Machine Learning for Humans

Part 1. Introduction. The big picture of artificial intelligence and machine learning—past, present, and future. Part 2. Supervised Machine Learning. In supervised machine learning, data are fed into a mathematical model, which is then 'trained' on the basis of that data; once trained, the model can then be used to predict outcomes based on new (unseen) data. Part 2. 1: Linear Regression and Classification with Logistic Regression/Support Vector Machine (SVM). In this section, we will cover two widely used approaches to supervised machine learning: linear regression and support vector machines (SVMs). Part 2. 2: Non-Parametric Methods for Supervised Machine Learning. This section will focus on non-parametric approaches to supervised machine learning, including k-nearest neighbors (k-NNs), decision trees, and random forests. Part 3: Unsupervised Machine Learning . Unsupervised methods for machine learning are able to make predictions without being given any existing data about the properties of the underlying data it's working with; instead, unsupervised methods learn from their own data, thus making it less reliant on the labels given by human beings