- [Instructor] Data analytics combines
age-old statistical techniques
with modern machine learning software.
A classic example of statistics is linear regression,
where a line is fit through a bunch of data points.
But linear regression is also considered
a supervised machine learning, or ML, algorithm.
So is linear regression statistics or machine learning?
It's both.
If it's a static onetime event, it's statistics.
If you're training a model with one dataset
to be used to make predictions about another dataset
and if the model is continuously being improved
with new data and if the data sets are huge,
it's machine learning.
The trick in data analytics is to find the right model
and then model its parameters, coefficients,
and constants to improve the results.
We can manage things if there are just two parameters,
but if there's hundreds of parameters,
which is often the case,
or even hundreds of thousands of parameters,
as found in deep learning networks,
then we need machine learning to tweak the parameters,
coefficients, and constants.
Machine learning is simply statistics
wrapped in learning software
that tweaks the model's parameters to make it more accurate.
Today's large datasets necessitate the tight integration
of statistics and learning software.
So although purists are technically correct
when they say that not all analytics is machine learning,
it's easy to understand why people
use the terms interchangeably.
Another way to classify analytics is by what it does.
Analytics and ML identify things,
like using facial recognition and speech recognition.
They predict things,
like predicting the effectiveness of a vaccine.
They group things together,
like when used in spam filtering,
and they associate things,
like when used for medical diagnoses.
And yet another way to classify analytics,
or in particular machine learning, is by how it learns.
There's three types of learning software.
There's supervised learning that requires
the datasets to be labeled
so the software can learn when it's right or wrong.
This is used when identifying things and predicting things.
A good example of this is teaching cars
to identify things on the road using deep neural networks
and learning the warning signs
for a jet engine predictive maintenance system.
A second learning software is unsupervised learning.
Unsupervised learning only needs raw data.
It is used for grouping things
or associating things together,
as it's used in recommender systems,
for example, by Netflix and Amazon.
The third way is reinforcement learning.
Reinforcement learning needs the data and the rules.
It's used for decision making as used in player AI
for EA sports games and for the Roomba vacuum robot.