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!