Supervised vs Unsupervised Machine Learning
Machine Learning is a branch of artificial intelligence that gives systems the ability to learn automatically and improve themselves from the experience without being explicitly programmed or without the intervention of a human. Its main aim is to make computers learn automatically from the experience. In this article, we will also see the comparison between Supervised vs Unsupervised Machine learning.
Requirements for creating good machine learning systems
So what is required for creating such machine learning systems? Following are the things required in creating such machine learning systems:
- Data – Input data is required for predicting the output.
- Algorithms – Machine Learning is dependent on certain statistical algorithms to determine data patterns.
- Automation – It is the ability to make systems operate automatically.
- Iteration – The complete process is an iterative i.e. repetition of the process.
- Scalability – The capacity of the machine can be increased or decreased in size and scale.
- Modeling – The models are created according to the demand by the process of modeling.
Machine Learning methods are classified into certain categories. These are:
- Supervised Learning – In this method, input and output are provided to the computer along with feedback during the training. The accuracy of predictions by the computer during training is also analyzed. The main goal of this training is to make computers learn how to map input to the output.
- Unsupervised Learning – In this case, no such training is provided leaving computers to find the output on its own. Unsupervised learning is mostly applied to transactional data. It is used in more complex tasks. It uses another approach of iteration known as deep learning to arrive at some conclusions.
- Reinforcement Learning – This type of learning uses three components namely – agent, environment, action. An agent is the one that perceives its surroundings, an environment is the one with which an agent interacts and acts in that environment. The main goal in reinforcement learning is to find the best possible policy.
How does machine learning work?
Machine learning makes use of processes similar to that of data mining. Machine learning algorithms are described in terms of target function(f) that maps input variable (x) to an output variable (y). This can be represented as:
There is also an error e which is the independent of the input variable x. Thus the more generalized form of the equation is:
y=f(x) + e
In machine, the mapping from x to y is done for predictions. This method is known as predictive modeling to make the most accurate predictions. There are various assumptions for this function.
Benefits of Machine Learning
Everything is dependent on machine learning. Find out what are the benefits of machine learning.
- Decision making is faster – Machine learning provides the best possible outcomes by prioritizing the routine decision-making processes.
- Adaptability – Machine Learning provides the ability to adapt to new changing environment rapidly. The environment changes rapidly due to the fact that data is being constantly updated.
- Innovation – Machine learning uses advanced algorithms that improve the overall decision-making capacity. This helps in developing innovative business services and models.
- Insight – Machine learning helps in understanding unique data patterns and based on which specific actions can be taken.
- Business growth – With machine learning overall business process and workflow will be faster and hence this would contribute to the overall business growth and acceleration.
- The outcome will be good – With machine learning the quality of the outcome will be improved with lesser chances of error.