So in this lecture, we will be introducing the next major part of this course, which is on machine
learning, this course is looked at two very different techniques thus far.
The first being vector based models.
And the second being probability based models.
In this part of the course, we will now look at new models, which are based on what we've already
learned.
In some cases, these new models will be vector based, and in others they will be probability based,
and in some cases they will be both.
Now, one pattern I hope you're starting to see is that many of these different techniques we're learning
about are interchangeable.
A simple example of this is with count vectors, as you know, any place where I use count vectors.
I can also use TFI Taf.
So these two techniques are interchangeable.
What works best for your use case is really dependent on the specifics of your data set.
So if you see me using TFI Taf in the coming lectures, this does not imply that you should always use
TFI Taf if it's simply one possible option out of many in your work.
You should be testing all options to see which works best for you.
Another example is with machine learning models.
Some machine learning models we will learn about are meant for the same task.
For example, logistic regression and naive Bayes, both of these can be used for text classification.
So anywhere you see one, you could use the other.
And if you know of any other classifiers you'd like to try.
Feel free to use those as well.
Note that in this course, when we talk about machine learning, this refers to techniques which are
not based on deep learning and neural networks.
This is only to disambiguate these topics within this course outside of this course.
Deep learning is simply a subset of machine learning.
And so just be aware of how we categorized each topic.
OK, so let me give you a brief outline of the coming sections.
Now, unlike the other sections of this course, these will be centered around the application rather
than the technique.
So for example, when we looked at vector models and Markov models, these were centered around the
technique.
Our goal was to learn the technique and any applications we discussed were secondary in the machine
learning sections.
This is sort of flipped around, which is more in the spirit of V1 of this course.
So as an example, the first section will be based on an application known as spam detection.
We will study a technique that happens to be useful for this called naive Bayes in the second section.
This will be based on an application known as sentiment analysis.
In this section, we will study a technique known as logistic regression.
In the third section, we will study an application called Latent Semantic Indexing, which is relevant
for search engine optimization, also known as SEO.
In this section, we will study techniques such as PCA and SVT.
In the next section, we'll look at an application called topic modeling.
This is a way for you to automatically categorize a set of documents without being told what they are
about.
It is an unsupervised technique.
In this section, we'll study a technique known as latent directly allocation.
So why are these sections based on the application instead of the technique primarily?
My goal is to help you see that NLP is really applicable in the real world by putting the application
at the forefront.
You will see this more immediately.
In addition, some people simply learn better this way.
Many people don't seek out to learn naive Bayes for no reason.
Typically, you are part of a business and you have in mind some business application.
You will generally use whatever techniques exist that will help you improve your business capability
at handling that application.
And if not, you base happens to be one of those techniques, then that is what you will opt to learn.
So this is simply a different way that one may encounter and learn about machine learning.
But either way, you will still learn about both techniques and applications, regardless of their order
or emphasis.
So one thing I wanted to mention about this part of the course is that each section is essentially independent.
What I mean by this is that it doesn't really matter which order you do them in.
So if you want to do topic modeling before you do spam detection, that should work.
This is unlike some of the other sections.
For example, you can't do cipher decryption before you learn about basic markup models because Markov
models are a prerequisite.
So that's one thing to keep in mind for the machine learning sections.
Please feel free to do them in any order that works for you.
Now, one point of caution is that from my experience, students are typically more comfortable with
supervised methods compared with unsupervised methods.
In this case, spam detection and sentiment analysis are supervised.
On the other hand, topic modeling and latent semantic indexing are unsupervised.
Interestingly, tech summarization can be either, but for the purpose of this course, it's unsupervised
as well.
However, it's conceptually easier to understand compared with the other unsupervised topics.