The steps in the machine learning workflow

From medical diagnosis, speech, and handwriting recognition to automated trading and movie recommendations, machine learning techniques are being used to make critical business and life decisions every moment of the day. Each problem is unique, so it can be challenging to manage raw data, identify the right data to include in the model, train multiple types of models, and perform model assessments. Machine learning uses algorithms that learn from data to help make better decisions; however,it is not always obvious what the best machine learning algorithm is going to be for a particular problem. Luckily, information such as variable importance and model assessment tools can help us decide which machine learning techniques to apply. Examples of machine learning techniques include clustering, where objects are grouped into bins with similar traits; regression, where relationships among variables are estimated; and classification, where a trained model is used to predict a categorical response.