Linear Regression Models: Multiple & Parsimonious
Machine Learning
| Intermediate
- 12 videos | 1h 10m 12s
- Includes Assessment
- Earns a Badge
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
-
2m 20s
-
7m 45sLearn how to identify the reasons to use multiple features when doing a regression and the technique involved in creating such a multiple regression model. FREE ACCESS
-
3. Kitchen Sink Regression6m 32sFind out how to prepare a dataset containing multiple features to be used for training and evaluating a linear regression model. FREE ACCESS
-
4. Training and Evaluating the Model4m 55sIn this video, you will learn how to configure, train, and evaluate the linear regression model, which makes predictions from multiple input features. FREE ACCESS
-
5. Preparing Data for a Neural Network6m 17sLearn how to create a dataset with multiple features in a form which can be fed to a neural network for training and validation. FREE ACCESS
-
6. Building a Neural Network6m 56sFind out how to define the architecture for a Keras sequential model and set training parameters such as the loss function and optimizer. FREE ACCESS
-
7. Evaluating the Neural Network4m 6sIn this video, you will make predictions on the test data and examine the metrics to gauge the quality of the neural network model. FREE ACCESS
-
8. Finding Correlations in a Dataset2m 34sFind out how to use Pandas and Seaborn to visualize correlations in a dataset and identify features that convey similar information. FREE ACCESS
-
9. Introducing Parsimonious Regression8m 54sIn this video, you will identify the risks involved with multiple regression and the need to select features carefully. FREE ACCESS
-
10. Applying Parsimonious Regression with Scikit Learn5m 28sIn 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. FREE ACCESS
-
11. Applying Parsimonious Regression with Keras8m 50sIn this video, learn how to build a Keras model by selecting only the important features from a dataset. FREE ACCESS
-
12. Exercise: Multiple Linear Regression5m 34sFind out how to encode categorical integers for ML algorithms, as well as how to use Pandas and Seaborn to view correlations and enumerate risks. FREE ACCESS
EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE
Skillsoft is providing you the opportunity to earn a digital badge upon successful completion on some of our courses, which can be shared on any social network or business platform.
Digital badges are yours to keep, forever.
