Different Methods of Machine Learning
Machine Learning is one of the most significant sub-categories of Artificial Intelligence which is gaining popularity day by day. It is considered to be that field of AI that enables machines to learn and automatically improve through experience.
Machine learning is different from traditional programming because it allows computers to learn without explicit programming. Machine learning is widely used in different sectors such as Finance, Healthcare, Infrastructure, Marketing, Social media platforms, cyber security, gaming, etc.
Machine learning is still going through its development phase so many new technologies are being added to this field. Although continuously evolving, Machine Learning is still used by many top-notch companies as an integral part of their business operations.
Machine Learning is important because it gives companies a fair picture of consumer behaviors and operational business patterns. It also supports enterprises in the development of new products and services.
How did it work?
Machine learning is a form of Artificial Intelligence that gathers past data to make future predictions with minimal human intervention. Let us explore the four technologies where Machine Learning is significantly used-
- Supervised Learning: Supervised learning is an ML method that needs prior supervision to calculate results. In this type of learning, some well-labeled data is transferred into machines with correct outputs. So whenever, a system comes across any new data, the supervised learning algorithms come into play by analyzing data and giving correct predictions.
Supervised learning can be classified into two algorithms namely, Classification and Regression.
This type of ML technology allows you to collect and provide output data based on experience. It also helps in optimizing performance by using experience and solving complex computation problems.
- Unsupervised Learning: Contrary to supervised learning, unsupervised learning doesn’t need to feed the machines with any well-labeled data. Unsupervised learning focus on extracting unsorted information based on certain patterns and differences without any prior data training. In unsupervised learning, the machines receive no supervision and have to find out hidden structures in unlabeled data on their own.
Unsupervised learning works on two different algorithms namely, Clustering and Association.
- Semi-supervised Learning: Semi-supervised learning is the integration of both supervised and unsupervised machine learning methods. This type of learning is used to eradicate the drawbacks of supervised and unsupervised learning. It implies that a machine is trained with labeled and unlabeled data at the same time. Although, the unlabeled data present in the machine is greater in number than labeled data.
Semi-supervised learning has real-world applications such as web content classification, speech analysis, text document classifiers, etc.
- Reinforcement Learning: Reinforcement learning is referred to as a feedback-based method of machine learning without labeled data. There is an agent that learns to behave in a specific environment based on actions performed and their result.
There is no training data in RL so the agents are compelled to learn from the experiences of their own.
Although evolving, Machine Learning has the potential to practically drive real business results such as saving your time and money.
Machine learning can dramatically improve the future of new businesses just as it has positively affected well-established companies. Have a Machine Learning project in mind? Reach out to us at www.internetshine.com
