All Machine Learning Models Explained in 5 Minutes _ Types of ML Models Basics

Welcome to Whiteboard programming where we simplify programming for you with easy to understand whiteboard videos .

And today , I'll be giving you a brief explanation of all machine learning models .

So let's get started broadly speaking , all machine learning models can be categorized as supervised or unsupervised .

We'll uncover each one of them and what all types they have .

Number one supervised learning .

It involves a series of functions that maps an input to an output based on a series of example , input output pairs .

For example , if we have a data set of two variables , one being age , which is the input and the other being the shoe size as output , we could implement a supervised learning model to predict the shoe size of a person based on their age .

Further with supervised learning , there are two sub categories .

One is regression and the other is classification in regression model .

We find a target value based on independent predictors .

That means you can use this to find a relationship between a dependent variable and an independent variable in regression models .

The output is continuous .

Some of the most common types of regression model include number one linear regression , which is simply finding a line that fits the data .

Its extensions include multiple linear regs that is finding a plane of best fit and polynomial regression .

That is finding a curve for best fit next one decision tree .

It looks something like this where each square above is called a node and the more nodes you have the more accurate your decision tree will be in general next .

And the third type random forest , these are assemble learning techniques that builds off over decision trees and involve creating multiple decision trees using bootstrapped data sets of original data and randomly selecting a subset of variables at each step of the decision tree .

The model then selects the mode of all the predictions of each decision trees .

And by relying on the majority wins model , it reduces the risk of error from individual tree next neural network .

It is quite popular and is a multilayered model inspired by human mind like the neurons in our brain .

The circle represents a node .

The blue circle represents an input layer .

The black circle represents a hidden layer and the green circle represents the output layer .

Each node in the hidden layer represents a function that input goes through ultimately leading to the output in the green circles .

Next classification .

So with regression types being over now let's jump to classification .

So in classification , the output is discrete .

Some of the most common types of classification models include first logistic regression which is similar to linear regression but is used to model the probability of a finite number of outcomes .

Typically two next support vector machine .

It is a supervised classification technique that carries an objective to find a hyper lane in N dimensional space that can distinctly classify the data points next nave base .

It's a classifier which acts as a probabilistic machine learning model used for classification tasks .

The crux of the classifier is based on the base theorem .

Coming up .

Next decision trees , random forest and neural networks .

These models follow the same logic as previously explained .

The only difference here is that the output is discrete rather than continuous .

Now , next , let's jump over to unsupervised learning .

Unlike supervised learning , unsupervised learning is used to draw inferences and find patterns from input data without references to the labeled outcome .

Two main methods used in supervised learning include clustering and dimensionality reduction .

Clustering involves grouping of data points .

It's frequently used for customer segmentation , fraud detection and document classification .

Common clustering techniques include K means clustering , hierarchical clustering , mean shape , clustering and density based clustering .

While each technique has different methods in finding clusters , they all aim to achieve the same thing coming up .

Next dimensionality reduction , it is a process of reducing dimensions of your feature set or to state simply reducing the number of features .

Most dimensionality reduction techniques can be categorized as either feature elimination or feature extraction .

A popular method of dimensionality reduction is called principal component analysis or PC A .

Obviously , there's a ton of complexity .

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