Many of the world's services are powered by machine learning algorithms. Machine learning is a fast-growing market because of its potential application, but Ripton Rosen cautions that it can be a hard industry to break into. Machine learning models are effective because of a programmed algorithm, and there are several basic algorithms that provide the core foundation for data learning.

## The Top Machine Learning Algorithms

Machine learning algorithms can help solve highly complex, real-world challenges. There are four categories of these algorithms: supervised, unsupervised, reinforcement, and semi-supervised. These categories divide how the machine learns according to the algorithm used, and these are some of the top algorithms in use today.

### Logistic Regression

Also called logit regression, the algorithm is supervised learning and is used for binary classification. This is decision-making that attempts to put specific data into one class or another. This is a prediction of the probability of categorizing an input into a particular class. While it can be used for predictive modeling, its use is typically reserved for binary classification projects.

### Linear Regression

Another supervised learning algorithm, linear regression, can predict and forecast values along a continuous range. This works by performing a regression task. Using an input value and a variable output, a constant slope is mapped to predict a quantity or numeric value. The labeled data create a line of best fit or regression line, which analysis uses to make predictions.

### Naïve Bayes

As a supervised learning algorithm, this is a predictive model applicable for both multi- or binary classifications. According to Rosen, Bayes' theorem utilizes conditional probabilities assumed from combined factors to make a classification. This algorithm notes the likelihood of certain categorizations for data.

### Random Forest Algorithm

With this algorithm, a collection of decision trees is used for predictive modeling and classification. This model could use hundreds or thousands of decision trees, though each is trained with a random sampling from the set. Researchers enter the same data into each tree in the random forest and select the most common result from the sum of all results as the most likely outcome.

### Decision Tree

In this supervised learning application, one decision tree (resembling a flowchart) takes a complex data set and works it through the tree with specific questions. Answers to the question could move the data down the tree or to another branch as the questions and answers for the data evolve.

### K-Nearest Neighbor Algorithm

Ripton Rosen advises that this supervised algorithm works for predictive modeling and classification since it classifies an output according to the data's close alignment with other outputs on a graph. If an output presents closer to a cluster of red points when compared to the cluster of blue points, the result is the classification of the data as a member of the red group.

### K Means Algorithm

Although similar to the KNN proximity classification system, this algorithm is an unsupervised learning path. The classification is made using the proximity of a real or imaginary center point within the cluster and the output, making it useful for larger data sets and an accurate connection.

### Support Vector Machine Algorithm

Classification or regression problems use this algorithm to divide the data into distinctive categories according to the location of a particular line that separates the data set. The algorithm works to find the hyperplane that creates the maximum distance between the data classes, which increases the probability of a more accurate classification for data.

## The Importance of Knowing Machine Learning Algorithms

Choosing the right algorithm for the data set influences the accuracy of your anticipated results. According to Ripton Rosen, algorithms should be specific to the type of data or desired outcome since the decision-making varies according to inputs and outputs.