What is Likelihood Function in Data Science and Machine Learning?
by I will try to explain Likelihood Function in very clear and simple terms. Likelihood Function in Machine Learning and Data Science is the joint probability …
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Basics of Bayesian Inference
Bayesian Inference is simply a way of making statistical inference by applying Bayes’ Theorem. Assuming there is a particular hypothesis H. Let the probability of …
Dockers, Containers and Kubernetes – A Simple Explanation
In this short write-up, I would explain to you these important trends in IT. The interesting thing is that they are actually very simple concepts …
What are Microservices? – A Simple Explanation
I’m going to explain microservices to you based on the following sections: What are Microservices? Benefits of Microservices How to Create Microservices Challenges of Microservices …
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, …