Linear Regression Models: Multiple & Parsimonious

Machine Learning    |    Intermediate
  • 12 Videos | 1h 10m 12s
  • Includes Assessment
  • Earns a Badge
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Several factors usually influence an outcome, and users need to consider all of those by using regression. Regression models help us mathematically evaluate our hunches. This course explores machine learning techniques and the risks involved with multiple factor linear regression. Key concepts covered here include reasons to use multiple features in a regression, and how to configure, train, and evaluate the linear regression model. Next, learn to create a data set with multiple features in a form that can be fed to a neural network for training and validation. Review Keras sequential model architecture, its training parameters, and ways to test its predictions. Learn how to use Pandas and Seaborn to view correlations and enumerate risks. Conclude by applying parsimonious regression to rebuild linear regression models.

WHAT YOU WILL LEARN

  • identify the reasons to use multiple features when doing a regression and the technique involved in creating such a multiple regression model
    prepare a dataset containing multiple features to used for training and evaluating a linear regression model
    configure, train and evaluate the linear regression model which makes predictions from multiple input features
    create a dataset with multiple features in a form which can be fed to a neural network for training and validation
    define the architecture for a Keras sequential model and set the training parameters such as loss function and optimizer
    make predictions on the test data and examine the metrics to gauge the quality of the neural network model
  • use Pandas and Seaborn to visualize correlations in a dataset and identify features which convey similar information
    identify the risks involved with multiple regression and the need to select features carefully
    apply the principle of parsimonious regression to re-build the Linear Regression model and compare the results with the kitchen sink approach
    build a Keras model after selecting only the important features from a dataset
    encode categorical integers for ML algorithms as well as use Pandas and Seaborn to view correlations, and enumerate risks

IN THIS COURSE

  • Playable
    1. 
    Course Overview
    2m 20s
    UP NEXT
  • Playable
    2. 
    Understanding Multiple Regression
    7m 45s
  • Locked
    3. 
    Kitchen Sink Regression
    6m 32s
    FREE ACCESS
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    4. 
    Training and Evaluating the Model
    4m 55s
    FREE ACCESS
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    5. 
    Preparing Data for a Neural Network
    6m 17s
    FREE ACCESS
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    6. 
    Building a Neural Network
    6m 56s
    FREE ACCESS
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    7. 
    Evaluating the Neural Network
    4m 6s
    FREE ACCESS
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    8. 
    Finding Correlations in a Dataset
    2m 34s
    FREE ACCESS
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    9. 
    Introducing Parsimonious Regression
    8m 54s
    FREE ACCESS
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    10. 
    Applying Parsimonious Regression with Scikit Learn
    5m 28s
    FREE ACCESS
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    11. 
    Applying Parsimonious Regression with Keras
    8m 50s
    FREE ACCESS
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    12. 
    Exercise: Multiple Linear Regression
    5m 34s
    FREE ACCESS

EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE

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