Machine Learning

Machine learning (ML) is a field of computer science and artificial intelligence that involves using data and algorithms to enable AI to learn from experience and improve its accuracy over time.

Machine Learning methods

Supervised machine learning

Supervised learning uses labelled datasets to train algorithms to accurately classify data or predict outcomes. The model adjusts its weights as input data is fed into it to avoid overfitting or underfitting. Some popular methods used in supervised learning include neural networks, naïve bayes, linear regression, logistic regression, random forest, and support vector machine (SVM).

Unsupervised machine learning

Unsupervised learning uses machine learning algorithms to analyse and cluster unlabelled datasets to discover hidden patterns or data groupings without human intervention. This method is ideal for exploratory data analysis, cross-selling strategies, customer segmentation, and image and pattern recognition. It is also used to reduce the number of features in a model through the process of dimensionality reduction. Common approaches for this include principal component analysis (PCA) and singular value decomposition (SVD). Some algorithms used in unsupervised learning include neural networks, k-means clustering, and probabilistic clustering methods.

Semi-supervised learning

Semi-supervised learning uses a more minor labelled data set to guide classification and feature extraction from a more extensive, unlabelled data set. It is a good solution when there is not enough labelled data for a supervised learning algorithm or if labelling enough data is too costly.