What is Machine Learning?
Machine Learning (ML) is a specific, important subset of Artificial Intelligence (AI) that trains a machine on how to learn. It is a branch from AI based on the idea that systems can learn through data, identifying patterns and making decisions with minimal human intervention.
Machine Learning is a method of data analysis that automates analytical model building. Birthed from patterns of recognition, the model looks for patterns in a given data before drawing conclusions from it without being explicitly programmed by people. It can learn what needs to be done based off receiving given examples. Automatically applying complex mathematical calculations to big data continuously with increased speed was only possible in recent years through countless evolution in Machine Learning. It is known in most industries with large amounts of data to understand the importance of machine learning due to the efficiency and advantages an organisation can gain over competitors through insights received from the given data.
Application of Machine Learning from Day-to-Day Life
Many things we interact with throughout our day-to-day lives have Machine Learning integrated into them. Some examples include self-driving cars which showcases the essence of ML, acquiring recommendation offers from online stores and even gaining customer insights from data at your workplace. Finance sectors may use the model to identify important data for investment opportunities and prevent fraud. Retail sectors use it as means to extract insights, analyse it and use them to create more personalised shopping experiences for consumers, with inclusion of pricing optimisations, marketing campaigns and additional customer insights.
Can Machine Learning be Self Taught?
Machine Learning models can most definitely be self-taught to run through pools of data, with some requiring more supervision than others. There are 4 main types of learning Machine Learning model:
1. Supervised Learning
A subcategory of ML and AI that defines its use of labeled datasets to train algorithms and classify data or predict outcomes accurately. The learning is mostly supervised by a person, allowing them to correct an algorithm if the model makes a mistake. There are 2 types of Supervised Learning: Regression and Classification. An example would be the use of classification in mobile phones on BioInformatics. We use them daily and it stores the biological information of humans such as fingerprints, face recognition and more to use it as an authentication measure of security for the device or a system.
2. Unsupervised Learning
Uses ML algorithms to train and infer patterns from a dataset without any known reference or labeled outcome. This model cannot be directly applied to classification or regression problems like Supervised Learning due to the lack of knowledge on what value outputs might surface. An example would be clustering. The model will analyse and group up multiple kinds of data that has no pre-labeled classes or attributes. This can help businesses to manage their datasets better overall.
3. Semi-supervised Learning
A combination of supervised and unsupervised learning. This method is a middle ground between the performance of supervised learning and the efficiencies of unsupervised learning. It takes guidance from a small portion of labeled datasets before it explores a large amount of unlabeled data. By doing so, the model will be free to explore and develop its own understanding of the datasets with prior knowledge learnt from the small pool of labeled data. An example would be a machine translation. It teaches the algorithms to translate a language based off less than a dictionary full of words.
4. Reinforcement Learning
A feedback-based machine learning technique that has algorithms learn through trial and error to yield the best course of action in a multi-step process. It works by having an algorithm prescribe a set of rules after accomplishing a distinct goal. A simpler way to understand these learning components are to break them up into 3 parts: the agent, the environment and the actions. The agent learns how to behave in an environment by performing actions and reviewing the results of actions.
Each good action allows the agent to receive positive feedback while each bad action gives the agent a negative one. Unlike supervised learning, the agent can learn automatically without labeled data through feedbacks. Throughout the trial-and-error process and based on experiences, it’ll learn to perform tasks in a better manner. For example, it can be used in computer games like chess. It teaches bots to learn and adjust feature weights based off the results received or subsequent predictions during self-play.