# 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'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.

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