Different types of Machine Learning

Machine Learning Types

Machine learning has proven to be instrumental in changing the landscape of technological advancements over the past decade. The needs and requirements of the ever-changing and growing corporate world have also complemented machine learning and the emerging consumerism has also made machine learning as well as artificial intelligence more relevant than ever. It also goes without saying that artificial intelligence and machine learning have also eased a trend of digital transactions and help in fraud detections in banking or product recommendations. Machine learning has its own sub-sects. The three main types of machine learning are Supervised Learning, Unsupervised Learning, Reinforcement Learning.

Supervised Learning

Being the most basic type of machine learning, the machine learning algorithm is trained on labelled data. This can mean that the entire process might be extensive and exhaustive, it could also wield out quick results and prove to be fruitful when used in the right setting and circumstances. Dataset given to the machine learning algorithm is a small training dataset to work with but this small training dataset is a part of the larger whole or a bigger dataset which helps in providing the algorithm with a meaningful solution, a basic idea and data points to be dealt with. The training dataset is characteristically and compositionally similar to the larger training dataset and gives it the labelled parameters that are required. The whole process is a gradual development of dataset and labelled parameters establishing a relationship among them so that this relationship between input and output could give a basic idea and solution to the algorithm. This is further deployed to the larger or final dataset for use.

Unsupervised Learning

While supervised learning works with labelled and trained data, unsupervised learning works with unlabeled data which means that human input and labour is not needed to make the data understandable for machines, which further makes it possible for larger datasets to be worked upon. Hidden structures are produced because there are no labels used for datasets work of off. Relationships and equations between data points are formed theoretically without human input and interference. The versatility of unsupervised learning can be accounted for the creation of these hidden structures.

Reinforcement Learning

Reinforcement learning is directly inspired by humans and their ability to interpret data. Gradual self-improvement and developing a memory through experiences by a trial-and-error method are the algorithm's characteristic features. Outputs that are favourable are reinforced whereas non-favourable outputs are ‘condemned’. Psychological structuring and conditioning which developed through an interpreter and reward system are central to reinforcement learning. The interpreter decides whether the outcome is favourable or non-favourable. If the outcome is correct, the algorithm is rewarded and if not, it is reiterated to the point when it gives the correct solution. The solution given by the algorithm in reinforcement learning is not represented by an absolute value but is rather given in percentage value. Higher percentage value means more reward for the algorithm.