Machine Learning is a field of study focused on computer algorithms designed to perform tasks without being explicitly programmed to do so. Another interpretation is to consider machine learning algorithms as those able to improve in performance on a specific task, assessed by a specific measure, after experience with said task.<\/p>\n
Machine learning is a subset within the broader field of Artificial Intelligence and commonly used in fields such as data science, bioinformatics, natural language processing, and computer vision. Other common consumer-facing applications include product recommendation engines, price comparison tools, and image recognition software.<\/p>\n
The two most common types of machine learning are supervised<\/strong> and unsupervised<\/strong> machine learning. These types of ML algorithms address a wide range of applications ranging from predicting housing prices to risk factors of specific diseases. While broad in scope, they differ quite significantly in their foundational design.<\/p>\n Supervised machine learning algorithms use training<\/em>\u00a0data\u00a0<\/em>where the desired output is given. For example, a set of features on new cars paired with the final sale price\u2014the price being the given outcome. The machine learning model would be trained<\/em> on this data to predict the market price for data where the car features were given but the price was unknown.<\/p>\n Supervised machine learning models are designed to work with datasets that have no outcome variable and seek only to discover structure within the data. Common applications include clustering, which has many applications within fields such as medicine, finance, and statistical modeling. For example, genetics researchers may group certain individuals together based on their specific genetic expressions.<\/p>\nSupervised Learning<\/h3>\n
Unsupervised Learning<\/h3>\n
Other Types of Machine Learning<\/h3>\n