Machine learning and statistical modeling share a common goal of data learning. These two are different.

Since both these concepts drive similar end-goals, the amount of confusion surrounding whether they are similar or opposites, synonyms or antonyms sprouts up.

## Before understanding the differences, first, let’s quickly go over the definitions of each.

Machine learning is more like enabling your computer to learn from the data on its own without having any manual feed of rules and instructions. When handling huge data sets, it is manually not possible to feed instructions for each data set. Instead, programmers design algorithms that enable the computer to keep learning with time and achieve the final goal that remains pre-defined mostly.

Statistical modeling is a part of mathematics. It is mostly concerned with establishing a relationship between two variables that can predict a proper outcome. This is entirely in the form of mathematical equations.

The end goal for both is same but with some basic differences. One difference is pretty evident from the above definitions. While machine learning is part of artificial intelligence and computer science, statistical modeling is about mathematical equations. The former learns from the data, and the later predicts an outcome.

## Machine Learning vs. Statistical modeling

Without much ado, let’s get straight to the points of difference.

The very first thing that sets both apart is their time of inception. Both belong to two different eras, understandably one followed by another only to evolve the way data is handled. Statistical modeling is oldest among the two and has been around for several centuries. But machine learning came into the picture only in the late 1950s, credited to computer scientists Arthur Samuel and Tom Mitchell. The concept of machine learning took time to emerge, and it was not before 1990’s that pattern recognition and theory of computational learning in artificial intelligence made way into the mainstream programming world.

Machine learning algorithms can learn from n-number of observations, going over each one by one. It accesses each observation and learns over time to predict the exact combination of actions that can help in achieving the end-goal. Usually, machine learning deals with data that has numerous attributes and a large number of observations.

On the contrary, statistical models encompass series of assumptions that deal with a long lineage of observed and similar data. It does not rely on predictive accuracy like machine learning. In fact, you can define machine learning as model and assumptions checking without the statistical touch in it.

Now let’s talk about the extent of human intervention in both these concepts. Machine learning requires almost no human intervention because it is about enabling a computer to learn on its own from a large set of data without any set instructions from a programmer. It explores the various observations and creates definite algorithms that are self-sufficient enough to learn from data as well as make predictions.

That’s not how things work in statistical modeling. It is about mathematics equations and requires the modeler to accurately understand the relationship between two variables before they are fed into the data.

Despite these points of differences, the line separating the two is slowly getting blurred with time. Machine learning and statistical modeling are apparently related to the concept of predictive modeling. With time, these two concepts are evolving to learn from one another to sustain in the dynamic data world. Analysts and data scientists have voted equally for the importance of learning both these concepts because staying relevant in the data-driven world is the ultimate goal for data analysts and scientists. That cannot happen by ignoring or choosing one over the other.