What is machine learning?
A field of study in artificial intelligence (AI), machine learning involves the development of algorithms that allow computers to find patterns in data and forecast or make judgments about their findings using computational models.
What types of algorithms does machine learning use?
Machine learning may use a variety of different algorithms to receive and analyze input data, identifying patterns, correlations, and anomalies that humans may miss. As they do so, they learn and optimize their operations, improving performance and developing their artificial intelligence over time. In this way, the outputs can forecast trends and make data-driven predictions as well as identify potential risks or opportunities.
The main types of machine learning algorithms are:
- Supervised learning – where the machine is taught by example. It is given data that is already understood, alongside the desired inputs and outputs. It is then up to the machine to work out how to achieve those results. The algorithm can make mistakes as it learns and will be corrected by the operator.
- Unsupervised learning – where the machine is given data and left to its own devices. With no instructions, it analyzes the inputs and discovers patterns and correlations, grouping and organizing the data according to its findings. Over time, decisions improve as it refines its processes.
- Semi-supervised learning – a combination of supervised and unsupervised learning. The machine is given labeled and unlabeled data. Using the labels it has been given, the machine learns how to label and assign the unlabelled data, refining its methods over time.
- Reinforcement learning – the machine is given a rigid set of instructions and information (actions, parameters, outputs). By using trial and error, the machine works with the data according to its instructions, monitoring and evaluating its results to discover the most optimal approach.
Knowing which machine learning algorithm is the best choice for a specific data set is nearly impossible. It all depends on a variety of factors including the size of the dataset, its quality and diversity, the accuracy required, the desired answers, and training time. Without experimenting with each method, there is no way to know.
As such, it often becomes a case of picking the option that best fulfills the remit within time and budget constraints.
Where is machine learning used?
Machine learning is used in numerous industries to enhance the understanding of data and advance fields of study through calculated predictions.
Areas where machine learning is used include:
- Medical diagnosis and healthcare – by analyzing biological data (known as bioinformatics), machines can predict the likelihood of illnesses and diseases occurring, their likely prognosis, and the best means of medicating patients based on their profile.
- Financial services – machine learning is used to facilitate stock trading, understand risk metrics, and highlight instances or likelihood of financial crime (money laundering, fraud, etc.)
- Marketing – text generation, image creation, and image recognition all use machine learning to launch their product and improve their offering to users over time. Marketers use these products to improve their productivity and increase efficiency.
- Web searches and advertising – search result relevance is improved through machine learning, as is internet and email advertising, learning the basic profiles of users to offer them the adverts that they are most likely to be interested in, and iterating based on subsequent behavior.
- Retail and customer support – retailers are increasingly using machine learning to tailor their products to customers and enhance their stock control warehousing, freight, and delivery processes. Alongside this, their customer support (via email and chatbots) provides answers based on algorithms and pre-existing data points.
- Industrial – machine learning can improve production processes, predict equipment failures, and enhance overall efficiency through data analysis and pattern recognition. It enables predictive maintenance strategies and real-time monitoring.
