Mindblown: a blog about philosophy.
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Medical Diagnosis
ML provides methods, techniques, and tools that can help in solving diagnostic and prognostic problems in a variety of medical domains. It is being used for the analysis of the importance of clinical parameters and of their combinations for prognosis. E.g. prediction of disease progression, for the extraction of medical knowledge for outcomes research, for therapy planning and support, and for overall patient management. ML…
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Speech Recognition
Speech recognition (SR) is the translation of spoken words into text. It is an interdisciplinary subfield of computing and linguistics that develops methodologies and technologies that enable the popularity and translation of speech into text by computers. Speech recognition is employed to spot words in speech. Voice recognition may be a biometric technology wont to identify a specific individual’s voice or for talker identification. It is also known as “automatic…
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Image Recognition
It is one of the most common machine learning applications.There are many situations where you can classify the object as a digital image. For digital images, the measurements describe the outputs of each pixel in the image. In the case of a black and white image, the intensity of each pixel serves as one measurement. So if a black and white image…
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High error-susceptibility
Machine Learning is autonomous but highly susceptible to errors. Suppose you train an algorithm with data sets small enough to not be inclusive. You end up with biased predictions coming from a biased training set. This leads to irrelevant advertisements being displayed to customers. In the case of ML, such blunders can set off a chain…
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Interpretation of Results
Another major challenge is the ability to accurately interpret results generated by the algorithms. You must also carefully choose the algorithms for your purpose.
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Time and Resources
ML needs enough time to let the algorithms learn and develop enough to fulfill their purpose with a considerable amount of accuracy and relevancy. It also needs massive resources to function. This can mean additional requirements of computer power for you.
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Data Acquisition
Machine Learning requires massive data sets to train on, and these should be inclusive/unbiased, and of good quality. There can also be times where they must wait for new data to be generated.
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Wide Applications
You could be an e-tailer or a healthcare provider and make ML work for you. Where it does apply, it holds the capability to help deliver a much more personal experience to customers while also targeting the right customers.
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Handling multi-dimensional and multi-variety data
Machine Learning algorithms are good at handling data that are multi-dimensional and multi-variety, and they can do this in dynamic or uncertain environments.
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Continuous Improvement
As ML algorithms gain experience, they keep improving in accuracy and efficiency. This lets them make better decisions. Say you need to make a weather forecast model. As the amount of data you have keeps growing, your algorithms learn to make more accurate predictions faster.
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