The machine learning model is nothing but a piece of code; an engineer or data scientist makes it smart through training with data. Its further divided into two categories:
In Supervised learning, an AI system is presented with data which is labelled, which means that each data tagged with the correct label. The supervised learning is categorized into 2 other categories which are “Classification” and “Regression”.
Classification problem is when the target variable is categorical (i.e. the output could be classified into classes — it belongs to either Class A or B or something else).
These some most used classification algorithms:
K-Nearest Neighbor
Naive Bayes
Decision Trees/Random Forest
Support Vector Machine
Logistic Regression
While a Regression problem is when the target variable is continuous (i.e. the output is numeric).
These some most used regression algorithms:
Linear Regression
Support Vector Regression
Decision Tress/Random Forest
Gaussian Progresses Regression
Ensemble Methods
In unsupervised learning, an AI system is presented with unlabeled, un-categorized data and the system’s algorithms act on the data without prior training. The output is dependent upon the coded algorithms. Subjecting a system to unsupervised learning is one way of testing AI The unsupervised learning is categorized into 2 other categories which are “Clustering” and “Association”.
A set of inputs is to be divided into groups. Unlike in classification, the groups are not known beforehand, making this typically an unsupervised task.
These some most used Clustering algorithms:
Clustering
Methods used for clustering are:
Gaussian mixtures
K-Means Clustering
Boosting
Hierarchical Clustering
K-Means Clustering
Spectral Clustering
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