Machine Learning, a subfield of artificial intelligence, learn from data to train and improve its accuracy in doing tasks like classification and prediction. There are different types of machine learning algorithms that can be divided into supervised and unsupervised learning. This division is often important in determining the right algorithm to be used for a particular business problem.
Supervised learning is when labelled datasets are used to train machine learning models with the help of human intervention. In other words, there is an input variable that is fed to a model which directly affects the output variable. Over time, the model learns the desired outcome and continues to make iterations until the algorithm achieves a suitable performance level. Some examples of supervised learning include linear regression and classification.
Unsupervised learning, on the other hand, utilizes unlabelled data to explore patterns without human intervention. And unlike supervised learning, there is no output variable. Thus, an example of unsupervised learning is clustering:
Clustering: Clustering is the process of discovering a hidden structure within an unlabelled dataset. An example of clustering in a business context is grouping customers according to their purchasing behaviours.
Machine Learning Applications in Real Life
In the agricultural sector, linear regression is very useful for predicting crop yield. By testing the effects of different amounts of water and fertilizer on a variety of fields, scientists can estimate the expected crop yield. This is useful for overcoming the unpredictability that farmers face otherwise and increasing crop productivity.
The food and restaurant review sites benefit from their users uploading images onto them. This inspired development of image recognition systems that creates data from pictures. By collecting information from photo captions and attributes, they are able to sort images into classes relevant to restaurants i.e., whether an image is one of food, outside, or a menu.
Recommender systems are used by e-commerce retailers like Amazon to encourage cross-selling. Clustering methods are necessary for increasing the accuracy of these recommendation systems. Amazon’s recommendation algorithm works by user-based collaborative filtering which matches a visitor to their website to others with similar purchasing histories and recommends products based on that.
The type of machine learning algorithm that can be applied to a business problem will depend on the type of data available. For example, supervised learning can be applied if the data is labelled with dependent and independent variables. Alternatively, unsupervised learning can be applied if the data is unlabelled and will require identifying inherent patterns.