This is a quick introduction on popular Supervised Learning Algorithms.

As we may recall, Supervised Learning refers to the set of algorithms that uses training data comprising both of inputs and corresponding output to build a model that subsequently predicts the best output for future inputs.

Supervised Learning problems fall in two broad categories:

If your organization is running a web application, you are likely to use EC2. Further, there is a high likelihood your instances are all Linux. Linux systems are robust and a suitable first choice for server applications like a MEAN stack express.js web server, an application built using open source LAMP stack, commercial proprietary software, or basic server using Apache or Nginx. Linux is also very widely used for both SQL and NoSQL database applications like Redis, Mongo, or Postgres or MySQL.

In this blog, we will walk through Supervised and Unsupervised Learning and answer these questions: 

This is part 3 of this series on Data Analysis. The focus of this article is all the different types of data visualizations and the reasons you might choose each one.

If you missed the first two pieces in this series, read them here: An Introduction to Data Analytics and  What is Data.

This is part 2 of this series on Data Analysis. The focus of this article is all the different types of data and how each one can be analyzed.

If you have not yet read Part 1, you can do so here.

Every day, words like “Artificial Intelligence”, “Big Data”, “Data Analytics”, and “Machine Learning” are thrown around constantly these days, but what do they actually mean? Perhaps more importantly, why should you care?