Linear Regression Models: Building Models with Scikit Learn & Keras
Machine Learning
| Beginner
- 9 videos | 41m 3s
- Includes Assessment
- Earns a Badge
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.
WHAT YOU WILL LEARN
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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
IN THIS COURSE
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2m 32s
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4m 32sIn this video, you will 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. FREE ACCESS
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3. Splitting a Dataset for Training and Validation4m 16sIn this video, you will create training and validation sets for your regression model. FREE ACCESS
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4. Keras Installation4m 38sLearn how to configure a linear regression model, train and validate it, view the metrics for the model, and visualize it using Matplotlib. FREE ACCESS
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5. Training and Evaluating a Model6m 18sFind out how to install the Keras library and prepare the dataset to be used by a Keras model. FREE ACCESS
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6. Building a Sequential Model4m 10sFind out how to define the architecture for a Keras sequential model and initialize it. FREE ACCESS
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7. Training a Neural Network5m 35sLearn how to compile a Keras sequential model by defining the loss function and optimizer. Train it to get the optimal values for weights and biases. FREE ACCESS
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8. Evaluating a Neural Network3m 15sTo evaluate a Keras sequential model, use it to make predictions on test data. FREE ACCESS
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9. Exercise: Building Simple Regression Models5m 47sLearn how to work with training sets and the Keras sequential model. FREE ACCESS
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