Machine Learning models are key to the continued improvement of artificial intelligence. This article is the first in a series of articles that I’ll be publishing introducing reads to various Machine Learning concepts. First up, we introduce some typical steps involved in producing a machine learning model. 1. Problem Definition…
In part 1 of this Machine Learning Concepts series of posts I provided an introduction to the necessary steps to deploy a machine learning model. Check that out if you missed it. Now that we understand the requirements for deploying machine learning models we need to begin by defining what…
Previously, we gave an introduction to the necessary steps to deploy a machine learning model as well as started the discussion of problem definition and data collection. Now that we have a firm understanding of the problem our machine learning model is attempting to solve we need to perform a…
Next up in our machine learning concepts series we are going to discuss exploratory data analysis (EDA). EDA is a cornerstone of data science; you can think of it as a process for you to get to know your data by uncovering patterns, trends, relationships and potential issues. It can…
So far in our ML Concepts series we’ve defined out problem and goal, we’ve identified, gathered and preprocessed the necessary data, and we’ve developed an understanding of our data through exploratory data analysis. Today’s article will introduce selecting the best model(s) for our data depending on various data characteristics. When…
In part 6 of our Machine Learning Concepts series we are going to discuss model training. Up until now we’ve identified the goal of our machine learning model, have pre-processed our data into a standard format, identified the features of interest in our data, and selected the necessary models that…