Machine learning is an application of Artificial intelligence (AI) that provides a systems the ability to automatically learn and improve from the experience without being explicitly programmed.
Machine learning mainly focus on the development of an computer logical programs that can access any data and use it learn for themselves.
Machine Learning has been classfied into three main Algorithms.
1.Supervised Learning.
2.Unsupervised Learning.
3.Reinforcement Learning.
This algoritham contains a target (or) outcome variable (or dependent variable) which is to be predicted from any given set of predictors (independent variables).Using these set of variables, we can generate a function that maps inputs to a desired outputs.This process atands until a model achieves a desired level of accuracy on a training data.
Ex : Regression, Decision Tree, Random Forest,KNN,Logistic Regression etc.
This type of Algorithm we do not have any outcome variable (or) target to predict.It is mainly used for clustering population in different groups, such as widely used for a segmenting customers in different groups for a specfic intervention.
Ex: Apriori Algorithm,k-means.
This Algorithm the machine is trained to make a specfic decisions.It works in such a way as a Machine is exposed to an envirnoment such that it trains y itself and continually using trail and error.This machine learns from a past ecperience and tries to capture all the best possible knowledge for accurate decisions.
Ex: Markov Decision Process
There have been dozens of articles for writing and comparing a python and R Data Codes for a subjective point of view.
1.Linear Regression.
Linear Regression is used to estimate the real values based on continuous variables such as (cost of houses,number of calls,total sales etc.)
2.Logistic Regression.
Logistic Regression is a classification not a regression algorithm.It is used to estimate discrete values as Biary 0/1,yes/no,true/false.
3.Decision Tree
It is a type of supervised learning algoritham that is mostly used for classfication of problems.
It works for both categorical and continuous dependent variables.Here in this algoritham we can split the population into 2 or more homogeneous sets.
4.SVM (Support Vector Machine)
This algoritham is called as classfication method.
Here we plot each data item as apoint in n-dimensional space with a value of each feature being value of a particular coordinate.
5.Naive Bayes
This algorithm is based on Bayes' theorem with an assumption of and ndependence predictors.
Naive Bayesian model is an easy to build and particularly useful for very large data sets.
6.kNN (k- Nearest Neighbors)
It can be used for classfication and Regression problems.
K nearest neighbours is a simple algorithm that stores all available cases and classifies.
7.k-Means
It is a type of unsupervised which sloves the clustering problem
R-Code library(cluster) fit <- kmeans(X, 3) # 5 cluster solution
8.Random Forest.
Random Forest is a trademark term for an ensemble of decision trees.
R-Code
library(randomForest) x <- cbind(x_train,y_train) # Fitting model fit <- randomForest(Species ~ ., x,ntree=500) summary(fit) #Predict Output predicted= predict(fit,x_test)
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