learning problem, which is supervised learning.

They are called semi supervised or self supervised, and they use elements of unsupervised learning and pair them with supervised learning.

Is that supervised, unsupervised or reinforcement learning?

We'll talk about supervised learning, which is one side of machine learning, and Peter will tell you about unsupervised learning, which is a different style.

So here's how this supervised learning algorithm works.

learning algorithm. So this is an example of a supervised learning algorithm.

The supervised learning paradigm is the dominant paradigm in machine learning, and there are a vast amount of papers being written about it.

And that looks just like what we did when we used supervised machine learning to update our waits.

Let's show some examples of machine learning problems and I want you to tell me, for each one, whether it's best addressed with supervised learning, unsupervised learning, or reinforcement learning.

In the last video, we talked about Supervised Learning.

What is machine learning?

That's also machine learning.

So, that's machine learning.

Within supervised learning, the performance of many supervised learning algorithms will be pretty similar and when that is less more often be whether you use

learning. Randomized decision trees, another technique from machine learning.

learning at the beginning of the class but it's useful to contrast it with supervised learning.

So this is an example of a supervised learning algorithm. And it's supervised learning because we're given the, quotes, right answer for each of our examples.

Welcome to the machine learning unit.

Machine learning is a fascinating area.

learning, I feel like us machine

So, congrats on learning about your first Machine Learning algorithm.

The main two types are what we call supervised learning and unsupervised

Machine learning algorithms for data mining tasks

Machine learning is a super fascinating topic.

Support vector machines are supervised learning models used for classification and regression analysis.

I'd like to say a few final words about supervised versus unsupervised learning.

We talked about classification and regression and different methods to do supervised learning.

In the next video, I'm going to define what is supervised learning and after that, what is unsupervised learning.

And yes, Supervised Learning algorithm may break these data into these two separate clusters.

Consider a supervised learning classification problem where you have a training set like this.

You've just learned a lot about machine learning.

You now know a bunch about machine learning.

This is where machine learning comes into play.

Those machine learning problems are called regression problems.

Welcome to the first class on machine learning.

So why is machine learning so prevalent today?

So, that's it for supervised learning and in the next video I'll talk about unsupervised learning, which is the other major category of learning algorithms.

Let me back up a step further, and let's look at supervised learning more generally.

Here's another supervised learning example, some friends and I were actually working on this earlier.

Peter will tell you some more about machine learning.

What companies are famous for machine learning using data?

Welcome to this free online class on machine learning.

But for the time being, understand that the paradigms supervised and unsupervised span 2 very large areas of machine learning, and you learn quite a bit about it.

We talked a lot about supervised learning in which we are given data and target labels.

We have supervised learning, in which the training set is a bunch of input output pairs