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“A year spent in artificial intelligence is enough to make one believe in God.”
— Alan Perlis
In this day and age of digitalization, humans and machines are living together. As far as we, i.e. humans, are concerned, we have been evolving by learning from our past experience for millions of years. On the flip side, however, the era of machine learning has just started.
Since the arrival of avant-garde technology, the job of managing processes has become quite easy for humans. For instance, we can do shopping, banking, etc. from our comfort zone. Actually, it wouldn’t be wrong to say that living without the present state-of-the-art technology could be very difficult for us.
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Expectations from machines have been increasing with every passing day. Many developed countries like USA, Australia, etc. have been working on artificial intelligence (AI) to make those things possible which were beyond our imaginations just a few decades ago.
With the help of AI, machines can learn from their own experience without being programmed for new things. This is known as Machine Learning (ML).
Machine learning, an application of artificial intelligence, lets systems learn from the stored data and give outcomes according to the situation. Seeking some real-time examples related to ML? Here’re some that may act as eye-openers:
While checking desired products on e-commerce websites, generally, similar products of other brands also come out as suggestions. Have you ever thought who decides those recommendations? Well, pal, it is machine learning at work!
Another example: Sometimes, people get contacted by banks or finance companies regarding a loan or an insurance policy. But the question that arises here is ‘Do they contact all their customers?’ Of course not! They select only those customers who seem potential loaners or borrowers. In doing this, the underlying basis is none other than machine learning algorithms.
Machine learning helps build models from sample data which can help automate the decision-making process and be used for prediction and forecasts. There should be constant machine learning model management after training an ML model to make it perform efficiently.
Curious about the types of machine learning algorithms? Let’s get the ball rolling:
You may already have got an idea about supervised learning, as its name is self-explanatory. To leave nothing to chance, however, we like to mention that these machines require a ‘supervisor’ to learn. Here, a dataset acts as a teacher, and essays the role of a ‘trainer’ of machines.
After the training, the output generated by the machines gets checked against the intended result. If there’s some difference, a process gets initiated to find errors. After identifying and rectifying the ambiguities, the machines run again to check whether accurate results are being generated or not.
Contrariwise to supervised learning, these machines don’t need a teacher in order to learn. In unsupervised learning, all that needs to be done is giving an initial protocol and dataset to a machine. Once it’s done, the machine automatically starts learning by creating clusters after finding out patterns and relationships in the dataset.
Here, it is worthy to note that machines cannot add labels to the created cluster. For instance, they cannot say if an object belongs to ‘apples’ or ‘mangoes’ without prior classification; however, they can separate all the mangoes from the group of apples.
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To describe semi-supervised learning in the simplest manner, you can say it is the combination of supervised and unsupervised learning. In supervised learning, a machine gets labeled data to learn, while unlabeled data gets provided to a machine when unsupervised learning is used.
By means of semi-supervised learning, learning accuracy can be improved very easily. Customarily, semi-supervised machine learning gets preference when the labeled data isn’t enough to train a machine.
Reinforcement learning, a type of dynamic programming, trains a machine as per the system of reward and punishment. For better understanding, if machines perform in the way which they are supposed to, they get reward points and vice versa.
Actually, the idea behind this is to make certain that machines act appropriately in the environment they are put in. Furthermore, machines are deemed to swing into operation once they start scoring maximum reward points and making negligible errors.
After understanding machine learning’s basics, we hope that you have got a better insight into machine learning (ML). Here, we would like to mention that many enterprises have been using machine learning applications so that business growth can be achieved in an effective manner.
There are many reputed vendors like Amazon, Google, IBM, etc. that have immense experience in handling ML activities that include data collection, data preparation, etc. For these activities, they are being approached by those companies that really want to achieve their business objectives as quickly as possible.
Are we done here? Nope. After going through several research reports, we have got our hands on some stats that have the potential to leave you stunned. So, take a gander:
Finally, we are finished with elaborating upon the prominence of machine learning. Hope you have got all the information that you were seeking, and enjoyed the whole tour of this write-up.
Thanks for staying connected till the end!!
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Machine learning, an application of artificial intelligence, lets systems complete tasks without requiring any specific programming. Willing to learn about machine learning in-depth? Well, you can consider this write-up worthy to read: Machine Learning – A New Wave of Intelligent Automation.
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