Machine Learning

Supervised Learning is becoming a good friend for marketing business in particular. For example how much money will we make by spending more dollars on digital advertising? Or even making small predictions for stock markets i.e. What’s going to happen to the stock market tomorrow?

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Since no labels are given to the learning algorithm, leaving it on its own to find structure in its input. Unsupervised learning can be a challenging goal in itself. The training data consists of a set of input vectors x without any corresponding target values; hence known as learning…

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Reinforcement learning can be understood by using the concepts of agents, environments, states, actions and rewards. This is an area of machine learning; where there’s no answer key, but RL agent still has to decide how to act to perform its task. The agent is inspired by…

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Before we get into types of Machine Learnin lets understand some basics here. The approach of developing ML includes learning from data inputs based on “What has happened”. Evaluating and optimising different model results remains focus here. As on date Machine Learning is…

Machine learning has evolved from Artificial Intelligence Subset to Its own domain. It has reached an inflection point – at least in terms of messaging. I remember in my school days as part of statistics class we were told something about AI and ML and we laughed then in the 1990s. Classic example of ML which we all use almost on daily…

MLaaS helps and  features; “feature engineering”  as it’s very important and integral part to tell us how to do well. MLaaS allowing use of  common methods for different data types, including images, text, and logs. Also help is understanding how different techniques such as feature scaling and principal component analysis work can support. MLaaS…