Agent: In reinforcement learning, the entity that uses a policy to maximize expected return gained from transitioning between states of the environment.Once all the examples are grouped, a human can optionally supply meaning to each cluster. Clustering: Grouping related examples, particularly during unsupervised learning.Association: Discovering interesting relations between variables in large databases where the connections that found are crucial.estimation of the price of the house based on size. Regression: Estimating the most probable values or relationship among variables.Classification: Separating into groups having definite values Eg.Labelled data: It consists of a set of data, an example would include all the labelled cats or dogs images in a folder or all the prices of the house based on size, etc.Before getting into the various types of machine learning and as we have already mentioned that AI comprises advanced algorithms that follow a mathematical function, let us learn some of the machine learning jargons that are commonly used. There are typically three types of machine learning: Supervised Machine Learning, Unsupervised Machine Learning, and Reinforcement Machine Learning.
These predictions could be accurately recognizing speech or spotting humans or traffic signs in front of a self-driving car.
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In a nutshell, machine learning can be described as the process of developing a computer system on how to make highly accurate & reliable predictions when furnished data. One of the most important aspects of machine learning is that it doesn’t require any human intervention to make changes when required. Machine learning has the ability to scale itself when exposed to more volume of data unlike expert systems or knowledge graphs.
Popular AI tools like expert systems, knowledge graphs are not falling under machine learning. This means all machine learning considered as Artificial Intelligence, however not all AI counts as machine learning. Now let’s take a deep dive into Machine Learning & Deep Learning. Examples include visual perception, speech recognition, decision making, translations between languages, etc. But generally AI comprises advanced algorithms that follow a mathematical function, which is able to handle higher processes similar to humans. To common people AI can be referred to an automated responder (assistant) to a voice recognition system like Siri, Alexa etc or chess-playing computers or self-driving cars. The three forces ( Data, Algorithms and Computing power) that brought AI to life is being popularly quoted as Trinity of Artificial Intelligence. As the volume of data business generates is growing at an exponential rate with ever improving computer-programming techniques/algorithms and TPU and quantum computing power, AI maturity and the potential use cases are also growing along with it. There are several top-notch technologies that come under the umbrella of AI. Artificial General Intelligence (AGI), which has cross-domain capability (like humans), can learn from a wide range of experiences (like humans). Whereas Strong AI is an AI that understands itself well enough to self-improve. Such AI normally referred as Weak AI that bound to perform only certain range of tasks. Considered together, all these if-then statements are sometimes called rules engines, expert systems, knowledge graphs or symbolic AI. The if-then statements are simple rules programmed by humans. It can be a complex statistical model or an if-then statement. According to the father of Artificial Intelligence, John McCarthy, it is “The science and engineering of making intelligent machines, especially intelligent computer programs.” AI is a computer program that performs something smart. Before understanding Machine Learning & Deep Learning, let’s first understand AI in detail.
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