Algorithm and Machine Learning
Machine Learning is the science of creating algorithms and program which learn on their own. Once designed, they do not need a human to become better. ML involves feeding complex algorithms for data processing from huge amounts of data. The result is computer systems which become capable of learning. Its only when you try to make a machine act like humans, you require algorithms to be fed into the machines. Progressively, the machines are getting smarter as they develop cognitive intelligence in them. They cannot just act as commanded but can make decisions by developing new algorithms on their own. Intelligent algorithms do not only decode the data but analyze and provide the right output at the right time.
The “learning” part of machine learning means that those programs change how they process data over time, much as humans change how they process data by learning. So a machine-learning algorithm is a program with a specific way to adjusting its own parameters, given feedback on its previous performance in making predictions about a dataset.
Machine learning focuses on enabling algorithms to learn from the data provided, gather insights and make predictions on previously unanalyzed data using the information gathered.
Machine Learning enables machines to improve themselves as time goes by and as algorithms become more and more sophisticated. ML is based on algorithms that can learn from data without relying on rules-based programming. Machine Learning algorithms can predict patterns based on previous experiences. They could assist, simplify, and speed up the development of new materials through simulations. Data munging (or data transformation) is used in Machine Learning to restructure data in a way that could be used by a learning algorithm. Thanks to advanced ML algorithms powered by tons of data that computers are now able to perform handwriting recognition much more accurately than they were ten years ago.
The new Google Search algorithms use ML and new neural networking techniques to better understand a query word in a sentence and its context. This latest refinement uses ML to improve how it handles conversationally phrased English-language requests.
Instead of planning traditional fixed machine maintenance schedules, machine learning’s predictive algorithms are being used to design flexible plans. Product testing and quality control also have progressively become automated. The secret sauce in machine learning is algorithms that are capable of quickly processing large, interconnected data sets that would be too complex for the human brain.