## 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 …

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## Machine Learning 101 – Multiple Linear Regression

## Machine Learning 101 – Basics of Logistic Regression

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

## Machine Learning 101 – K-Nearest Neighbors Classifier

## Machine Learning 101 – The Bayes’ Classfier

## Machine Learning 101 – Introduction to Classification

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

## Machine Learning 101 – What is Probability Density?

## Machine Learning 101 – Application of Bayes’ Theorem

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

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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 …

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. …

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 …

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 …

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: …