What Is Machine Learning?
Machine Learning has become increasingly popular, particularly in relation to artificial intelligence and so-called Big Data.
Machine Learning is a subset of Artificial Intelligence, using algorithms to learn about data. Machine Learning algorithms are able to enhance themselves. The main focus of Machine Learning is the creation of computer programs which are able to teach themselves when faced with new data.
What Type Of Data Does Machine Learning Deal With?
Machine Learning refers to a broad range of algorithms. Algorithms such as Gradient Boosting and Random Forest are used on large data sets, depending on use case. Typically a data scientist would determine the best type of algorithm to use.
Smaller data sets, on the other hand, might use statistical techniques such as Statistical Modelling.
What Are The Applications for Machine Learning?
Machine Learning is already in production across a number of different verticals. Some applications of Machine Learning that you should know about are:
- Finance and Banking
- Machine Learning is being used to determine, in real time, customers who are likely to default on payments, or those who have become a victim of fraud. Machine Learning will be able to give banks and financial services institutions a better idea of who should be given mortgages, loans and credit cards, potentially eliminating human biases.
- Machine Learning is being used in Healthcare to assess likelihood that a patient may have (or if they may in the future be diagnosed with) a disease, based on past data. Machine Learning may also have important implications for preventative healthcare.
- Machine Learning is used in retail to build accurate maps of customer identity, identify popular products, and identify which combinations of products tend to be bought together. This can help retailers develop and maintain customer loyalty, and provide a first-class customer experience.
As businesses generate more and more data, and data scientists create models which are capable of processing this data with ever more accuracy, Machine Learning will provide strategic advantages in any number of industries.
Where Is Machine Learning Coming From?
A good question! There are a number of different disciplines that are coming together to work on Machine Learning, each with their own traditions and discourse.
- Computational Neuroscience – the study of brain function in terms of the information processing properties of the structures that make up the nervous system. Artificial Neural Networks are inspired by the biological neural networks that constitute animal brains. Computational neuroscience has proved particularly useful in applications that do not respond well to traditional rule-based algorithms.
- Statistics – Both statistics and machine learning work with data to solve problems, but approach it in different ways. Machine Learning emphasizes prediction, where statistics focuses on estimation and inference. Statistics and machine learning can be considered two sides of the same coin: computer scientists design algorithms that will be used as part of software packages, and statisticians provide the mathematical foundation for this research to take place.
- Adaptive Control theory – Adaptive control attempts to react to a control system with parameters that vary. For example, an aircraft decreases in mass as a result of fuel consumption, so a control law is needed that is able to adapt to those changing conditions. In robotics, a robot has sensors which need to react to changing environments.
- Psychology – Machine Learning is used routinely in data analysis in psychology and neuroscience. Machine Learning has, for example, been used on data -including IQ, personality traits, and blood makers to create predictive models of teenage binge drinking.
- Artificial Intelligence – AI and Machine Learning have always been strongly linked. Arthur Samuel’s was the world’s first self-learning program, and as such a very early demonstration of the fundamental concept of AI. Samuel believed that teaching computers to play games was a good technique for teaching computers to solve general problems. More recently, John Ross Quinlan’s research on decision tree algorithms has helped data scientists generate decision trees from datasets.
The Future of Machine Learning
Data scientists are working to create a future where automated techniques can create patterns that a human observer may have missed. Data scientists want to write algorithms for completing tasks, and to do so in the most efficient (and therefore inexpensive) way possible.
Categories: Data Science