ML Regression

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Master regression by predicting house prices, investigate regularized linear regression, manage extensive feature sets, and employ optimization algorithms to make precise predictions with large datasets.

Key AI Functions:

Linear Regression,Ridge Regression,Lasso (Statistics),Regression Analysis

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Description for ML Regression

Case Study on House Price Prediction: Using input features such as square footage and the number of bedrooms, create models to predict house prices, illustrating the effectiveness of regression.

Regularized Linear Regression: Investigate the application of regularized linear regression models in a variety of domains for the purpose of feature selection and prediction.

Managing Large Feature Sets: Develop the ability to manage extensive sets of features and select between models of varying complexities, while also evaluating the impact of outliers on predictions.

Optimization Algorithms: Employ optimization algorithms to develop models that can accommodate large datasets in order to make precise predictions.

Level: Beginner

Certification Degree: Yes

Languages the Course is Available: 22

Offered by: On Coursera provided by University of Washington

Duration: 22 hours (approximately)

Schedule: Flexible

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