Machine learning is a branch of artificial intelligence (AI) that utilizes data and algorithms to enable systems to analyze at scale and improve automatically. It is a method of analysis that builds a model based on sample training data in order to predict new output values or decisions with minimal human intervention. Simply put, machine learning trains computers how to learn.
The machine learning discipline has been around since the 1960s, with the term first coined by Arthur Samuel, an American pioneer in the field of computer gaming and AI. Because of the technological advancements enabling faster processing of big data in recent years, machine learning is now being applied in numerous use cases across industries. Examples include recommendations engines, fraud detection, self-driving cars, speech recognition, and algorithmic trading.
How Does ML Work?
The use of historical data and experience to make new classifications or predictions that improve accuracy over time is the main idea behind machine learning. Imitating the way humans learn, a machine learning algorithm is composed of three main parts:
- A Decision Process involves an estimate of a pattern in the input data.
- An Error Function evaluates the prediction and accuracy of the model.
- A Model Optimization Process assigns weights to the data points in the training set that adjust repeatedly to reduce the discrepancy between the known example and the model estimate until the desired accuracy is reached.
Machine Learning Approaches
There are four key approaches in machine learning based on the nature of the “signal” or “feedback” available to the learning system, or how an algorithm learns in order to increase the accuracy of its predictions.
- Supervised learning uses labeled datasets to train algorithms to map input data to desired outputs. Applications of this approach include ranking, recommendation systems, and face verification.
- Unsupervised learning provides no labels to the learning algorithm. The goal is to find a structure on its own by discovering either hidden patterns in data or a means towards an end. This technique is used in cross-selling strategies, image recognition, and customer segmentation.
- Semi-supervised learning offers an acceptable medium between supervised and unsupervised learning, usually used to solve situations where there is not enough labeled data to train a supervised algorithm.
- Reinforcement learning acts in a similar way to supervised learning, but isn’t trained using sample data. The algorithm has a certain goal as it navigates a dynamic environment. It gathers feedback or “rewards,” which it tries to maximize through trial and error, producing a sequence of successful outcomes. Example uses of this approach are time series models for predicting stock prices, natural language processing, and robotics.
Machine Learning in Retail
The growing volumes of data, availability of more powerful and cheaper computational processing systems, and accessible data storage are boosting the interest in ML. Retail is one of the industries that stand to gain a lot from its rapid and continuous development. Here are some ways brands and retailers can use machine learning:
- Improve merchandising and pricing strategy in real-time based on the present context
- Optimize marketing campaigns and customer segmentation
- Predict product demands and stock replenishment by analyzing search patterns and consumer behavior
- Offer customers personalized product recommendations
- Deliver online shopping experiences that are customized to customers’ browsing behavior, previous purchases, and real-time intent
- Enhance the buying and search experience with image recognition capabilities
Check out how White Rose improved the online shopping experience with technologies powered by machine learning.