In most real world applications, we have a RESTful API service facing various client applications and a collection of backend services which process the data coming from those clients. Depending on the application, the architecture might have various services spread across multiple clusters of servers, and some form of queue or messaging service gluing them together. Read more...
When we think of communicating with someone today, we mostly think of sending them a text message or a voice note on WhatsApp. And some other people who are least bothered about their privacy online, think of Facebook Messenger. But not all these users know what’s happening with the messages they exchange on these platforms. Read more...
Originally published here: https://medium.com/@contactsunny/simple-apache-kafka-producer-and-consumer-using-spring-boot-41be672f4e2b
Before I even start talking about Apache Kafka here, let me answer your question after you read the topic — aren’t there enough posts and guides about this topic already? Yes, there are plenty of reference documents and how-to posts about how to create Kafka producers and consumers in a Spring Boot application. Read more...
For decades, we have been using the two-pronged key system for securing our electronic data and services. The two-pronged key we’re talking about is the username/password combination. There are variations of this, of course. For example, instead of a username, you might be using your email address, or something called a user ID. Read more...
Multicollinearity is a term we often come across when we’re working with multiple regression models. Even we have talked about it in our previous posts, but do we know what it actually means? Today, we’ll try to understand that. Read more...
In most of our posts about machine learning, we’ve talked about overfitting and underfitting. But most of us don’t yet know what those two terms mean. What does it acutally mean when a model is overfit, or underfit? Why are they considered not good? Read more...
Now that we know what is feature selection and how to do it, let’s move our focus to validating the efficiency of our model. This is known as validation or cross validation, depending on what kind of validation method you’re using. Read more...
In our previous post, we discussed what is feature selection and why we need feature selection. In this post, we’re going to look at the different methods used in feature selection. There are three main classification of feature selection methods – Filter Methods, Wrapper Methods, and Embedded Methods. Read more...
If you’ve come across a dataset in your machine learning endeavors which has more than one feature, you’d have also heard of a concept called Feature Selection. Today, we’re going to find out what it is and why we need it. Read more...
Today we’ll be looking at a simple Linear Regression example in Python, and as always, we’ll be using the SciKit Learn library. If you haven’t yet looked into my posts about data pre-processing, which is required before you can fit a model, checkout how you can encode your data to make sure it doesn’t contain any text, and then how you can handle missing data in your dataset. Read more...