Learn how to use the Scikit Learn and Keras libraries to build a linear regression model to predict a house's price in this 8-video course, and learn steps involved in preparing data and configuring regression models. Key concepts covered here include using the Pandas library to load a data set in the form of a CSV file for consumption by a linear regression model; creating training and validation sets for a regression model; and how to configure a linear regression model and train and validate it, view the metrics for the model, and visualize it by using Matplotlib. Next, learn to install the Keras library and prepare the data set for consumption by a Keras model; learn the architecture for a Keras sequential model and initialize it; and compile a Keras sequential model by defining loss function and optimizer and train it to get optimal values for weights and biases. Finally, evaluate a Keras sequential model by using it to make predictions on test data; and work with training sets and the Keras sequential model for machine learning solutions.