Machine Learning with Ridge and Lasso Regression

Ridge and Lasso Regressions are machine learning algorithms with an integrated regularization functionality. Built upon the essentials of Linear regression with an additional penalty term, they serve as a calibrating tool for preventing overfitting. In this practical course, you will learn how to apply Ridge and Lasso Regression in Python and determine which of the two would be the best choice for a particular dataset.

This course was prepared by two data scientists working at one of the FAANG companies. It aims to teach you how to approach data science work with a growth mindset. You will learn why this mindset can be the key lever to power and sustain the impact you make on your business. Most mid- to senior-level managers would be skeptical about investing in data enablement without any idea of what the ROI would be. In this course, Davis and Tina explore low-cost ways for you to de-risk this decision for the budget owner, and in the process reveal the skills and tools that you would need to fully exploit the value hidden in your data.

In this course you learn everything about the machine learning process beginning with framing the problem all the way through model productionization. This course teaches you exactly how to get the most out of the models that you use to create great value on your projects and in your work. In part one we give you all the details on what machine learning is and when to use it. In part 2 we focus on the necessary process for creating the most value from your models. We cover: (1) Machine Learning Problem Framing (2) Data Collection (3) Data Cleaning (4) Exploratory Data Analysis (5) Feature Engineering (6) Cross Validation (7) Modeling (8) Productionization and (9) Documentation

We designed this course as an end-to-end machine learning algorithm resource. You can either go through it sequentially to learn the algorithms or use it as a reference. What is different about these courses is that we focus on the practical implications of these algorithms rather than just high-level theory and toy examples. We tell you how these are used in the real world and insights around when and why you may or may not want to use them in practice. We are also major believers in open source, so we included all the resources that we used to make this course alongside our lectures for you to directly reference.