Modeling & Machine Learning



Published August 20, 2020

11 Courses

ML System Design
Machine Learning
Deep Learning

Overview and objectives

In this course we'll tackle how to solve machine learning problems asked in interviews framed around case studies, benefits and tradeoffs, and business applications.


The audience for this course is anyone who has a basic understanding of the fundamentals behind the most common machine learning algorithms. In-depth knowledge of theory and advanced concepts like deep learning is not required.


Courses in this learning path are:

Introduction to 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.

4 of 4 Completed

Modeling Case Study

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.

2 of 2 Completed

Data Pre-Processing

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.

1 of 5 Completed

Feature Selection

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.

1 of 4 Completed

Model Selection

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.

0 of 4 Completed

Machine Learning Algorithms

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.

0 of 7 Completed

Recommendation and Search Engines

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.

0 of 5 Completed

Model Evaluation

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.

0 of 9 Completed

Applied Modeling

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?

0 of 5 Completed

Machine Learning System Design

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.

0 of 5 Completed

Generalized Linear Models and Regression

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

8 of 13 Completed

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You have 47 sections remaining on this learning path.

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