Rainbow Team

Supervised learning In this document, I will not go so much into explaining the concepts and different algorithms used in supervised learning but will try to explain it for novice understanding. Supervised learning in one of the approaches one can use in machine learning. Some may say it is is the easier approach as compared to its counterparts like the unsupervised learning. Supervised Learning works basically on the principle of having training data where each instance has an input (a set of attributes) and a desired output (a target class). Then we use this data to train a model that will predict the same target class for new unseen instances. In short, Supervised learning occurs when the learning data contains the “right answers”. There is an influx of supervised learning algorithms such as the simple Naïve Bayes and K-Nearest neighbors to advanced linear classifiers, such as Support Vector Machines (SVM). Some methods, such as decision trees, will allow us to

credit:www.crowdanalytix.com Machine Learning (ML) has become a buzz word in 2016  and clearly means a lot in today’s technology. Looking at advancements from Google, Microsoft, Facebook, Amazon and other Companies to champion the use of this technology only tells you its importance. The downside is that for new engineers it’s hard to comprehend and those who have tempted to develop it have found it cumbersome. In simple terms, Machine Learning (ML) is a technology that allows the machine to learn how to do certain activities which couldn’t have been done with direct programing of the machine and will always change with future data. In most cases, these activities involve a lot of data, unidentified variables, non-linear repetitive (very dynamic repetitive activity) tasks and others. Let’s get an Example Imagine you had a website showing movies to users on the landing page, so for every user, a new recommendation is made. Programmatically, we can program a system

Hello World!,  From the Amonsoft,   Rainbow Team , We shall be doing a number of posts on Key technologies that we are using internally at Amonsoft to build better products in 2016. Machine learning  Artificial Intelligence Authentication Cloud Computing Messaging UI/UX  Analytics Tokenized Payment Databases Security This will be our first series of blog posts, and we hope to impact many other companies.