Machine Learning 101 – Multiple Linear Regression
by 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 …
Category: Machine Learning 101
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: …