Machine learning is an essential part of technological, industrial and professional advancement. Each day, we come across various instances of machine learning which is a vital part of our day to day activities. It is the capacity of a system to learn from past data and determine present outcomes by permuting and combining built-in algorithms. It is a break-through of artificial intelligence which uses existing statistics to learn and create outcomes. It is used in face detection, speech recognition, medical diagnosis, classification etc.
The type of data which is fed into the machine as an input influences and determines the outcome of the prediction. The algorithm is reinforced numerous times in order to achieve the desired output. Gradually, this helps the machine to learn on its own on a real-time basis to produce the most efficient, effective and optimal outcome which has an improved rate of accuracy over the passage of time.
Machine learning is divided into two main types, namely supervised learning and unsupervised learning. Under the former type, the data is supervised and is used to train the machine. Once the training is complete, the machine is capable of responding to unknown data and responds accordingly. The latter type involves feeding the machine with unlabeled data without subjecting the machine to guidance. The machine tries to determine a pattern and tries to provide the desired response. The difference between the types is mainly with respect to the level of the involvement of the human mind for the outcome. Apart from the given types, there exists a third type of machine learning known as reinforcement learning which is traditional in nature. The machine gives an outcome through the process of trial and error. It determines the best outcome based on which action results in higher rewards in comparison to the other outcomes.
Machine learning courses has changed the outlook towards the idea of data extraction and interpretation. Its sophistication has helped analyze large chunks of data in one go. It can produce accurate analysis and results by the development of effective algorithms which are data driven and are capable of processing data within a short period of time. We can relate the concept of data optimization in machine learning to the concept of effective assembling and manufacture of products in mass production of products.
However, every action has its pros and cons. With respect to machine learning, the model is trained and validated based on a narrow scope of data sets. Thus, biased data can lead to biased outcomes. Irrespective of the fact of whether the learning is supervised or otherwise, it requires the involvement of the human mind at any given stage which defeats the purpose of machine learning. It requires a massive amount of time, money and manpower which many entities do not have access to. Even with the existence of required resources, additional resources are necessary to improve the computing power of the machine. Also, the accuracy of the algorithms and the data must be checked on a regular basis.
Thus, given the wide range of access to information, it is the responsibility of entities to make an effective and ethical use of machine learning. Artificial intelligence must be used only as a tool to simplify working methodologies and not as tools for takeovers by technology.
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