Linear Regression Models: Building Models with Scikit Learn & Keras

Machine Learning    |    Beginner
  • 9 videos | 41m 3s
  • Includes Assessment
  • Earns a Badge
Rating 4.1 of 22 users Rating 4.1 of 22 users (22)
Learn how to use the Scikit Learn and Keras libraries to build a linear regression model to predict a house price. This course reviews the steps needed to prepare data and configure regression models. It shows how to prepare a data set to feed a linear regression model; how to use the Pandas library to load a CSV data set file; and how to configure, train, and validate linear regression models. The course also shows how to visualize metrics with Matplotlib; how to prepare data for a Keras model, how to learn the architecture for a Keras sequential model and initialize it; and finally, how train it to use optimal weights and biases for machine learning solutions.

WHAT YOU WILL LEARN

  • Use the pandas library to load a dataset in the form of a csv file into a dataframe for consumption by a linear regression model
    Create training and validation sets for your regression model
    Configure a linear regression model and then train and validate it and view the metrics for the model and visualize it using matplotlib
    Install the keras library and prepare the dataset for consumption by a keras model
  • Define the architecture for a keras sequential model and initialize it
    Compile a keras sequential model by defining the loss function and optimizer and train it to get the optimal values for weights and biases
    Evaluate a keras sequential model by using it to make predictions on test data
    Work with training sets and the keras sequential model

IN THIS COURSE

  • 2m 32s
  • 4m 32s
    In this video, you will use the Pandas library to load a dataset in the form of a CSV file into a Dataframe for consumption by a linear regression model. FREE ACCESS
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    3.  Splitting a Dataset for Training and Validation
    4m 16s
    In this video, you will create training and validation sets for your regression model. FREE ACCESS
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    4.  Keras Installation
    4m 38s
    Learn how to configure a linear regression model, train and validate it, view the metrics for the model, and visualize it using Matplotlib. FREE ACCESS
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    5.  Training and Evaluating a Model
    6m 18s
    Find out how to install the Keras library and prepare the dataset to be used by a Keras model. FREE ACCESS
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    6.  Building a Sequential Model
    4m 10s
    Find out how to define the architecture for a Keras sequential model and initialize it. FREE ACCESS
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    7.  Training a Neural Network
    5m 35s
    Learn how to compile a Keras sequential model by defining the loss function and optimizer. Train it to get the optimal values for weights and biases. FREE ACCESS
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    8.  Evaluating a Neural Network
    3m 15s
    To evaluate a Keras sequential model, use it to make predictions on test data. FREE ACCESS
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    9.  Exercise: Building Simple Regression Models
    5m 47s
    Learn how to work with training sets and the Keras sequential model. FREE ACCESS

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