Machine Learning is a way of analyzing data and automates the analytical model building. It is a branch of Artificial Intelligence that deals with the idea that machines can learn from data. Under machine learning development services, developers train ML models using patterns and logic without explicit programming to derive the preferred output.
In Machine Learning we consider n sample of data and then try to predict the property of unknown data. If each sample contains more then one property or multidimensional array, then it is said having multiple features or attributes. All such machine learning algorithms are a part of the larger scheme, i.e. artificial intelligence services.
Supervised learning is learning in which we have training dataset as well as the output to train the machine.Under supervised learning, we are well aware and in control of the output to be received in exchange of the sample input. Supervised learning is done basically in the context of classification and regression. Classification is what when we want to map our input to some output label whereas regression is what when we want to map our input to the continuous output variable. Some algorithms that are commonly used in supervised learning are KNN, decision tree, random forest and support vector machine(SVM), etc. In supervised learning, we generally use the model to train the machine and predict the result on the basis of training data and testing data.
Unsupervised learning is training a machine from data or information which is neither classified nor labeled and allows the algorithm to act upon the information without any guidance. In unsupervised learning AI machines grouped the unsorted data based on similarities and differences even though there are no categories. Chatbot, self-driving car, facial recognition, expert System and robots are some common application of machine learning.
A small application of machine learning in python using Scikit Learn Library. In the following application, we have used iris dataset provided by Sklearn. This dataset contains 50 samples of each three species of iris i.e Setosa, Virginica, Versicolor. Four features were measured from each sample i.e petal length, petal width, sepal width, and sepal length. Based on this data we will develop a small application to distinguish the feature from the species. All we need to know is the basics of python and Scikit learn library before we begin.
import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import accuracy_score
iris = datasets.load_iris()
x = iris.data[:,:2]
y = iris.target
X_tr, X_tes, y_tr, y_tes = train_test_split(x, y, test_size=0.25, random_state=42)
knn = KNeighborsClassifier(n_neighbors=5)
knn.fit(X_tr , y_tr)
y_preds = knn.predict(X_tes)
acc = accuracy_score(y_tes , y_preds)
In the above application, iris is dataset from which we store the data in x and label in y. knn is the classifier provided by the Sklearn.
First, we divided the data into training data and the test data and train the model by providing training data X_tr and y_tr. Then we predict the output of test data which will give the label of the test data. At last, we get the accuracy score which is nothing but the percentage of change in the original label and the predicted label.