Linear Algebra and Feature Selection

Linear algebra is a centuries-old field of mathematics, involving the application of vectors, matrices, and linear transforms. Nowadays, it takes an important role in shaping the world of machine learning. Feature selection – another key concept we will cover – detects unneeded variables in a machine learning model and linear algebra. Explaining the calculations behind an ML algorithm can be extremely useful in identifying the most efficient approach to manipulating data. This course will teach you the mathematical concepts behind linear algebra for machine learning and will show you how to integrate these concepts into the feature selection activity.

Introducing you to the essentials of machine learning in Excel with hands-on projects. You will use linear and logistic regression, k-means clustering, decision trees, and Azure ML Studio.

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 your impact to 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 attain the skills and tools that you would need to fully exploit the value hidden in your data.