Machine Learning

What is Machine Learning?

Machine Learning (ML) is a function of Artificial Intelligence (AI) that often uses statistical techniques to give computers the ability to “learn” on its own and improve from experience without being explicitly programmed. These ML algorithms receive data inputs and run statistical analytics and observations on it to find patterns that would ultimately help the computer learn automatically and make better decisions and predict results with better accuracy. The primary focus of Machine Learning program is to focus on the development of the computer programs automatically without any human intervention or assistance and take actions accordingly.

Machine Learning applications in day to day life

Similar to Data Mining and Predictive Modelling, Machine Learning process requires to search and observe the data and look for a pattern and tune the program actions accordingly. We encounter Machine Learning use cases very commonly in our everyday internet affairs. For example, the well-known ad recommendation services that give personalized recommendations based on your surfing history uses machine learning methods to run personalized ad delivery in real time. Fraud detection, network security, spam detection, threat detection, predictive maintenance are some other commonly used case of Machine Learning.

Machine Learning Methods

Machine Learning often is categorized as supervised and unsupervised.

In Supervised Machine Learning task, the algorithm runs analysis on the new data inputs by drawing insights from the known training dataset and providing the output values that predict future events. In this case, the system needs sufficient training and supervision on its Machine Learning skills from the Data Scientist and Analysts to provide specific targets for any new data set. The Data Scientist train the system by determining the variables, features and, the models that should be used to derive a prediction for a given new data set.

To the contrary, Unsupervised Machine Learning does not use any labeled or classified data set to develop its machine learning skills. Instead, they use an approach called as ‘Deep Learning’ to analyze data and draw a conclusion. The approach of ‘Deep learning’ also known as ‘Neural Networks’ is applied for complex tasks that cannot be achieved by the supervised learning system. For example, image recognition, natural language generation, and speech-to-text conversion and so on. The unsupervised learning system scutter through millions of unlabeled training data and automatically identifies the slight analogs among many variables. This process has come more into action after Big Data came into limelight as it requires an enormous amount of training data for its consumption.

Examples of Machine Learning

Remember when you opened the Amazon App and it shows you exactly the thing that you want to purchase on the home page itself. How do you think Amazon knows or remembers what you have been looking for? Well, through the Machine Learning technique Amazon give you highly personalized recommendations based on your previous purchase, surfing or any other activity.

Wondered how Facebook identifies the person in the picture that you just uploaded and suggests you tags? Alternatively, when suddenly you get too many feeds from one friend, while the other’s feeds cease from appearing. All of these adjustments, recognition, and recommendations are achieved through the process of machine learning. The social networks these days use some awe-inspiring technology to predict your action based on the past data or your activity in that particular app or site.