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
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identify the reasons to use multiple features when doing a regression and the technique involved in creating such a multiple regression modelprepare a dataset containing multiple features to used for training and evaluating a linear regression modelconfigure, train and evaluate the linear regression model which makes predictions from multiple input featurescreate a dataset with multiple features in a form which can be fed to a neural network for training and validationdefine the architecture for a Keras sequential model and set the training parameters such as loss function and optimizermake predictions on the test data and examine the metrics to gauge the quality of the neural network model
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use Pandas and Seaborn to visualize correlations in a dataset and identify features which convey similar informationidentify the risks involved with multiple regression and the need to select features carefullyapply the principle of parsimonious regression to re-build the Linear Regression model and compare the results with the kitchen sink approachbuild a Keras model after selecting only the important features from a datasetencode categorical integers for ML algorithms as well as use Pandas and Seaborn to view correlations, and enumerate risks
IN THIS COURSE
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1.Course Overview2m 20sUP NEXT
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2.Understanding Multiple Regression7m 45s
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3.Kitchen Sink Regression6m 32s
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4.Training and Evaluating the Model4m 55s
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5.Preparing Data for a Neural Network6m 17s
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6.Building a Neural Network6m 56s
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7.Evaluating the Neural Network4m 6s
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8.Finding Correlations in a Dataset2m 34s
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9.Introducing Parsimonious Regression8m 54s
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10.Applying Parsimonious Regression with Scikit Learn5m 28s
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11.Applying Parsimonious Regression with Keras8m 50s
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12.Exercise: Multiple Linear Regression5m 34s
EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE
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