What is Machine Learning?
In today's modern world, machine learning, automation, and artificial intelligence are taking more and more ground by the minute, especially since companies are investing huge parts of their budgets into adopting these disruptive technologies. In a survey, 91.5% of the world's most important companies are already investing in machine learning, automation, and artificial intelligence.
Machine learning, or rather the idea machines can learn to ‘do’ without an explicit set of instructions (programming), has been the basis of many movies where humans end up getting the short end of the deal. But is machine learning truly that dire?
Unlikely. Machine learning, which is a subcategory of artificial intelligence, is simply a way for machines to imitate intelligent human behavior. It’s a type of data analysis that allows programs to learn via experience in order to complete complex tasks, much like humans problem-solve.
Working of machine learning?
Machine learning uses algorithms to process datasets in order to learn and improve accuracy. Supervised learning models offer computers labeled training data. Unsupervised learning models use unlabeled data. There are also models of training that fall between the two called semi-supervised learning.
Supervised learning
Supervised learning maps an input to an output based on example input-output pairs. A classic example of supervised learning is predicting house prices. Data in the form of square footage, features, number of rooms, location, etc are fed into the model. Data training can lead to accurate home price predictions from data inputs.
Another example is weather prediction. By using historical temperature, precipitation, wind, and humidity data, better predictions of future weather can be made.
Semi-supervised learning
Semi-supervised learning takes the same approach as supervised learning, but with less data labeling. For example, in semi-supervised learning, only 30% of 10,000 cat and dog images fed into a computer will include a “cat” or “dog” label.
Unsupervised learning
Unsupervised learning looks for previously unknown or undetected patterns to find clusters of data with commonalities. This kind of pattern recognition is a cliche in some bad tv shows, where it seems like certain characters have nearly superhuman abilities to connect disparate data. Think of Sherlock, House, The Blacklist, or any cop show.
In machine learning, unsupervised learning reduces the complexity of the problem by reducing the number of random variables needed to solve the problem. Algorithms cluster data in order to further reduce complexity.
Machine learning algorithms
It is a method that will allow system learning and improvement from experience alone, without explicit programming.
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