# Linear Regression Models: Multiple & Parsimonious

Machine Learning    |    Intermediate
• 12 videos | 1h 10m 12s
• Includes Assessment
Rating 4.8 of 10 users (10)
Several factors usually influence an outcome, and users need to consider all of those by using regression. Regression models help us mathematically evaluate our hunches. This course explores machine learning techniques and the risks involved with multiple factor linear regression. Key concepts covered here include reasons to use multiple features in a regression, and how to configure, train, and evaluate the linear regression model. Next, learn to create a data set with multiple features in a form that can be fed to a neural network for training and validation. Review Keras sequential model architecture, its training parameters, and ways to test its predictions. Learn how to use Pandas and Seaborn to view correlations and enumerate risks. Conclude by applying parsimonious regression to rebuild linear regression models.

## WHAT YOU WILL LEARN

• Identify the reasons to use multiple features when doing a regression and the technique involved in creating such a multiple regression model
Prepare a dataset containing multiple features to used for training and evaluating a linear regression model
Configure, train and evaluate the linear regression model which makes predictions from multiple input features
Create a dataset with multiple features in a form which can be fed to a neural network for training and validation
Define the architecture for a keras sequential model and set the training parameters such as loss function and optimizer
Make predictions on the test data and examine the metrics to gauge the quality of the neural network model
• Use pandas and seaborn to visualize correlations in a dataset and identify features which convey similar information
Identify the risks involved with multiple regression and the need to select features carefully
Apply the principle of parsimonious regression to re-build the linear regression model and compare the results with the kitchen sink approach
Build a keras model after selecting only the important features from a dataset
Encode categorical integers for ml algorithms as well as use pandas and seaborn to view correlations, and enumerate risks

## IN THIS COURSE

• Learn how to identify the reasons to use multiple features when doing a regression and the technique involved in creating such a multiple regression model.
• 3.  Kitchen Sink Regression
Find out how to prepare a dataset containing multiple features to be used for training and evaluating a linear regression model.
• 4.  Training and Evaluating the Model
In this video, you will learn how to configure, train, and evaluate the linear regression model, which makes predictions from multiple input features.
• 5.  Preparing Data for a Neural Network
Learn how to create a dataset with multiple features in a form which can be fed to a neural network for training and validation.
• 6.  Building a Neural Network
Find out how to define the architecture for a Keras sequential model and set training parameters such as the loss function and optimizer.
• 7.  Evaluating the Neural Network
In this video, you will make predictions on the test data and examine the metrics to gauge the quality of the neural network model.
• 8.  Finding Correlations in a Dataset
Find out how to use Pandas and Seaborn to visualize correlations in a dataset and identify features that convey similar information.
• 9.  Introducing Parsimonious Regression
In this video, you will identify the risks involved with multiple regression and the need to select features carefully.
• 10.  Applying Parsimonious Regression with Scikit Learn
In this video, you will learn how to apply the principle of parsimonious regression to rebuild the Linear Regression model and compare the results with the kitchen sink approach.
• 11.  Applying Parsimonious Regression with Keras
In this video, learn how to build a Keras model by selecting only the important features from a dataset.
• 12.  Exercise: Multiple Linear Regression
Find out how to encode categorical integers for ML algorithms, as well as how to use Pandas and Seaborn to view correlations and enumerate risks.

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