Where do we Use Machine Learning in Our Day-to-Day Lives?

Machine Learning

Machine learning is the application of Artificial Intelligence (AI) that offers the ability to the machines to automatically learn and enhance form experience without the need for clear programming into it. Machine learning stresses on developing computer programs that can easily access data and use the same for their learning. The procedure of learning is initiated by observing or information like instructions, examples, or direct experience for patterns in data and improvement in decision making in future based on the provided examples. The main goal of machine learning is to enable computers to learn automatically with no human assistance or interference and perform actions accordingly.

There are several methods of machine learning. The machine learning algorithms are divided into unsupervised and supervised.

  1. Unsupervised Machine Learning Algorithms are applied when the data required to train is neither labelled nor classified. Unsupervised learning learns how machines can understand a function to present a hidden structure from non-classified data. The machines do not describe the right result, but explores the information and can calculate conclusion from the datasets to reveal the hidden structures from non-classified data.
  • Supervised Machine Learning Algorithms is used when the past learned things need to be applied to new information with labelled examples for predicting future actions. Beginning from the analysis of known training information, the learning algorithm creates a conditional function for making forecasts regarding the results. The machine is capable of presenting targets for new input after enough training. The algorithm is also capable enough to compare its result with the real result. It can also proficiently find errors for alteration and change it according to the model requirement.
  • Reinforcement Machine Learning Algorithms is the method that communicates with its surrounding by creating actions and identifies rewards or errors. The most relevant features of reinforcement learning search and delayed and trial and error rewards. This method enables the software and system agents to automatically identify the best behaviour within a particular framework for increasing its performance. The agent requires simple rewards for learning the best action, which is termed as a reinforcement signal.
  • Semi-Supervised Machine Learning Algorithms is placed between unsupervised and supervised learning, since both of them use unlabeled and labeled information for training, generally a large quantity of unlabeled data and small quantity of labeled data. The machines that use this technique or method can efficiently improve their learning precision. Normally, semi-supervised learning method is selected when the labeled information requires genuine and relevant resources for training or learning from the same. Else, obtaining unlabeled information usually does not require extra resources.

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Some of the common examples of Machine Learning are:

Predictions while Commuting:

The GPS services that are used by almost everyone, is the best example of machine learning. The velocities and central locations are saved in the server for management of traffic. This management is then displayed on the screen that supports the user for navigation. Another best example is the online transport networks i.e. online taxi booking applications.

Social Media Services:

When the user uploads a picture with a friend on Facebook, the application identifies the friend by capturing the similar poses and features. This is the face recognition feature of the application that may look simple, but is complicated at the back end.


Video Surveillance:

It is of course difficult for a single person to monitor multiple screens with CCTV cameras. Use of ML allows the monitoring system to alert the observer in case of any unusual behavior. The CCTV camera records the unusual activity or a motionless act for long time and signals the system for the same.

Besides, virtual personal assistants, online customer support, malware filtering and email spam, product recommendations on online shopping portals, search engine result refinement, and online fraud detection are other examples.

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