Insights into Evoluiton of Machine Learning
Machine Learning is a branch of Artificial Intelligence based on the idea that systems can learn from data. It can automate analytical model building of data analysis to make decisions and identify patterns with minimal human intervention. Machine Learning of today is not the same as it was in the past, thanks to new computing technologies. It was basically born from pattern recognition.
A researcher wanted to test if a computer can learn to perform certain monotonous jobs with being programmed and can they do them using data. In this concept they placed the machines into doing iterative jobs and expose them to data using patterns so that they are able to adapt independently. It was research to check if machines learned from computation and produce, relatable, reliable decisions and results. It is a science that is not new but has gained fresh momentum.
The recent development in this area has been on the use of big data- to apply complex mathematical calculations and faster and over and over again to gain the desired output without errors. Widely publicized machine learning examples that we might be familiar with are Google cars, offers from Amazon and Netflix, Twitter to know what your customers are saying using linguistic rule creating and the most important uses of it in fraud detection.
Importance of Machine Learning (ML)
Data volumes are increasing day by day through data mining techniques. Companies want this data collected to be analyzed using Bayesian analysis with the help of easily affordable computational processing methods and what more by saving the data in an affordable manner.
Machine Learning becomes important with more and more data coming into market and it needs quick and automatic models to produce bigger and more complex data and deliver accurate and faster results. By building precise models, an organization has a better chance of identifying profitable opportunities – or avoiding unknown risks. To create a good machine learning system one requires; data preparation capabilities, algorithms, iterative process, and automation, ensemble modeling and scalability. A target in ML is called a label, called a dependent variable in statistics, a variable is called a feature in ML and transformation in statistics is called a future creation in ML.
By using algorithms to analyze data, organizations can make better decisions without human interventions and learn more about the technologies that are shaping the world. There are obviously some challenges and opportunities for organizations in this field that they can gain or overcome using skilled workforce. Moreover ML can also be applied to Internet of Things (IoT).
ML can change the way an organization manages and how it can change them to adapt to the world of everyday challenges. Banks mainly use ML to get important insights into data and to prevent fraud. Insights can give banks to consider investment opportunities, data mining can identify high-risk clients and use cyber surveillance to pinpoint warning signs of fraud. Moreover, ML can be used by banks to enhance customer experience with apps, understanding their logon patterns so that an attack can be avoided.