Supervised learning involves a series of functions that map an input to an output based on a series of example input-output pairs. For example, if we have a dataset of two variables, one being age (which is the input) and the other being shoe size (as the output), we could implement a supervised learning model to predict the shoe size of a person based on their age. Further, with supervised learning, there are two subcategories: one is regression, and the other is classification. In regression models, we find a target value based on independent predictors. That means you can use this to find the relationship between a dependent variable and an independent variable in regression models. The output is continuous. Some of the most common types of regression models include: 1. Linear regression, which is simply finding a line that fits the data. Its extensions include multiple linear regression, which is finding a plane of best fit, and polynomial regression, which is finding a curve for the best fit. Next, we have decision tree. It looks something like this, where each square above is called a node, and the more nodes you have, the more accurate your decision tree will be in general. Next, the third type is random forest. These are ensemble learning techniques that build off of decision trees and involve creating multiple decision trees using bootstrap datasets of the original data and randomly selecting a subset of variables at each step of the decision tree. The model then selects the mode of all the predictions from each decision tree, and by relying on the majority wins model, it reduces the risk of error from individual trees. Next, neural network. It is quite popular and is a multi-layered model inspired by the human mind, like the neurons in our brain. The circle represents a node: the blue circle represents an input layer, the black circle represents a hidden layer, and the green circle represents the output layer. Each node in the hidden layer represents a function that the input goes through, ultimately leading to the output in the green circles. Next, classification. So, with regression types over, now let's jump to classification. In classification, the output is discrete. Some of the most common types of classification models include: First, logistic regression, which is similar to linear regression but is used to model the probability of a finite number of outcomes, typically. Next, support vector machine. It is a supervised classification technique that aims to find a hyperplane in n-dimensional space that can distinctly classify the data points. Next, Naive Bayes. It's a classifier that acts as a probabilistic machine learning model used for classification tasks. The crux of the classifier is based on Bayes' theorem. Coming up next, decision trees, random forests, and neural networks. These models follow the same logic as previously explained. The only difference here is that the output is discrete rather than continuous. Now, next, let's jump over to unsupervised learning. Unlike supervised learning, unsupervised learning is used to draw inferences and find patterns from input data without references to the labeled outcome. Two main methods used in unsupervised learning include clustering and dimensionality reduction. Clustering involves grouping of data points. It's frequently used for customer segmentation, fraud detection, and document classification. Common clustering techniques include k-means clustering, hierarchical clustering, mean-shape clustering, and density-based clustering. While each technique has different methods in finding clusters, they all aim to achieve the same thing. Coming up next, dimensionality reduction. It is a process of reducing the dimensions of your feature set, or states, simply reducing the number of features. Most dimensionality reduction techniques can be categorized as either feature elimination or feature extraction. A popular method of dimensionality reduction is called principal component analysis, or PCA. Obviously, there's a ton of complexity.