## Machine Learning 101 – Multiple Linear Regression

You already know of Simple Linear Regression. You also know of Logistic Regression. Now we would discuss Multiple Linear Regression. This is a case where …

## Machine Learning 101 – Basics of Logistic Regression

First I would like clarify that the Logistic Regression model is a model for classification. Also note that Machine Learning 101 focuses on Supervised Learning. …

## Machine Learning 101 – Minimizing Misclassification Rate in Bayes’ Classifier

In Lecture 4, we learnt about the Bayes’ classifier. Here  we would see how to minimize misclassfication rate in Bayes classifier. Again, we would review …

## Machine Learning 101 – K-Nearest Neighbors Classifier

In the last lecture, we discussed Bayes’ Classifier. Now, we are going to discuss K-Nearest Neighbors Classifier. Remember that Bayes Classifier tries to classify X …

## 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’ …

## Machine Learning 101 – Introduction to Classification

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, …

## Machine Learning 101 – Bias-Variance Trade-off

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. …

## Machine Learning 101 – What is Probability Density?

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 …

## Machine Learning 101 – Application of Bayes’ Theorem

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 …

## Machine Learning 101 – Rules of Probability & Bayes’ Theorem

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: …