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