Interpreting the multiple regression fit - Machine Learning: Regression
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Interpreting the multiple regression fit - Machine Learning: Regression
Machine Learning Specialization
Case Study - Predicting Housing Prices
In our first case study, predicting house prices, you will create models that predict a continuous value (price) from input features (square footage, number of bedrooms and bathrooms,...). This is just one of the many places where regression can be applied. Other applications range from predicting health outcomes in medicine, stock prices in finance, and power usage in high-performance computing, to analyzing which regulators are important for gene expression.
In this course, you will explore regularized linear regression models for the task of prediction and feature selection. You will be able to handle very large sets of features and select between models of various complexity. You will also analyze the impact of aspects of your data -- such as outliers -- on your selected models and predictions. To fit these models, you will implement optimization algorithms that scale to large datasets.
Learning Outcomes: By the end of this course, you will be able to:
-Describe the input and output of a regression model.
-Compare and contrast bias and variance when modeling data.
-Estimate model parameters using optimization algorithms.
-Tune parameters with cross validation.
-Analyze the performance of the model.
-Describe the notion of sparsity and how LASSO leads to sparse solutions.
-Deploy methods to select between models.
-Exploit the model to form predictions.
-Build a regression model to predict prices using a housing dataset.
-Implement these techniques in Python.
Linear Regression, Ridge Regression, Lasso (Statistics), Regression Analysis
I really like the top-down approach of this specialization. The iPython code assignments are very well structured. They are presented in a step-by-step manner while still being challenging and fun!,This is an excellent course. The presentation is clear, the graphs are very informative, the homework is well-structured and it does not beat around the bush with unnecessary theoretical tangents.
The next step in moving beyond simple linear regression is to consider multiple regression where multiple features of the data are used to form predictions. p More specifically, in this module, you will learn how to build models of more complex relationship between a single variable (e.g., 'square feet') and the observed response (like 'house sales price'). This includes things like fitting a polynomial to your data, or capturing seasonal changes in the response value. You will also learn how to incorporate multiple input variables (e.g., 'square feet', '# bedrooms', '# bathrooms'). You will then be able to describe how all of these models can still be cast within the linear regression framework, but now using multiple features. Within this multiple regression framework, you will fit models to data, interpret estimated coefficients, and form predictions. pHere, you will also implement a gradient descent algorithm for fitting a multiple regression model.
Interpreting the multiple regression fit - Machine Learning: Regression
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