Artificial Intelligence and Machine Learning
are used interchangeably by a lot of people but Artificial Intelligence is more like a superset of Machine Learning and is even a wider field in scope. Though it is a bit vague to define Artificial Intelligence (boundaries) as it keeps on changing with the advancement of technology, in simple terms, AI can be defined as a science to make computers behave in a manner that brings them closer to human levels of intelligence and capability. It is quite a broad and vague field in terms of scope but the overall idea is to simulate human intelligence to solve problems or behave in an ‘intelligent’ way, similar to how humans might behave in a given situation. For example, a robot performing certain tasks based on situation instead of simply performing repetitive tasks (automation) is an example of AI. Thus, Machine learning can be considered a branch or sub-field of AI, serving to achieve the overarching aims of AI.
What are different types of Machine Learning algorithms?
There are many different types of learning algorithms. However, there are two general types: supervised and unsupervised. In simple terms, in supervised learning you teach the computer how to do something and computer learns, typically by giving the computer feedback about its performance (the algorithm is given feedback about its performance on a set of training data). In unsupervised learning, the computer must infer patters in the data without explicit guidance about which answers are right or wrong.
In supervised learning, we provide the algorithm with a data set which has the real-world scenario ‘answers’ or ‘labels’ (the correct input-output pairings) and then based on this data the algorithm learns and is able to generate answers (predictions) even for data it has not seen before, based on what it learned from the training data.
In unsupervised learning, the algorithm is provided with the data but we don’t have labels for each data point. In other terms, correct ‘answers’ or ‘labels’ are not given. Instead we are just given a data and expect the algorithm to find some sort of structure in the data. This type of algorithm can be used when we need to find some pattern in the data and cluster data into different sets.
Other type of Machine Learning algorithms include Reinforcement Learning, Semi-supervised Learning or Recommender Systems.
Machine Learning usage has become so common that you probably use it many times a day without even knowing it. For example, when you search on Google or Bing or any other search engine, in the background there is a learning algorithm which has learned how to rank pages based on user queries. Similarly, when you see the photo detection feature on different social media applications or see spam filter filtering out bogus/unwanted emails in your mailbox, behind the scene is a Machine learning algorithm which learns and detect faces or spams emails respectively. A more recent technology use case is the advent of self-driving cars.
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