There are various Types of Machine Learning Algorithms that exist, such as Supervised, Unsupervised, and Reinforcement.
Here we are beginning this article with the Definition of Machine learning and its classifications.
What Is Machine Learning (ML)?
According to Wikipedia ” (ML), Machine learning is the study of algorithms and mathematical models that computer systems use to progressively improve their performance on a specific task”.
In a formal way, it may be defined as “the branch of AI which helps machines in learning from the Given Raw Data (Organised Data or Unorganised Data) Using various Machines Learning Algorithms and Producing the desired result”.
For Example:- If you browse any website say YouTube then firstly it shows you the basic videos available for your Country/ Area Specific. Then as you start watching videos, say comedy the next time you will see that videos available will be available for you will be of Comedy Categories.
Types Of Machine Learning Algorithms
Different Types of Algorithms used for analyzing the data and predicting the outcomes are discussed below.
Supervised Machine Learning Algorithms
According to the Wikipedia “Supervised learning is the machine learning task of learning a function that maps an input to an output based on pairs of input-output. It makes use of a function from labeled training data consisting of a set of training examples. In supervised learning, each example is a pair consisting of an input value and the desired output value, also called the supervisory signal”.
For Example –
1. Supervised Learning has the agent which got the information about the particular disease and based upon this data agent determines whether the given patient will suffer from cancer on not.
2. We Provide the model a large collection of photos and also pass the information that whether they are pics of hot-dog or not. then after the training model becomes able to recognize whether the given photo is of Hot Dog or not.
The most widely used Algorithms in Supervised Machine Learning are –
- linear regression
- logistic regression
- Support Vector Machines
- decision trees
- k-nearest neighbor algorithm
- Neural Networks (Multilayer perception)
- naive Bayes
- linear discriminant analysis
- Similarity learning
Unsupervised Machine Learning Algorithms
“Unsupervised learning may be defined as a set of algorithms that learns from test data (most commonly known as dirty data) that has not been labeled, classified, or categorized. Instead of giving feedback, unsupervised learning indicates common properties in the data and takes decision-based on the presence or absence of such commonalities in each new piece of data “.
Alternatively, It is the Learning technique that simply classifies the data based upon their similarities into the clusters and based upon these newly made clusters responds to the next data.
For Example –
1. From a given dataset of 5 people’s photos when a model makes his own assumptions to classify the data into clusters of 5 People using Unsupervised Machine Learning.
2. You gave the data regarding the particular disease and the model will itself make assumptions to find on which values the given disease may happen or not.
Unsupervised learning uses the following Algorithms/ Techniques-
Latent Variable Models
- Expectation–maximization algorithm (EM)
- Method of moments
- Blind signal separation
- Principal component analysis
- Independent component analysis
- Non-negative matrix factorization
- Singular value decomposition
Anomaly detection
- Local Outlier Factor
Clustering
- hierarchical clustering,
- k-means
- mixture models
- DBSCAN
- OPTICS algorithm
Neural Network
- Autoencoders
- Deep Belief Nets
- Hebbian Learning
- Generative Adversarial Networks
- Self-organizing map
Reinforcement Machine Learning Algorithms
According To Wikipedia ” Reinforcement learning (RL) is an area of machine learning concerned with how software agents ought to take actions in an environment so as to maximize some notion of cumulative reward “.
For Example:
In a given Situation an agent in Chess decides whether the new move will help him to reach the Goal State or not. Agent Accomplish this task will the help of previously used steps or Reinforcement.
Various Reinforcement Learning Algorithms Are –
- SARSA
- SARSA – Lambda
- Q-learning
- Q-learning – Lambda
- DQN
- DDPG
- PPO
- TRPO
- NAF
- A3C
Final Words – This Article gave you a simple Introduction to the different types of Machine Learning Algorithms. If you have any doubt regarding the content of this Article you can let us know in the comment box below.
Final Words – This Article gave you a simple Introduction to the different types of Machine Learning Algorithms. If you have any doubt regarding the content of this Article you can let us know in the comment box below.