Machine Learning

After the Internet and Android, what is next? It’s Machine Learning!

By definition, machine learning (ML) is a type of artificial intelligence (AI) that provides computers to learn without being explicitly programmed. “ML is the study of computer algorithms that improve automatically through experience” is another definition by famous computer scientist Tom Mitchell. In machine learning, we make such algorithms that improve itself by finding the pattern in present data without being programmed later on.

Simplest and interesting example is ‘Google Search’; whenever we search something (as a keyword) repeatedly, Google search automatically shows that particular keyword next time, with just a few starting alphabets.

ML entirely depends on the data; algorithm passes through the new data and makes new improvements in existing model.

Some major paradigms of ML are supervised learning, unsupervised learning and reinforcement learning. In supervised learning, training data comprises examples of the input vectors along with their corresponding target vectors wherein unsupervised learning pattern is found in given data and reinforcement learning is training by rewards and punishments. ML helps machines to improve itself with every new data.

IBM is working with machine learning and artificial intelligence for its major project i.e. Watson. Some other applications of ML are Computer vision including object recognition, search engines, detecting credit card fraud, stock market analysis and medical diagnosis.


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