Machine learning is a process which allows a machine (computer) to show the behavior not programmed preliminary. Using algorithms, multiply extracted from data, machine learning helps the computer find the needed request without explicit programming. Today machine learning is a first-class ticket to the most successful career growth in the field of data analysis. It involves information technology and statistics, and uses this data to enhance the results. This technology is a must-have skill for every analyst striving for the improvement and acceleration of the data collection and result analysis. Newsfeed of Facebook social network uses machine learning to analyze the channel of every element. For instance, while scrolling the newsfeed you stop to read or like the post of one of your friends, the system remembers it and subsequently shows you more news from this friend. Actually the software simply uses statistical and predictive analyses in order to identify algorithms in the user’s data and to apply them in newsfeed. If later on you don’t read the posts of this friend, new data will be input in the data set and newsfeed will be corrected accordingly. This called iterative aspect of the machine learning – ability to independently adapt to the new data. Exactly machine learning specialists are presented in TOP experts’ category down below: Middle and Senior level with strong programming skills in popular languages (mostly Java and Python). These talents are specialized in Digital Signal Processing, Recommender Systems, Computer Vision, as well as Sentiment Analysis, Computer Linguistics and Natural Language Processing for more niche project requests.

Predictive or forecasting analytics is, first of all, a variety of statistics methods, data analysis and game theory applied for the analysis of current and historical information to ensure future data prediction. The most popular application of such an analytics is credit scoring which helps to estimate the client’s creditworthiness when issuing loans in the bank. Here it goes about how to avoid walking twice in the same river: any credit scoring is based on the historical facts, so if a group of clients has previously failed to repay loan on time and you somehow have similarities with this group, then your application for credit will likely be turned down. But this is just one of the spheres where predictive analytics is applied; it can be also used for product design, targeted audience selection, next best offer, etc. If you still don’t use predictive analytics, we would recommend you to take a closer look at these techniques as they can essentially enhance your business efficiency.