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There are many different approaches or models in machine learning, but generally, they can be broken down into two major categories called supervised learning and unsupervised learning.
Vocabulary and Definitions
- Model: An algorithm or an approach to a problem
- Probability: A means of expressing how likely it is that an event will occur, or a way of measuring how close a value might be to the actual correct value
- Supervised learning: A case where a machine intelligence is tasked with predicting a category or a quantity
- Unsupervised learning: A case where a computer analyzes unlabeled data and has no previous examples, and tries to identify patterns in the data
- Classification: A supervised machine learning model that makes a prediction about how a piece of data should be categorized
- Regression: A supervised machine learning model that attempts to predict a quantity or a number
- Clustering: An unsupervised machine learning model that attempts to group similar examples together
Further Reading
- Ethical Design: Treehouse course - Stage 3, in particular, covers machine learning.
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There are many different approaches,
or models, in machine learning.
0:00
But generally,
they can be broken down into two
0:04
major categories called supervised
learning and unsupervised learning.
0:07
I'm going to use a lot of new terms
here that might be confusing at first.
0:13
But don't worry,
0:17
I'll break each one down a little
further after the initial explanations.
0:18
You might wanna pause the video
periodically to read the teacher notes as
0:23
each concept is introduced.
0:27
You might also need to go back and
rewatch parts of a video to review.
0:28
Let's start with an important
vocabulary word.
0:34
When you hear the word model or algorithm
in reference to machine learning,
0:38
it essentially means
an approach to the problem.
0:44
A model in machine learning is just like
an architectural model or a doll house.
0:47
It's a simplification that
attempts to simulate or
0:53
demonstrate some aspect of the real world.
0:57
Meteorologists use weather models
to try and forecast the weather.
1:01
Because even all the computing
power in the world
1:06
couldn't perfectly simulate
every tiny air particle and
1:09
temperature and pressure change
that would influence the outcome.
1:13
So instead, we can use a model
to simplify the problem and
1:17
get a useful approximation of the result.
1:21
A model in machine learning
is typically probabilistic.
1:25
In other words, it usually does
not produce an exact result.
1:29
Rather, it makes a prediction with
a corresponding percentage of confidence.
1:34
This isn't something you need to
understand in great detail, but
1:39
it's good to know the basics.
1:43
Probability is a means of expressing how
likely it is that an event will occur, or
1:46
a way of measuring how close a value
might be to the actual correct value.
1:51
Probability is typically quantified
as a value between 0 and 1,
1:58
with 0 being a complete guess,
and 1 being complete certainty.
2:03
For example, if you're rolling a die and
hoping to roll a 2,
2:08
you have a 1 in 6 chance of rolling a 2,
2:13
because the die has 6 sides,
and only one of them is a 2.
2:17
1 divided by 6 is 0.16 repeating,
2:22
or a 16.6% chance.
2:27
This is relevant to machine
learning because if a prediction is
2:30
far outside the norm, the model might
have low confidence in the answer,
2:35
because it's highly probable
that it's not correct.
2:40
If the prediction matches
up almost perfectly with
2:44
an existing example in a data set,
then the confidence will be very high.
2:47
Now, with the definition
of a model in mind,
2:53
let's take a look at the two major
categories of machine learning.
2:56
The first of the two categories for
machine learning approaches or
3:01
models is called Supervised Learning.
3:05
Supervised learning is when
a machine intelligence
3:09
is tasked with predicting a category or
a quantity.
3:12
Predicting a category or
3:17
a quantity comprises the two
subcategories of supervised learning,
3:19
which are called classification and
regression respectively, put another way.
3:24
A classifier looks at a piece of data and
3:30
tries to categorize it, or,
in other words, classify it.
3:33
And a regression tries to
predict a quantity or a number.
3:38
The second of the two major
categories is Unsupervised Learning.
3:43
Unsupervised learning is when
a computer analyzes unlabeled data, and
3:48
has no previous examples, and
tries to identifies patterns in the data.
3:53
One of the most common subcategories
of unsupervised learning is called
3:58
clustering, which are models that attempt
to group similar things together.
4:03
Because learning is unsupervised,
4:08
the model's definition of similar
might be different than our own.
4:11
Regressions, classification,
and clustering are not the only
4:16
approaches in the two categories of
supervised and unsupervised learning.
4:20
There are many more, and
4:25
you should check out the notes associated
with this video for more resources.
4:26
That's a lot of concepts all at once.
4:31
So now, let's focus and break things
down further by thinking about
4:33
an example application for one of these
starting with supervised learning, and
4:38
one of its subcategories, classification.
4:43
Let's say you want to classify
an email as spam or not spam.
4:48
You could give a machine intelligence
millions of email messages that
4:54
are already labeled as not spam, and
millions that are labeled as spam.
4:58
With each example message, you would
identify features of the data, like
5:06
the subject line, the sender, the body of
the email, the attachments, and so forth.
5:11
Then, when a new email comes through,
the machine intelligence can refer
5:18
to all of the features of the spam,
and not spam messages and
5:23
decide how closely the new email
matches any patterns in the data.
5:28
Then, it assigns a category
to the new message
5:34
with some percentage of confidence.
5:37
In this course, we're going to create our
own classifier that will attempt to label
5:41
new entries into a data set
based on the existing data.
5:46
However, for completion, let's take a
quick look at regressions and clustering.
5:50
A regression is another type
of supervised learning.
5:57
Instead of attempting to categorize data,
it tries to predict quantities.
6:01
For example,
say you're opening a restaurant, and
6:06
you're trying to decide how
dishes should be priced.
6:10
A regression algorithm could look at a
data set of other restaurants in the area,
6:13
and use features like the average price
of a dish, the relative distance to
6:19
the new restaurant's location, the average
review score from Yelp, and so forth.
6:24
Based on that information, the regression
could try and predict appropriate prices.
6:29
The last approach I mentioned
is called clustering,
6:36
which is one of the biggest categories
of approaches to unsupervised learning.
6:40
Have you ever been on a social network and
6:46
been shown suggested friends or
targeted advertisements?
6:48
Or have you watched something
on a video sharing site
6:52
that shows you suggested videos?
6:56
[SOUND] How do these websites
know what to show you?
6:58
Or what content is similar?
7:01
A cluster analysis can attempt the group
7:04
data by similarity without attempting
to apply any type of labels.
7:07
This cluster analysis might help to
identify hidden patterns in the data,
7:12
and automatically group things together
based on features of the data.
7:18
This was a broad overview of the different
approaches to machine learning.
7:23
And, as you can imagine, it's a deep
topic with lots more to explore.
7:28
I encourage you to check the notes
associated with this video
7:33
to help review what we've learned.
7:36
I also want to mention again,
7:39
don't worry if you're not
understanding everything right away.
7:41
In upcoming videos, we'll take a closer
look at some of these concepts.
7:44
Machine learning is a huge area of study,
and
7:49
it might take some review for
you to fully absorb everything.
7:52
Remember, you can always go back and
7:57
rewatch videos if you
feel like you need to.
7:59
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