Machine learning utilizes algorithms to detect insights and patterns hidden in data without having the need to be explicitly programmed to do so. In machine learning, it is crucial to understand the iterative aspect as ML models can adapt independently when subjected to new data. They iteratively learn from previous data sets to output reliable and repeatable decisions as well as results.
Due to technological advancements and faster processing speeds, machine learning has evolved greatly since its inception. Now, machine learning algorithms are capable of applying complex mathematical calculations to big data sets, quickly and on a regular basis, which was not possible initially.
Some of the most popular examples of machine learning applications in the world today are:
- Machine learning algorithms play a significant role in how autonomous vehicles learn in real-time and taking appropriate driving actions.
- Machine learning applications are also present in suggestions and recommendations made by streaming services and large retailers.
- Machine learning can be used in fraud detection.
Along with ML, there have also been significant advances in the fields of data mining and analysis which have led to the refinement of ML processes. As a result, data has increased in both; variety and volume, computation has become more powerful and economical and data storage has become affordable as well. Machine learning models have also been incorporated into daily business processes like credit scoring, providing offers, web search results and suggestions, email spam filtering and many more.
Many of the most commonly used machine learning methods include, unsupervised learning, supervised learning, semi-supervised learning and reinforcement learning. However, it is important to note that supervised learning models account for around 70% of machine learning applications and unsupervised makeup around 10%-20%.
- Supervised learning algorithms are employed where the desired outcome is already known. The algorithm is given input sets and their corresponding output sets. The algorithm creates an analysis by comparing the actual outcome with the required outcome.
- Unsupervised learning is employed when data has no historical labels. The algorithm works on its own to discover patterns and insights within the data that were previously hidden.
- Semi-supervised learning is a combination of both supervised and unsupervised learning. Its algorithms learn from datasets that include both labeled and unlabeled data, with the majority of the data being unlabeled.
- Reinforcement learning is the training of machine learning algorithms to create a sequence of decisions. The algorithm learns to achieve a particular goal in an uncertain and potentially complex environment. Its goal is to maximize the total desired outcome. It is commonly used in gaming, navigation and robotics.