Course details

Linear Regression Models: Building Simple Regression Models with Scikit Learn and Keras

Linear Regression Models: Building Simple Regression Models with Scikit Learn and Keras


Overview/Description
Expected Duration
Lesson Objectives
Course Number
Expertise Level



Overview/Description
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.

Expected Duration (hours)
0.7

Lesson Objectives

Linear Regression Models: Building Simple Regression Models with Scikit Learn and Keras

  • Course Overview
  • 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
  • Course Number:
    it_mllrmddj_02_enus

    Expertise Level
    Beginner