Before talking about machine learning, let's first understand what a machine is. So, A machine gets some inputs and converts it into the desired expected output of the user. This is the work of a machine.
Our world is surrounded by electronic gadgets and machines. Let's take an example of humans, we know that human beings learn from their past experiences and they try to improve.
Because our past experiences are stored in our memory and this is the reason why we improve our mistakes. But that's not the case with the machines.
Machines follow the instructions given by humans, but what if machines also start learning from their past data and improve it without any help from the humans.
This article on machine learning provides a general idea of its history, important definitions and applications.
What is Machine Learning?
Machine Learning is a part of Artificial Intelligence (AI) and computer technology. It uses the data and algorithm in such a way to reflect the process of human learning, step by step increasing its accuracy.
The main goal of machine learning is that machines work efficiently without any help from humans and improve the accuracy of work from past data history. In simple words, we want to make a machine that thinks like human beings. Machine learning is an important part of the expanding field of data science. To make decisions without being particularly programmed to make those decisions.
History of Machine Learning
Arthur Samuel introduced the phrase "Machine Learning" in 1952. In 1952 Arthur Samuel saw the first computer program which was able to learn as it ran. This program is related to computer gaming.
Machine Learning Methods
- Supervised Machine Learning
- Unsupervised Machine Learning
- Semi-Supervised Machine Learning
- Reinforcement Machine Learning
1. Supervised Machine Learning - In supervised Learning machine applied the previous trained data and improved data in the process. On the basis of this machine provides an output. In this supervised learning machines are trained with the usage of well labelled data. The labelled data means that kind of data which is already available with the correct output.
This is a process of providing the input data to the machine learning model and taking the correct output from the machine learning model. The objective of supervised learning is to make a machine which predicts output correctly.
Advantage of Supervised Machine Learning
With the help of supervised Learning , the machine learning model can give the output based on previous experiences.
Disadvantage of Supervised machine Learning
Supervised Learning is not suitable for complex problems.
2. Unsupervised Machine Learning - In unsupervised Learning , machines have to suppose the algorithms on the basis of input data. This is why these algorithms learn from test data and real data which are not labelled, categorized and classified.
This algorithm finds the similarities among the given data and provides the output based on those findings. This is almost similar to the human brain, when humans learn new things. The objective of the Unsupervised Machine Learning is to find the primary structure of data, combine the data on the basis of similarities and give the output on the basis of similarities of given data.
Advantage of Unsupervised Machine Learning
This is used for complex tasks as compared to supervised learning .
Disadvantage of Unsupervised Learning
The output of unsupervised learning is not less accurate because the data is not labelled.
3. Semi-Supervised Machine Learning - In semi-Supervised machine learning the algorithm is experienced with labelled data and Unlabelled data both. This algorithm contains less amount of labelled data and more amount of Unlabelled data previously. Semi-Supervised learning is used in speech analysis and so on.
Advantage of semi-Supervised Machine Learning
It is simple and easy to understand with high efficiency.
Disadvantage of semi-Supervised Machine Learning
It has less accuracy and is not considered to be network-level data.
4. Reinforcement Machine Learning - Reinforcement machine learning is a kind of algorithm or computer programming that communicates with the environment and learns behavior and acts like that. Its main objective is to improve the performance by learning. Reinforcement machine learning has no labelled data and it is completely based on its experience to learn.
It is learning by observing the actions of the environment. It is used in robotics, game playing, business and so on.
Advantage of Reinforcement Machine Learning
It is suitable for complex problems that cannot be solved by traditional methods.
Disadvantage of Reinforcement Machine Learning
It is not preferable for simple problems because this algorithm needs a lot of data and computation.
Applications of Machine Learning
- Image Recognition - one of the popular applications of machine learning is image recognition. It can recognize the image of person's, places and so on. Deep face technology is behind the automatic friend tagging suggestion.
- Traffic prediction - We all use google Maps when we travel to new places. It is also an application of machine learning. It provides the information of traffic, Short routes. It also provides the real time location and average time to reach that place etc.
- Product recommendations - We can see those recommendations in various E-commerce sites. Whenever we are using the internet we see a lot of recommendations of our choice . This is one of the popular applications of machine learning.
- Speech recognition - We know about Amazon Alexa, apple siri and google assistant . These are all the algorithms of machine learning.
- Self driving cars - Whenever we hear about self driving cars I guess we all get one name Tesla , this is an amazing application of machine learning . They use unsupervised learning to train self-driving cars to detect objects and persons while driving.