14 Jan 2020
Machine Learning (ML) is an application of Artificial Intelligence (AI) that enables systems to automatically learn and improve from experience without being explicitly programmed. Therefore, instead of writing the code, you need to feed the data to the generic algorithm, and the algorithm/ machine builds the logic based on the given data. It focuses on the development of computer programs that can access data and use it to learn themselves.
Hence, the main aim of machine learning is to let the computers learn automatically without human intervention or assistance and adjust actions accordingly.
Generally, ML algorithms are trained using a training data set to create a model. Whenever a new input data is introduced to the ML algorithm, it makes a prediction on the basis of the model. The accuracy of prediction will be evaluated, if the accuracy is acceptable, the Machine Learning algorithm will be deployed and if the accuracy is not acceptable, then the Machine Learning algorithm will be trained repeatedly with an augmented training data set.
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Machine learning is mainly divided into three types:
Here, there is a dataset which acts as a teacher and trains the model or the machine. Once the model is trained, it can start making a prediction or decision when new data is given to it.
Here, the model learns through observation and finds structures in the data. So, when a dataset is given to the model, it automatically finds patterns and relationships in the dataset by creating clusters in it. However, it cannot add labels to the cluster.
An agent should interact with the environment and identify what is the best outcome. For a right or a wrong answer, the agent is rewarded or penalized with a point. On the basis of the positive reward points gained, the model trains itself. When trained again, it will predict the new data presented to it.
The following are the top 5 real-life examples of ML.
1)Virtual Personal Assistants
Some of the popular examples of virtual personal assistants are Siri, Alexa, Google Now etc. They help you in finding information, when asked over voice. All you have to do is activate them and ask questions. For answering, the personal assistant will look out for the information, recall your related queries or send a command to other resources like phone apps to collect info. You can also instruct assistants to perform certain tasks. In fact, ML is a vital part of these personal assistants, as they collect and refine the information on the basis of your previous involvement with them. Then, this set of data is utilized to deliver results that are tailored to your preferences.
Nowadays, the videos surveillance systems are powered by Artificial Intelligence. This makes it possible to detect a crime before they happen. AI powered video surveillance system tracks unusual behaviour of people like standing motionless for a long time, stumbling, or napping on benches etc. Thus, the system can give an alert to human attendants and avoid mishaps. All this happens with machine learning performing its task at the back-end.
3)Social Media Services
In fact, social media platforms are utilizing machine learning for their own and for user benefits. The following are a few examples that you may be noticing, using and loving in your social media accounts, without realizing that these wonderful features are the applications of ML.
4)Email Spam and Malware Filtering
Email clients use a number of spam filtering approaches. In order to ensure that these spam filters are continuously updated, they are powered by ML. When rule-based spam filtering is done, it may fail to track the latest tricks adopted by spammers. Some of the spam filtering techniques powered by ML are Multi-Layer Perceptron, C 4.5 Decision Tree Induction etc.
In fact, more than 360,000 malwares are detected every day and each piece of code is 90-98% similar to its previous versions. The system security programs powered by machine learning understand the coding pattern. Thus, they detect new malware 