Machine learning algorithms use computational methods to “learn” information directly from data without assuming a predetermined equation as a model. They can adaptively improve their performance as you increase the number of samples available for learning. Machine learning algorithms that develop decision-making rules by learning from labeled training data are known as “supervised learning” algorithms. “Unsupervised learning” algorithms can uncover useful patterns and structures from unlabeled data.
Machine Learning algorithms are used in applications such as computational finance (credit scoring and algorithmic trading), computational biology (tumor detection, drug discovery, and DNA sequencing), energy production (price and load forecasting), natural language processing, speech and image recognition, and advertising and recommendation systems.
Machine learning is often used in big data applications which have large datasets with many predictors (features) and are too complex for a simple parametric model. Examples of big data applications include forecasting electricity load with a neural network or bond rating classification for credit risk using an ensemble of decision trees.
Supervised learning techniques to build predictive models from known input and response data:
Unsupervised learning techniques to find hidden patterns or intrinsic structures within the data: