What is Machine Learning? Types, Examples

on January 14, 2020

What is Machine Learning?


what is machine learning


 

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.


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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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Types of Machine Learning**

 Machine learning is mainly divided into three types:


Supervised Learning


what is supervised learning


 

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.


Unsupervised Learning


what is unsupervised learning


 

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.


Reinforcement Learning


what is reinforcement learning


 

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.


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Top 5 Real —Life Examples of Machine Learning

The following are the top 5 real-life examples of ML.


1)Virtual Personal Assistants


virtual 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.


2)Videos Surveillance


video surveillance machine learning


 

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


social media machine learning


 

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.

  • People You May Know- Generally, ML works on a simple concept: understanding with experiences. Facebook, social media and social networking sites, regularly notices friends you connect with, the profiles you visit very often, your interests, workplace, or a group you share with someone etc. On the basis of continuous learning, Facebook will suggest a list of users with whom you can become friends with.
  • Face Recognition- When you upload your picture with a friend, Facebook will immediately recognize that friend. Facebook will check the poses and projections in the picture, notice the unique features, and then match them with the people in your friend list. In fact, the entire process at the backend is complicated and takes care of the precision factor, but at the front end, it seems to be a simple application of ML. 
  • Similar Pins- Machine learning is the key element of computer vision. This technique helps extract useful information from images and videos. In fact, Pinterest uses computer vision to identify the objects or pins in the images and recommends similar pins accordingly.

4)Email Spam and Malware Filtering


spam filter machine learning


 

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 with 2-10% variation and offers protection against them.


5)Online Customer Support


machine learning customer support


 

Nowadays, many websites offer the option to chat with the customer support representative, while they are navigating within the site. However, not all the website has a live executive to answer your queries. In many cases, you will be talking to a chatbot. These bots extract information from the website and present it to the customers. They understand the user queries better and serve the user with better answers. This is possible due to its machine learning algorithms.


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Conclusion


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Machine learning is growing so rapidly that it is being used in multiple fields and industries. Today, many big companies are increasingly investing in ML-based solutions to improve business decisions, increase productivity, detect disease, forecast weather, and do many more things that are complicated or time-consuming for humans to solve. In short, it can be said that, ML is a magnificent breakthrough in the field of artificial intelligence.