by Angie Ma, Gary Willis, Alessandra Stagliano | Publisher: Infinite Skills | Release Date: August 2017 | ISBN: 9781492023890

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 supplyAngie Ma, Gary Willis, and Alessandra Stagliano are data scientists with ASI Data Science, a London based AI/machine learning solutions firm. Angie co-founded ASI and is also the founder of Data Science Lab London, one of the biggest communities of data scientists and data engineers in Europe, with over 2,500 members. Angie holds a PhD in physics from London's University College, Gary Willis holds a PhD in statistical physics from London's Imperial College, and Alessandra Stagliano holds a PhD in computer science from the University of Genoa. Collectively, the group has worked on over 150 commercial AI/machine learning projects.

1. Linear Regression
   • Introduction 00:03:28
   • Review of modelling 00:05:20
   • Linear regression 00:12:33
   • Linear regression example 00:06:12
   • Model evaluation 00:19:55
   • Numerical stability and ridge regression 00:11:15
   • Conclusion 00:02:36