Machine learning

Machine learning refers to the application of algorithms to make computers learn from data. The models that are learned by computers are used to make predictions and forecasts. Machine learning has been successfully applied in a variety of areas, such as natural language processing, self-driving vehicles, image and speech recognition, chatbots, and computer vision.

Machine learning algorithms are broadly categorized into three types:

• Supervised learning: In supervised learning, the machine learns the model from a training dataset that consists of features and labels. The supervised learning problems are generally of two types: regression and classification. Regression refers to predicting future values based on the model, while classification refers to predicting the categories of the input values.
• Unsupervised learning: In unsupervised learning, the machine learns the model from a training dataset that consists of features only. One of the most common types of unsupervised learning is known as clustering. Clustering refers to dividing the input data into multiple groups, thus producing clusters or segments.
• Reinforcement learning: In reinforcement learning, the agent starts with an initial model and then continuously learns the model based on the feedback from the environment. A reinforcement learning agent learns or updates the model by applying supervised or unsupervised learning techniques as part of the reinforcement learning algorithms.

These machine learning problems are abstracted to the following equation in one form or another:

Here, y represents the target and x represents the feature. If x is a collection of features, it is also called a feature vector and denoted with X. The model is the function f that maps features to targets. Once the computer learns f, it can use the new values of x to predict the values of y.

The preceding simple equation can be rewritten in the context of linear models for machine learning as follows:

Here, w is known as the weight and b is known as the bias. Thus, the machine learning problem now can be stated as a problem of finding w and b from the current values of X so that the equation can now be used to predict the values of y. 

Regression analysis or regression modeling refers to the methods and techniques used to estimate relationships among variables. The variables that are used as input for regression models are called independent variables, predictors, or features, and the output variables from regression models are called dependent variables or targets. Regression models are defined as follows:

Where Y is the target variable, X is a vector of features, and β is a vector of parameters (w,b in the preceding equation).