What is Machine Learning?

Machine learning (ML), a subfield of artificial intelligence, is concerned with programming computers to make decisions automatically. ML aims to build mathematical models capable of learning from existing data to make predictions or to find patterns and trends in the data.

Examples of ML applications

ML is required when we want computers to recognize patterns in data. Learning might also be required when the solution to a problem changes over time. For instance, routing algorithms working in a computer network can require previous data to perform better routing.

Note: ML requires example data to learn patterns and to make decisions.

Some of the real-world examples of ML include:

• Disease diagnosis

• Fraud detection in finance

• Robotic process control in the industry

• Spam email detection

• Search engines in web mining

Types of ML

Three main types of ML and their types are given in the following figure.

• Supervised learning: This uses the training examples along with labels to build an ML model. Its major types are:

  • Regression: This deals with continuous labels.

  • Classification: This deals with discrete labels.

• Unsupervised learning: This uses the training examples without labels to build an ML model. Its major types are:

  • Clustering: This groups similar data based on input features.

  • Dimensionality reduction: This reduces the dimensions of the data to get only the most salient features.

  • Association: This finds associations or relationships between variables in large datasets.

• Reinforcement learning: We maximize some kind of reward over a series of actions (policy). Suppose we want to develop a computer game that plays chess. The system’s output is a sequence of actions. It’s the policy, instead of single actions, that is crucial to winning the game. Learning a policy is the main task in reinforcement learning. Its main types are given below.

  • Positive reinforcement learning: This learns a series of actions to increase the frequency and strength of a favorable stimulus.
    Negative reinforcement learning: This learns a series of actions to decrease the frequency and strength of an adverse stimulus.

Basic terminology in ML

ML model

An ML model is a mathematical description or a formula that describes a dataset. It can be a simple linear formula or a complex model whose number of parameters can vary according to the dataset.

Model parameters and hyperparameters

Model parameters determine how the input data transforms into the desired output. For a simple linear model, $f(x) = \theta_1x+\theta_0,$ ...