Machine learning is very common in the industries these days, but a large number of people are not clear on what machine learning is all about.
Machine learning is the technique or science to teach the machines how to learn by themselves and improve from experience without being explicitly programmed. The main reason to teach the machines to learn by themselves is to increase the efficiency of machines as machines can do high frequency repetitive work with high accuracy without getting bored unlike human beings. Humans can get bored by doing tasks repetitively like cleaning and mopping the floor or windows of a building. On the other hand, we can teach the machines to clean and mop the floor on their own. The machines would perform the same task far better than humans since they would never get bored or tired.
Machine learning aims to enable the machines to learn and think on their own in order to decide tasks like whether the floor needs to be cleaned and mopped or for how long does the floor need to be cleaned? The machines take data from the environment and forward it to the machine learning model to decide what work should be done and when. However, things which are generally intuitive to humans can be very difficult for machines. We need to demonstrate a specific task to a human a few times before they can perform it on their own. But, we need to collect a lot of data along with the required outcomes in order to teach the machines to perform specific tasks and this is where machine learning comes into play. A machine learning model would take a lot of data from the environment to help the machine understand various tasks and outcomes. For the given example of cleaning and mopping, the machine learning would help the machine to understand the intensity of cleaning, duration of cleaning and type of cleaning based on the nature of the floor and condition of the environment.
Everyone’s life in already heavily impacted by machine learning. The smartphones now detect faces while taking photos or the depth effect created on clicked pictures or for unlocking the smartphone with face recognition features. The social media sites auto tag you in a photo that looks like you. Also, they recommend your friends and ads you might be interested in. Online shopping sites will recommend products based on your recent search or browsing history. Google maps can tell you the estimated time it might take to reach a destination. The voice assistants like Apple’s Siri, Google Assistant, Amazon’s Alexa and Microsoft’s Cortana are also powered by machine learning.
The applications of machine learning courses are immense. There is also a huge potential of machine learning in healthcare to plan recovery paths of patients or to provide tools for better diagnoses. However, due to a lack of suitable data, wrongly chosen algorithms, privacy problems, or lack of resources and evaluation problems, the machine learning programs had failed many times. In 2018, a pedestrian was killed after a collision from a self-driving Uber which failed to detect the pedestrian.
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