Boost your skills by learning these vital Ai algorithms
By 2035 AI could boost average profitability rates by 38 percent and lead to an economic increase of $14 Trillion.
— Accenture
An algorithm’s complexity will be based on the complexity of every step, which is required to execute, and on the sheer number of steps it is required to execute. Mostly the algorithms are quite simpler. Computing has undergone major transformations from large mainframes to personal computers to the cloud. The progress in technology and constant evolution in computing has given rise to automation.
In this article, let’s understand the few commonly used Ai algorithms, which are helpful for solving any type of data problem.
● Decision Tree
This is among the supervised Ai algorithms, which are mainly used for classification problems. This algorithm best fits both categorical and continuous dependent variables. With the help of this algorithm, the population is divided into two or more homogeneous sets based on the most significant independent variables/ attributes. The Decision tree algorithm is very helpful in the banking industry for the classification of loan applicants.
● Linear Regression
This is used to estimate the real values like the cost of properties, number of calls, total sales, and many more depending on a continuous variable. In this process, a relationship is formed between the independent and dependent variables by fitting the best line. This best fit line is called the regression line and is represented by a linear equation Y= a *X + b.
In this equation:
- Y — Dependent Variable
- a — Slope
- X — Independent variable
- b — Intercept
The coefficients a &b are derived based on decreasing the sum of squared difference of distance between the regression line and data points. The Ai professionals use this algorithm for risk assessment in the insurance sector. Linear regression is used to find the number of claims for customers of multiple ages and then deduce the increased risk based on the age of the customer.
● Logistic Regression
This is used to review discrete values (mainly Binary values like 0/1, yes/no, true/false) based on the available set of the independent variable(s). In simple terms, it is useful for predicting the probability of occurrence of an event by fitting data to a logit function. It is also called logit regression.
The following list can be tried to improve the logistic regression model
- Including interaction terms
- Removing features
- Regularize techniques
- Using a non-linear model
Logistic Regression is used highly in the political sector to predict if a specific candidate will win or lose a political election.
● Support Vector Machine (SVM)
Among the various Ai algorithms, this is a classification method where the raw data gets plotted as points in n-dimensional space (Here n is the number of features that are available). The value of every feature is being the value to a particular coordinate. This makes it quite easy to classify the data. For instance, if we take two features like the height of a person and hair length. First, these two variables will be plotted in the two-dimensional space, where each point has two coordinates, these are called Support Vectors.
The Support Vector Machine learning algorithm is used for comparison of stock performance for stocks in the same sector. This is also helpful in making decisions for managing investments by financial institutions.
● Naive Bayes
The Naive Bayes classifier learning algorithm is based on the Bayes Theorem of Probability. In this, it assumes that the availability of a certain feature in a class is unrelated to the availability of any other feature. The Naive Bayes classifier will consider these properties independently while calculating the probability of a certain result. The Naive Bayes classifier algorithm is useful for Email Spam Filtering. Gmail mainly uses this algorithm to classify an email as Spam or Not Spam.
End Notes
If Ai professionals want to build a stellar career in artificial intelligence, then they should start right away. It is an emerging sector, the sooner one gains knowledge of these algorithms, the better they can perform the tasks that involve complex problems. Possessing an in-depth knowledge of these algorithms is very helpful to enhance one’s career.
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