regression model using a test data
    create a time series model using several years' worth of data
    apply past data to make future forecasts using a time series model
    apply a brute-force approach to find the optimal model for your dataset
    summarize the key concepts covered in this course

IN THIS COURSE

  • Playable
    1. 
    Course Overview
    2m 38s
    UP NEXT
  • Playable
    2. 
    Building an Ensemble Model
    8m 56s
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    3. 
    Configuring an Ensemble
    9m 20s
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    4. 
    Creating a Large Ensemble
    3m 47s
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    5. 
    Building a Dataset for Linear Regression
    6m 42s
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    6. 
    Training and Analyzing a Linear Regression Model
    8m 8s
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    7. 
    Using and Evaluating a Linear Regression Model
    5m 33s
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    8. 
    Preparing a Dataset for Logistic Regression
    8m 56s
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    9. 
    Building a Logistic Regression Model
    8m 2s
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    10. 
    Evaluating a Logistic Regression Model
    4m 3s
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    11. 
    Generating a Time Series
    8m 10s
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    12. 
    Forecasting with and Evaluating a Time Series
    5m 39s
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    13. 
    Using OptiML to Find the Best Model
    8m 26s
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    14. 
    Course Summary
    1m 38s

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