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Types of machine learning algorithms.

Updated: Nov 3, 2022

Machine learning contains a set of algorithms that work on a huge amount of data. Data is fed to these algorithms to train them, and on the basis of training, the model is built and specific tasks are performed. There are different types of machine learning algorithms which help in understanding the behaviour of the business.

Those ML algorithms help to solve different business problems in given situations.

Types of Machine learning algorithms are divided into four parts.

  1. Supervised Machine Learning.

  2. Unsupervised Machine Learning.

  3. Semi-Supervised Machine Learning.

  4. Reinforcement Learning.

Let's understand those types in detail.

1. Supervised Machine Learning.

In this algorithm, the machine is trained using the "labelled" dataset and based on the training, the machine predicts the output. The dataset which contains a target variable and independent variables is called a labelled dataset. This means we actually know what we have to return or predict. Once the machine is trained then we pass a new unlabelled dataset through our model and it predicts the target variable.

For example, House price prediction.

Categories of supervised machine learning algorithms.

It is of two types.

  1. Regression: It is used to solve problems in which there is a linear relationship between input and output variables. These are used to predict continuous output variables, such as market trends, weather predictions, etc.

  2. Classification: It is used to solve problems in which the output variable is categorical, like "Yes" or "No", "True" or "False", and "1" or "0". For example, Spam detection, Diseases classification, Cancer prediction, etc.

2. Unsupervised Machine Learning.

In unsupervised machine learning, the machine is trained using an unlabeled dataset, and the machine predicts the output without any supervision. Hence it is a complement to supervised machine learning. Here the data is neither classified nor labelled. It groups the unsorted dataset according to the similarities, patterns and differences.

Categories of unsupervised machine learning algorithms.
  1. Clustering: It is a way to group the objects into a cluster such that the objects with the most similarities remain in one group and have very less or no similarities with other groups.

  • K-Meas clustering

  • Principal Component Analysis

  • Mean-shift algorithm

2. Association: It helps to find relations among variables within a large dataset. The main aim is to find the dependency of one data item on another data item and map those variables accordingly so that it can generate maximum profits.

3. Semi-Supervised Machine Learning.

It is a kind of algorithm which lies between supervised and unsupervised machine learning. It uses a combination of labelled and unlabeled datasets during the training period.

To overcome the drawbacks and limitations of supervised and unsupervised learning algorithms, the concept of semi-supervised is introduced. Hence it effectively uses all the variables rather than only target variables just like a supervised machine learning algorithm.

4. Reinforcement Learning.

The goal of this technique is to maximize the rewards, it works on a feedback-based process, in which the agent explore its surrounding by hitting and trail, learn from its experience, takes action and improve its performance. Agent gets rewards for each good action and gets punished for each bad action.

Here no labelled data is present and the agent learns from its experience only.

Categories of reinforcement learning algorithms.
  • Positive Reinforcement Learning: It specifies increasing the tendency that the required behaviour would occur again by adding something.

  • Negative Reinforcement Learning: It increases the tendency that the specific behaviour would occur again by avoiding the negative condition.

For example, Video Games, Robotics and Text Mining

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