AI is a term that most people have heard of in this age where technology advances so rapidly. Most of us have read articles about robots taking over or automation destroying jobs. While based more on science fiction and less on an attainable future of AI, these articles keep AI skills at the forefront of many people’s minds.
You can find dozens of AI examples you use daily, including facial recognition, autocorrect, search engines or social media algorithms. Have you ever wondered what these programs do?
AI is based on algorithms, but they are not all the same. The algorithms are developed using different methods and goals. This article will discuss the four main categories of AI algorithms.
What is an AI algorithm?
A mathematical and computer science definition of an algorithm is a “set of instructions that must be followed to perform calculations or other operations”. At its core, an AI algorithm is a set of instructions instructing the computer to learn how to function independently.
AI algorithms are much more complicated than the algebra most people have studied. Artificial intelligence professionals are driven by complex rules that determine their learning ability and how they move. AI would not exist without an algorithm.
Categories of Artificial Intelligence Algorithms
Artificial Intelligence is a field that includes Machine Learning algorithms and Deep Learning Algorithms. The main purpose of these algorithms is to allow computers to learn and find patterns or make decisions independently. Artificial Intelligence algorithms learn from the data. Learning can be classified into three categories.
· Supervised Learning: The model is trained by observing and learning from it to predict the output of similar input data.
· Unsupervised Learning: This type of learning occurs when the output data is unknown, or it is necessary to find patterns within data.
· Reinforcement Learning: Algorithms are taught to perform a task by experience. Algorithms learn by trial and error which actions yield the best rewards. Selecting actions that will maximize expected rewards over a certain period is important.
There are three ways to divide algorithms into categories based on the problems humans face and solve.
1. Classification Algorithms
A classification algorithm is a supervised learning technique which identifies a new observation’s category using the training data. This can be used to categories data in a certain way. It is divided in sub – categories like:
· Naive Bayes
Naive Bayes is based on Bayes’ theorem and uses a probabilistic approach. This algorithm differs from other category algorithms. The algorithm generates several probabilities in advance for each category. The algorithm then revises the probabilities and constructs a posterior probability. This is useful when predicting whether an input falls into a given list of classes.
· Decision Tree
The decision tree algorithm is similar to a flowchart, where nodes represent the test of an input attribute while branches describe the test result.
· Random Forest
Random Forest works like a collection of trees. The input data is divided into multiple decision trees. The average output of all decision trees is evaluated. Random forests are more accurate than the decision tree algorithm.
· K Neighbors
KNN algorithm uses a collection of data points that have been classified to predict the class of an individual sample data point. It is known as a “lazy-learning algorithm” because it is relatively short compared to other algorithms.
2. Regression Algorithms
These algorithms must be able to predict future outcomes based on input data. It is preferred to use an algorithm which builds a model using the characteristics of the training data and then uses that model to predict new data. The most commonly used algorithms in this section include:
· Linear Regression
It’s used to evaluate natural qualities by assessing constant variables. It is one of the simplest regression algorithms.
· Lasso-Regression
The lasso-Regression algorithm finds the subsets of predictors, which minimizes prediction errors for a given response variable.
· Logistic Regression
Logistic Regression is used primarily for binary classification.
· Multivariate Regression
This algorithm must be used when more than one predictor variables are present.
· Multiple-Regression Algorithm
Multiple-Regression Algorithm combines linear and nonlinear regression algorithms, considering multiple explanatory variables.
3. Clustering Algorithms
Cluster analysis is a good example of unsupervised machine learning. This involves detecting groupings in data. Unlike supervised learning methods, clustering algorithms only interpret input data to find natural groups in the feature space.
However, each computer or gadget can run thousands of artificial intelligence algorithms simultaneously. Artificial intelligence is growing rapidly because it can simultaneously complete several instructions. Everyday devices become intuitive by performing multiple actions simultaneously.
· K Means Clustering
It’s the easiest unsupervised learning algorithm. The algorithm groups together identical data points and attaches them to a group.
· The Fuzzy c-Means Algorithm
FCM algorithm is based on probabilities. Each data point has a probability of belonging to another cluster. Data points do not have an absolute the algorithm is named fuzzy because it is based on membership in a particular cluster
· Expectation-Maximization (EM) Algorithm
It is based on the Gaussian distribution we learned in statistics. To solve the problem, data is visualized in a Gaussian model.
· Hierarchical Clustering Algorithm
After learning data points and observing similarity, these algorithms group clusters in hierarchical order.
Conclusion
Due to the many applications of artificial intelligence algorithms, there is a great demand for AI developers, and it offers a bright AI career in future. Artificial intelligence is a growing field that offers a variety of career opportunities.
Various Artificial intelligence certifications are available for beginners looking to enter the field. AI training programs offer supervised and non-supervised learning and instruction in the latest AI advances and technical approaches.
