Modeling & Machine Learning

Machine learning is a technology that is breaking ground at new speeds every day. Technically, it should be improving faster and faster, given that ML is essentially supposed to be learning itself.

The machine learning and modeling case study is the most common type of interview question that tests a combination of modeling intuition and business application.

Data processing and analysis is the first step that we need to consider once we've clarified details and started down the path of building the model.

Feature selection and feature engineering is the second part of the data processing step. Once we've understood what our data looks like, we need to begin to theorize the kinds of features we would use to build the model.

Model selection is usually the crux of any modeling case study problem. We want to be able to select a model or machine learning algorithm that will combine a bunch of factors to become the most optimal algorithm for the problem.

We have touched on the different machine learning algorithms throughout this lesson, but haven't yet dived deep into each one. The prior for this course is that you, as a candidate, have an idea of basic machine learning concepts, and the different modeling algorithms are one such example of them.

Recommendation and search engines are a subclass of the information filtering domain. They are used in almost every single modern web application or platform. Implementing recommendation and search engines usually requires a complex team of data scientists, data and software engineers, and machine learning engineers to collaborate and build at every company.

Most machine learning model deployment requires some technical details and implementation to doing so. But we can abstract away from that in an interview when we’re focusing on the model roll out.

Applied modeling is a type of case question asked about practical machine learning. The most common type of question framework is: Given an example scenario with a machine learning system or model, how would you analyze and fix the problem?

Machine learning system design focuses more heavily on the engineering aspects of model deployment. While slightly out of scope, we still wanted to cover some of the basics.

Regression models are used to predict the value of a dependent variable from one or more independent variables.