AI Machine Learning Models
Three words can describe the whole game under the concept of the ML model — Training, Evaluation, and Output. Artificial Intelligence (AI)Machine Learning (ML)model means a file, which has got specific training to recognize various patterns. AI-ML is trained over data-sets, the latter being provided with related algorithms and predicting some results out of them. For instance, you are building an application to create Emojis. Means? An application that will recognize your emotions based on the way your face is expressing! AI-Machine Learning model can be trained by giving it images of faces (required data)and all these images are tagged with specific emotions. Subsequently, the very ML model is applied in that application enabling it to recognize your emotions (prediction or the output).
What is AI-ML Model
AI-Machine Learning model is a mathematical algorithm that is trained based on data input and replicates a decision process that would lead to specific predictions. AI/ML model incorporates the replication of a decision process that would lead to an understanding in an automated way. Through an algorithm, the training processes vast data sets thus yielding a ‘trained ML model’.This model can detect patterns under varied situations and can distinguish them giving the required predictions. To t sum it up succinctly, the AI Machine Learning model is just like a mathematical function. It accepts some request as an input(data), then processes this data coming out with a prediction, and then serves a response!
Common AI-ML Models
AI-Machine Learning models are commonly categorized into two –
· Supervised Models
· Unsupervised Models
1. Supervised ML Models
Supervised ML model describes the pattern as detected out of the training data. The model describes the signal in the noise emanating from data sets given in training. Thus, it involves a function that emerges from learning patterns based on input/output theory. For example, there is a data set consisting of two variables — an age chart (as an input) and height (as an output). Then by implementing the Supervised ML model, you can find your height based on your age(the model will predict your age).
Age Chart → Supervised ML Model → Height Prediction
Supervised ML Model consists of the following sub-categories:
1. Regression
In Regression ML Supervised models, there is a continuous form of output. It has the following types-
· Linear Regression
As the word depicts, it is about finding a particular line that is the most suitable fit for given data. Some extensions of the Linear Regression type are Multiple Linear Regression and Polynomial Regression.
Source — https://towardsdatascience.com
· Decision-Trees
Another type of Regression model — Decision Trees are used for strategic planning or operations research. The trees are in the form of Nodes, and the idea is, the more nodes you have, your decision tree will be more accurate! The nodes that exist in the last part are those which contain the final output,i.e., the final decision. Those nodes are termed as leaves of the tree. Usually, this model type (Decision trees) is intuitive and can be easily built.
· Random-Forests
This form of Regression model consists of ensemble learning techniques that are built upon multiple decision trees. Random Forests (AI-Machine Learning Model)create varied decision trees with the use of bootstrapped datasets of the original datasets; at each point, or step, of the decision tree, they randomly select a subset of variables. The Random Tree model thereafter does the selection of the prediction- modes for each decision tree. By doing this, the model reduces the risk of any kind of error occurring from an individual decision tree.
· Neural Network
This type of Regression ML model exists in the form of a network made of mathematical equations. A Neural Network(an AI-Machine Learning model part)works in a way — by taking input variables, undergoing through a series of equations or the network of mathematical equations, it gives out output variables. Thus, a Neural Network accepts a vector of inputs, and then, gives out a vector of outputs.
2. Classification
In this type of AI-Machine Learning Model, the Supervised Model there comes out an output that is discreet in nature. It has the following types:
· Logistic Regression Model
The logistic Regression Model is very similar to the Linear Regression Model. However, this model is used to showcase the probability of outcomes in a finite number, say, just the ‘two in number. It is favored over the Linear model owing to varied reasons when used for modeling the probabilities of outcomes! This model is ideally created in a manner that it is bound to produce output values in two numbers,i.e., 0 and 1.
· Support Vector Machine Model
AI-Machine Learning model incorporates this model type because it is very intuitive at the fundamental level. It is a supervised classification technique. Though gets a bit complicated Support Vector Machine Model is intuitive when talks at the fundamental level.
· Naive Bayes
Naive Bayes Model is a popular Data Science Classifier. This Supervised Classification model works on the popular Bayes Theorem:
Source — https://towardsdatascience
· Random Forest, Decision Tree, Neural Network
These Supervised models (AI-Machine Learning model) follow a similar logic as explained under the Regression type. One difference you should know is the output you get here is discreet in nature, and not continuous.
2. Unsupervised Models
Unsupervised Models are used to find patterns from the given data input to give outcomes, to draw inferences (much unlike Supervised models). The two types are:
1. Clustering
Clustering, as the name depicts, is the model that does the clustering of data points. This model is perfect for fraud detection, customer segmentation, classification of documents, and so on. Some important Clustering techniques are-
· Hierarchical Clustering
· K- Means clustering
· Density-based Clustering
· Hierarchical Clustering
2. Dimensionality Reduction Model
Under this model, the dimension of feature sets is reduced.,i.e., the number of features is reduced. A popular method that falls under this model is PCA (Principal Component Analysis ).
Conclusion
There are so many AI-Machine Learning models that have multiple use-cases across industries. No doubt, all these models work on specific algorithms. You have seen certain models that are commonly used today. However, there also exist many other ML models, such as the Reinforcement model, Imitation Learning model, etc. dealing with complex tasks like Robotic Motion, Text Mining, optimization, etc.
