Machine Learning: Regression

Machine Learning: Regression

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.

๐Ÿ†“ Free to Audit
๐Ÿ•’ Approx. 22 Hours
๐Ÿ“ˆ Intermediate Level
๐Ÿงพ Paid Certificate Available Upon Completion
๐ŸŽ“ Offered by Washington University via Coursera

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