Introduction As data scientists and machine learning practitioners, we come across and learn a plethora of algorithms..It’s important to examine and understand where and how machine learning is used in real-world industry scenarios..We covered a fairly comprehensive introduction to random forests in part 1 using the fastai library, and followed that up with a very interesting look at how to interpret a random forest model..In this article, we will first take a step back and analyze machine learning from a business standpoint..Then we’ll jump straight back to where we left off part 2 – building a random forest model from scratch.. Table of Contents Introduction to Machine Learning: Lesson 6 Machine Learning Applications in Business Random Forest Interpretation Techniques Introduction to Machine Learning: Lesson 7 Building a Random Forest from Scratch in Python Introduction to Machine Learning: Lesson 6 Having learned the basic underlying concept of a random forest model and the techniques used to interpret the results, the obvious follow-up question to ask is – where are these models and interpretation techniques used in real life?.Jeremy Howard answers this question in Lesson #6, where he explains how a random forest model can be used to interpret and understand the data..Step 1: Define the Objective Before diving into the challenge and building a machine learning model, one must have a clear, well-defined objective or an end goal in mind.. Step 3: Data The next step is to find out what data can be helpful in identifying and setting the lever that the organization may have (or can collect).. Step 4: Predictive models Once we have the required data that can be helpful in achieving the above defined goal, the last step is to build a simulation model on this data..We’ll move on to understand the applications of machine learning from the industry and business point-of-view.. Machine learning Applications in Business As we alluded to earlier, we can divide the business market broadly into two groups – horizontal and vertical..Here is a group of marketing applications where machine learning can (and is) be used.. More details