Types of Machine Learning Algorithms

Machine Learning

Machine learning is used to predict, categorize, classify, find polarity, etc. from the given datasets and is concerned with minimizing the error. It can be classified into four categories:

• 1. Supervised Learning

2. Unsupervised Learning

3. Semi-supervised Learning

4. Reinforcement Learning

Supervised Learning

Supervised Learning is a machine learning algorithm used for structured dataset and making predictions. It analyzes the training data and generates a function that will be used for other datasets. The core concept is to use tagged data to train predictive models. Tagged data means observation where ground truth is already known. Training model means automatically characterizing tagged data in ways to predict tags for unknown data points.

Unsupervised Learning

It is used for raw datasets. Unsupervised Learning main task is to convert raw data to structured data. In today’s world, there is a huge amount of raw data in every field. Even the computer generates log files which are in the form of raw data. Therefore it’s the most important part of machine learning. It consists of machine learning for pattern discovery. Unsupervised Learning is most commonly used are clustering techniques which algorithmically detect what are the natural groupings that exist in a data set.

Semi-supervised Learning

Semi-supervised learning comprises characteristics of both supervised and unsupervised machine learning. It uses the combination of labeled and unlabeled datasets to train its algorithms.

Reinforcement Learning

Reinforcement learning is a feedback-based process. Simple feedback that informs the machine about its progress is required here to help the machine learn its behavior. Reinforcement Learning isn’t simple and is tackled by a plethora of different algorithms. As a matter of fact, in Reinforcement Learning, an agent decides the best action based on the current state of the results. The growth in this learning has led to the production of a wide variety of algorithms that help machines learn the outcome of what they are doing.