Hello! In this video, we’ll be covering the differences

between machine learning and statistical modeling. Statistical Modelling and Machine Learning

can be mixed up sometimes. So, to clarify …

Machine learning is an algorithm that can learn from data without being reliant on standard

programming practices, like Object Orientated Design. Here are some important facts about Machine Learning

* Machine Learning is a newer field of study than statistics (for instance, Machine Learning was invented

in 1959, whereas statistics originated in the 17thcentury)

* Machine Learning can result in more detailed information than statistical modelling. * Machine Learning is a subfield of computer

science and A.I., and contributes to building systems that can learn from data without explicit

programming * Finally, Machine Learning uses fewer assumptions

than statistical modelling Statistical Modeling is the formalization

of relationships between variables in the form of mathematical equations. Statistical Modelling is a subfield of math

that deals with finding relationships between variables to predict outcomes. It deals with a small amount of data with

fewer attributes and, as such, there is a good chance that over-fitting will occur. Statistical Modeling requires the modeller

to understand the relation and implementation that a variable has on an equation, in an

effort to best ‘estimate’ the function output to a certain error. In comparison, machine Learning requires minimal

human effort, as the workload involved in computing is placed squarely on the machine. Furthermore, Machine Learning has a strong

predictive power, as the machine is ‘fit’ and ‘trained’ to find patterns in the data. Here’s a table that details the different

naming terminologies between machine learning and statistical modeling. Please take a moment to review the chart

Beyond naming convention, there are several other differences between machine learning

and statistical modelling. This chart summarizes a few of them. [1] For instance, in machine learning, fewer

assumptions are made, due to a better accuracy from the predictive models, in comparison

to statistical modelling which is more mathematically based. [2] Machine Learning is a subfield of Computer

Science and uses algorithms, while Statistical Modelling is a subfield of Mathematics and

uses equations. [3] One of the main things that makes machine

learning useful is that it also works well with large sets of data, whereas statistical

modelling has a hard time doing so. Machine learning provides strong predictive

ability with minimal human effort, while statistical modelling provides the best estimate and more

human effort. So? how does Machine Learning actually work? Well, one of the more important concepts to

know in Machine Learning is being able to distinguish supervised and unsupervised learning. In a later module, we’ll cover supervised and

unsupervised learning in more depth, but for now here is a brief synopsis:

In supervised learning, we have a set of training data, or labeled data, in which we know the

structure and the outcome of it. We take this data and train a machine learning

model, so it can understand patterns in the data. Once the model has been trained, we can use

it to predict the results of out-of-sample data, or data in which the results are unknown. Conversely, if we are given a set of data

that is unstructured, then we can apply unsupervised machine learning models to find patterns that

exist within that data. Thanks for watching!

## 8 Comments

## K. M. Zubair Hasan

Lucid Explanation

## Michael Sinclair

I don't agree.

Statistical modelling AND machine learning are looking for relationships between variables.

There are many assumptions one must make when using machine learning techniques and you must choose the correct model.

A statistical model such as lm can be used on 'big data' and can be just as predictive as a machine learning algorithm depending on the data.

## tmusic99

This is an, if not erroneous, incomplete presentation of machine learning vs statistical modeling. Designed experiments is a vital part, if not a more complete form of statistical modelling than predictive statistical modeling. It allows you to model cause and effects and establish a base for mechanistic modeling. If you do not know why you get the prediction you get from the ML algorithms, you can "drill down" with a set of designed experiments. A/B testing is primitive but very commonly used designed experiment e.g. in web design. Thus, it is reasonable to assume that the most productive development environments will include both the machine learning toolset and the statistical modelling toolset. This especially if you are working in a cross-disciplinarian environment such as the auto, power or aerospace industries. Environments that also frequently use first principle simulation tools. You may assume that all these tools will merge into a powerful integrated toolset. Or?

## Michael Chajewski

This video is completely misinforming the viewer. Machine Learning is solidly based in statistics, but contextualizes the objectives in a descriptive rather than inferential way.

## Alex Tsaloukidis

Machine learning= glorified statistical modeling

## miguel medina

Why the output of a standard regression (with p-values, coefficients, etcetera) doesn’t look like a machine learning regression output?

## miguel medina

Why the output of a standard regression (with p-values, coefficients, etcetera) doesn’t look like a machine learning regression output?

## azhany.com

Are you kidding me? Algorithms based on mathematics too. Based on this video you know nothing about machine learning or even computer science itself. Lol