The main distinction between machines and humans is that humans learn from experience whereas machines work based on instructions. But we can make even machines work from experience this is called data in technical language. That is how machine learning came into being. Learning means acquiring knowledge, behaviour or any skill through experience and studying. Machine learning is among the emerging fields of computer science technology. Nowadays, it has become a necessity to perform various tasks which involves interpreting huge data sets. Machine learning is being used by us in our day to day lives as well and we are not really aware about it. For example: Age recognition feature in our smartphones, Google assistant or safety features in our cars for precautionary purposes in case of any accident.
Need of Machine Learning
There is a requirement for machine learning in case of complex tasks which cannot be performed by humans or as a helping hand so as to make our life easier. Sometimes, technology is needed. Not everything can be performed by humans. Nowadays, we are surrounded with huge amount of data. It is an essential part of our lives these days. Hence, machine learning is necessary to analyse huge amount of data.
Introduction
Machine Learning is a part of artificial intelligence wherein a process of self learning happens without being directly programmed. A computer program learns with Experience- E, against some Task- T and Performance Measure- P, if performance of the given task which is measured by P improves because of experience.
Evolution : How it evolved?
The researchers who were involved with artificial intelligence used to use modern technologies as compared to past. With time, they started identifying that the computers can adapt certain behaviours through data observation and experience.
Let’s try to understand this concept using the following examples:
Let’s suppose that your email program learns with time which mails you mark as spam and which are not marked as spam and accordingly filters them automatically. Here, this concept will be applied as follows:
- Learning the activity of the user through observation is the Experience, E.
- Classifying the E-mails as spam is the Task, T.
- Correctly classifying the number of mails as spam or not is the Performance Measure, P.
Machine Learning Methods
The main machine learning methods include supervised and unsupervised learning. Apart from these there are some other methods as well.
Supervised learning
The computers are taught how to do something. They are properly trained by defining various inputs and outputs. The algorithm is provided with a set of inputs along with correct output. It then tries to find errors using the given input and outputs. Classification and regression techniques are used in supervised learning.
- Classification: In case of classification, input data is divided into different categories. For example: classifying mails as spam, speech recognition
- Regression: In case of regression, data which involves continuous analysing is considered. For example: weather prediction
Semi supervised learning
Proper training is not given in case of semi supervised learning. Few information is provided with some of missing output.
Unsupervised learning
The computers are not taught how to do something. They learn with observation and experience. The correct output is not provided to the system. It needs to analyse the data properly to get to some conclusion. For example: classifying the consumers in different segments as per their repeated choice and preferences which will help in marketing and sales.
Clustering
Groupings or hidden patterns are analysed in clustering.
Reinforcement learning
Reinforcement learning includes three aspects: the agent, actions and environment. The agent Is the one who is going to take the decision i.e. the learner. The environment refers to whatever the agent interacts with. The actions include whatever is done by the agent. He/She learns from the environment through interaction and receives rewards for performing actions. The agent attempts to maximize the reward which leads to better performance. For example: a child learns by observing his/ her environment. In a shooting game, we try to shoot as many as possible to score more points and reach to the next level which is similar to maximizing our rewards.
Application
Machine Learning is used in diverse fields for smooth functioning of an organisation.
- Marketing: Machine learning is also used in marketing. While we do online shopping, choices are shown to us according to our previous preferences/ buying history, smartphone activity , etc. This is done by analysing data and hence creating a good shopping experience for us.
- Transportation: Analysing data and observing patterns can be useful in transportation sector as well. Self driven cars is also one of the application.
- Health sector: It is also useful in identifying certain diseases or bringing in new ways of treatment. The patient’s health can be analysed using such technology.
- Energy: Machine learning can also help in identifying new sources of energy.
- Financial services: Machine learning has its application in financial sector as well.
- Fraud prevention: It helps in preventing frauds by analysing the data properly. It is not easy to identify fraudulent activities because of modern technological advancements used by hackers. Hence, it proves to be very useful. the algorithm tries to understand the repeated actions taken by the account owner and tries to observe any change in activity.
- Analysing risk : Machine learning also helps in better risk management. The creditworthiness of others in the market is analysed deeply and help the investors in efficient risk management.
- Digital assistants: Digital assistants like Cortana in case of Microsoft, Siri in case of Apple,etc help in making our life easy. Such digital assistants perform specified tasks as heard i.e they work on our voice commands. It thus helps in saving our time as well.
Hence, with growing advancements in technology Machine learning can be useful to humans in a great way. With the world getting modern and technological advancements , we need to adapt ourselves according to the environment and act smartly by taking benefit of such a technology.
Lovely.