What is Machine LearningAPRIL 9, 2020 | BY SATVIK JAGANNATH MACHINE LEARNING
Machine learning has definitely been one of the most talked about fields in recent years, and for good reason. Every day new applications and models are discovered, and researchers around the world announce impressive advances in the quality of results on a daily basis.
Machine learning (ML) is the scientific study of algorithms and statistical models that computer systems use to perform a specific task without using explicit instructions, relying on patterns and inference instead. It is seen as a subset of artificial intelligence.
Machine Learning - Evolution
Machine learning as a discipline is not an isolated field—it is a part of a wider domain, Artificial Intelligence (AI). But as you can guess, machine learning didn't appear randomly out of nowhere. It has evolved over the years and can be categorised into four steps:
- The first model of machine learning was a rule-based decision-making system based on algorithms that involved data in itself. This implied that all the possible options will be hardcoded into the model beforehand by an expert in the field. This structure was implemented in the majority of applications developed since the first programming languages appeared in 1950. The outcome of this model was usually a Boolean value - True or False.
- The second stage of machine learning involved statistical reasoning, where probabilistic characteristics of the data was given a say, in addition to the previous choices or rules that was coded in advance. This better reflects the fuzzy nature of real-world problems, where outliers are common and where it is more important to take into account the nondeterministic tendencies of the data than the rigi approach of fixed questions-to-outcome mappings. This disciple involved using of the famous Bayesian probability theory.
- The machine learning stage is where we had generalized models of data. The models are capable of generating their own feature selectors, which aren't limited by a rigid target function, as they are generated and defined as the training process evolves. Another differentiator of this kind of model is that they can take a large variety of data types as input, such as speech, images, video, text, and other data types.
- AI is the final step in the scale of abstraction capabilities that, in a way, include all previous algorithm types, but with one key difference: AI algorithms are able to apply the learned knowledge to solve tasks that had never been considered during training. This involves solving problems like how human-brain works, but, with much higher compute power.
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