O’Reilly – Linear Regression

O’Reilly – Linear Regression
English | Size: 208.34 MB
Category: Tutorial

Linear regression is one of the most important machine learning tools. It is the simplest of the predictive modeling techniques and it is widely used, whether on its own or in combination with other techniques. This course teaches the principles and practices of linear regression. It reviews the meaning of modeling, explains linear regression’s key concepts (e.g., cost function, R-squared metric, etc.), describes the practice and need for hypothesis testing, illustrates how to implement linear regression computationally, and showcases an implementation of ridge regression. An understanding of basic mathematics is required, and some knowledge of linear algebra and differential calculus will allow the viewer to understand all of the subtle details.

• Understand what regression means and how to do linear regression
• Explore key machine learning concepts like cost function, F-test, and cross validation
• Learn about hypothesis testing and the frequently mentioned term "P value"
• Learn how to use – and enjoy free access to – the SherlockML data science platform
• Develop the skills required for the machine learning job market where demand outstrips supply

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