Machine Learning 101 – The Bayes’ Classfier
This is the second lecture on classification. It follow the first one: Introduction to Classification. Bayes’ Classifier is a classifier that works based on Bayes’ …
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This is the second lecture on classification. It follow the first one: Introduction to Classification. Bayes’ Classifier is a classifier that works based on Bayes’ …
In subsequent lectures, we have discussed regression problems. Now we would apply the same analysis to classification but with little adjustment. In case of classification, …
This Lecture follows from Lecture 7 on Underfitting and Overfitting. Here we would discuss Bias-Variance Trade-off. I will try to make this lesson very clear. …
By now, you probably understand probability as well as probability theory. You also know about the Sum Rule and Product Rule. Then you also understand …
In the previous lesson (Lesson 9), we derived Bayes theorem. So let’s write it out: Also recall that Bayes’ theorem helps us find conditional probabilities …
We will now consider some of the important rules of probability. Meanwhile we would also understand the meaning of terms along the line. They include: …
As you already know, one of the four basic theories of Machine Learning is the Probability Theory. Or simply, Probability. And this is one challenge …
Remember that in the previous lecture (Lecture 6), we discuss polynomial curve fitting. We kind of saw that the relationship in a dataset can be …
This is Lecture 6 of Machine Learning 101. We would discuss Polynomial Curve Fitting. Now don’t bother if the name makes it appear tough. This …
Let’s go back to the regression problem we solved in Lecture 4. We are given a dataset. You need to find the relationship between the …