Machine Learning Algorithms
According to a recent study, machine learning algorithms are expected to replace 25% of the jobs across the world, in the next 10 years. Machine learning applications are highly automated and self-modifying which continue to improve over time with minimal human intervention as they learn with more data.
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For instance, Netflix’s recommendation algorithm learns more about the likes and dislikes of a viewer based on the shows every viewer watches. To address the complex nature of various real world data problems, specialized machine learning algorithms have been developed that solve these problems perfectly.
Unsupervised Machine Learning Algorithms
There are no labels associated with data points. These machine learning algorithms organize the data into a group of clusters to describe its structure and make complex data look simple and organized for analysis.
Naive Bayes Classifier Algorithm
It would be difficult and practically impossible to classify a web page, a document, an email or any other lengthy text notes manually. This is where Naïve Bayes Classifier machine learning algorithm comes to the rescue.
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Naïve Bayes Classifier is among the most popular learning method grouped by similarities, that works on the popular Bayes Theorem of Probability- to build machine learning models particularly for disease prediction and document classification. It is a simple classification of words based on Bayes Probability Theorem for subjective analysis of content.
K-Nearest Neighbors Algorithm
The KNN used to compare between the training neighbors. When a new texts
document need to be categorized, the most similar documents, k are found and if a large
proportion of them have been assigned to certain category, otherwise not.Its is a non-parametric methods use to stores all available cases and clarifies new cases based on a similarity measure.
The main goal of KNN is the complexity in search for the nearest neighbor and the nearest neighbor being measured by distance function. It is also a lazy algorithm. What this means is that it does not use the training data points to do any generalization. In other words, there is no explicit training phase or it is very minimal. This means the training phase is pretty fast . Lack of generalization means that KNN keeps all the training data. More exactly, all the training data is needed during the testing phase.
Support Vector Machine Algorithm
Support Vector Machine is a machine learning algorithm for classification or regression problems where the dataset teaches SVM about the classes so that SVM can classify any new data. It works by classifying the data into different classes by finding a line (hyperplane) which separates the training data set into classes.
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As there are many such linear hyperplanes, SVM algorithm tries to maximize the distance between the various classes that are involved and this is referred as margin maximization. If the line that maximizes the distance between the classes is identified, the probability to generalize well to unseen data is increased.
J48 Decision Trees Algorithm
A decision tree is a graphical representation that makes use of branching methodology to exemplify all possible outcomes of a decision, based on certain conditions.
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In a decision tree, the internal node represents a test on the attribute, each branch of the tree represents the outcome of the test and the leaf node represents a particular class label. After that, the decision will made after computing all of the attributes.