The day a child comes into this world, their process of learning begins the moment they open their eyes. But we’re in 2018, other than human beings and animals, now machines can learn too.
Apple Inc. recently launched its 4th generation smartwatch that can perform an electrocardiogram (ECG). It’s one of the finest examples of how machine learning has integrated into our lives seamlessly. Whether it’s Siri and Cortana helping you with every piece of information available or it’s Amazon and Netflix making suggestions, solving all your dilemmas for you based on your previous choices; it’s all part of Machine Learning which falls under the umbrella of Artificial Intelligence (AI).
The term ‘machine learning’ was coined by Arthur Samuel in 1959. Machine Learning by definition is a field of artificial intelligence that uses statistical techniques to give computer systems the ability to “learn” from data, without being explicitly programmed. It primarily focuses on the development of computer programs and how they use the data fed to improve their ability and expertise.
There are various methods in which machines learn. Some of these known to us until now are:
Supervised Machine Learning
In a supervised machine learning algorithm, the provided dataset is the teacher which further trains the machine. Once this process of learning is completed, the machine can start making predictions or any decisions.
Unsupervised Machine Learning
In unsupervised machine learning algorithm, there is no teacher; instead, the computer learns through observation by itself. It traces patterns and clusters in data sets and separates them accordingly.
Semi-Supervised Machine Learning
In semi-supervised machine learning algorithm, the machine is provided with a certain amount of labelled data for its awareness and then a large chunk of unlabelled data for classification.
Reinforcement Machine Learning
In reinforcement machine learning algorithm, the machine employs a trial and error method to come up with a solution. The machine is then rewarded by the programmer thus maximising the total reward.
Transduction Machine Learning
In transduction machine learning algorithm, the device tries to predict new outputs which are based on new inputs, previous training inputs and training outputs.
Learning to Learn
In learning to learn algorithm, the machine organizes its own process of learning by processing large datasets of information.
Machine Learning can be applied in every industry depending on their needs. Some of the examples are:
Healthcare
In the healthcare industry, ML has helped in diagnosing patient ailments beforehand depending on their previous medical records. The doctors can easily review these predictions.
Social Network
All digital media websites provide match preferences depending on consumer choices. This, in turn, improves the consumer-website rapport.
ML can process large sets of data instantly; thus it helps in analyzing the past credit history of customers. Through this data, it can predict any fraudulent activity on a particular credit card.
A lot of customer traffic and purchasing patterns can now be predicted through machine learning.
Biology
It is proving to be helpful in finding patterns in gene mutations. For example, cells that could be cancerous in the future.
Platforms like Amazon Web Services and Microsoft Azure provide access to hardware which is required to train and run ML models. Similar to Azure’s Machine Learning Studio, Google also launched a drag and drop service that builds custom image-recognition models called as Cloud Auto ML. And it doesn’t require you to have prior machine learning expertise. Usually, the programming languages used are Python, R, C++, Java and MATLAB.
According to research, Machine Learning has been predicted to take over 50% of the jobs meant for humans. But a machine is a machine, which cannot exist without human intervention. So, it’s not the machine apocalypse yet, and we all can start with some basic courses in Machine Learning to be found at Global Tech Council.
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