This course will teach you how to build a regression model to predict house prices. You will learn about the input and output of a regression model, bias and variance, optimization algorithms, cross-validation, model performance, sparsity, model selection, prediction, and implementation in Python.
The case study will cover a variety of topics, including:
- How to describe the input and output of a regression model
- How to compare and contrast bias and variance when modeling data
- How to estimate model parameters using optimization algorithms
- How to tune parameters with cross-validation
- How to analyze the performance of the model
- How to describe the notion of sparsity and how LASSO leads to sparse solutions
- How to deploy methods to select between models
- How to exploit the model to form predictions
- How to build your regression model to predict prices using a housing dataset
- How to implement these techniques in Python
By the end of the case study, you can build a regression model to predict house prices and understand its concepts.
🆓 Free to Audit
🕒 Approx. 22 Hours
📈 Intermediate Level
🧾 Paid Certificate Available Upon Completion
🎓 Offered by Washington University via Coursera
