Machine Learning

Machine Learning is the act of getting the computer to learn and respond like a human do and improve the same over time through algorithms by providing them data and information in the form of real-world interactions and observation. Machine Learning takes the input data and gives the suitable and accurate output. If the relevant information is not found, then the machine learning process the data according to the algorithm.

Machine learning algorithms are categorized as supervised algorithm and unsupervised algorithm. Data scientists or data analysts use supervised algorithms to train the machines by providing input data into the machine and get the desired output along with feedback and predictions. Unsupervised algorithms don’t need training and will use iterative learning approach to review data and arrive at conclusions.

Machine learning is being used in a wide range of applications today. The news feed on Facebook uses machine learning to learn the member’s feed frequently and to also checks where they always visit to check posts and feeds. If a member frequently stops scrolling to read or like a particular friend's posts, the News Feed will start to show more of that friend's activity earlier in the feed.

The Facebook software uses statistical analysis and predictive analytics to identify patterns in the member’s data and use those patterns to check which posts and news feed the member frequently visits. Should the member no longer stop to read, like or comment on the friend's posts, that new data will be included in the data set and the News Feed will adjust accordingly. Machine learning is also entering an array of enterprise applications.

Customer relationship management systems use machine learning models to analyze email and prompt sales team members to respond to the most important messages first. More advanced systems can even recommend potentially effective responses. Business intelligence and analytics vendors use machine learning in their software to help users automatically identify potentially important data points.

Human resource systems use machine learning models to identify characteristics of effective employees and rely on this knowledge to find the best applicants for open positions. Machine learning also plays an important role in self-driving cars where deep learning neural networks are used to identify objects and determine optimal actions for safely steering a vehicle down the road.