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!
