Machine Learning is an algorithm that can make predictions about future events based on past behavior. It is as prone to bias as humans and can be used to predict the future outcomes of campaigns and products. One example of machine-learning algorithm is used to predict political votes by studying millions of users’ social media accounts. This algorithm allows politicians to cater their services and campaigns to a particular group. But how does this technology compare to the power of human discretion?
The process of machine learning is divided into two main categories: supervised and unsupervised. Supervised learning uses labels to categorize data and assign predictors to it. Unsupervised learning algorithms use unlabeled data and explore it to identify hidden patterns. These algorithms are perfect for image recognition, cross-selling strategies, and customer segmentation. They are also useful for reducing dimensionality. Some popular unsupervised learning algorithms include principal component analysis and singular value decomposition.
A machine-learning model makes predictions based on input data and adjusts its functions based on changes in data. For example, a simple machine-learning model can identify the ABV or color of a drink. Another model, called a linear regression with gradient descent, can estimate the number of ice creams sold at a store based on the temperature outside. The model is then trained by comparing past sales against past data on the temperature.