Types of Machine Learning Algorithms
Machine learning is used to predict, categorize, classify, find polarity, etc. from the given datasets and is concerned with minimizing the error. Machine learning is always algorithm based and basic machine algorithms can be classified as –
1. Supervised Learning
2. Unsupervised Learning
3. Reinforcement Learning
It is used for structured dataset. It analyzes the training data and generates a function that will be used for other datasets. It is machine learning for making predictions – 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.
It is used for raw datasets. Its 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.
Another modeling paradigm known as unsupervised learning tries to surface underlying patterns and associations in data when no existing ground truth is known. Within this broad category of methods, the most commonly used are clustering techniques which algorithmically detect what are the natural groupings that exist in a data set.
Reinforcement Learning spurs off from the concept of unsupervised learning and gives a high sphere of control to software agents and machines to determine what the ideal behavior within a context can be. This link is formed to maximize the performance of the machine in a way that helps it to grow. 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.