Machine Learning Tutorial

What is Machine Learning? Machine Learning Life Cycle Python Anaconda setup Difference between ML/ AI/ Deep Learning Understanding different types of Machine Learning Data Pre-processing Supervised Machine Learning

ML Regression Algorithm

Linear Regression

ML Classification Algorithm

Introduction to ML Classification Algorithm Logistic Regression Support Vector Machine Decision Tree Naïve Bayes Random Forest

ML Clustering Algorithm

Introduction to ML Clustering Algorithm K-means Clustering Hierarchical Clustering

ML Association Rule learning Algorithm

Introduction to association Rule Learning Algorithm

Miscellaneous

Top 5 programming languages and their libraries for Machine Learning Basics Vectors in Linear Algebra in ML Decision Tree Algorithm in Machine Learning Bias and Variances in Machine Learning Machine Learning Projects for the Final Year Students Top Machine Learning Jobs Machine Learning Engineer Salary in Different Organisation Best Python Libraries for Machine Learning Regularization in Machine Learning Some Innovative Project Ideas in Machine Learning What is Cross Compiler Decoding in Communication Process IPv4 vs IPv6 Supernetting in Network Layer TCP Ports TCP vs UDP TCP Working of ARP Hands-on Machine Learning with Scikit-Learn, TensorFlow, and Keras Kaggle Machine Learning Project Machine Learning Gesture Recognition Machine Learning IDE Pattern Recognition and Machine Learning a MATLAB Companion Chi-Square Test in Machine Learning Heart Disease Prediction Using Machine Learning Machine Learning and Neural Networks Machine Learning for Audio Classification Standardization in Machine Learning Student Performance Prediction Using Machine Learning

International Journal of Machine Learning and Computing Iris Dataset Machine Learning

Building models and algorithms that can learn on information and arrive at predictions or judgements based on that data is the focus of the branch of study known as machine learning. It is a segment of artificial intelligence that deals with teaching computers how to pick up new skills via experience rather than through explicit programming.

Contrarily, computing describes how to employ computers & computational methods to carry out activities, solve problems, store, and analyse data.

A common benchmark for classification systems is the Iris dataset, a well-known dataset in machine learning. It provides details on the morphological characteristics of three distinct iris flower species: Iris setosa, Iris versicolor, and Iris virginica. The dimensions of each flower's sepal and petal are among the characteristics. The dataset's objective is to identify the species a iris flower depending on these characteristics.

We normally divide the Iris dataset into a training dataset as well as a testing set in order to deploy machine learning techniques to it. The machine learning models are trained using the training set, and their effectiveness on fresh, untested data is assessed using the testing set.

The Iris dataset can be used with a variety of machine learning techniques, such as regression models, decision trees, regression trees, and support vector regression. These algorithms assume things about species of flower based on the characteristics of the blossoms.

Metrics including precision, accuracy, recall, or F1 score can be used to assess these algorithms' performance. These metrics provide us with a sense of how well your simulations are doing, and they can aid us in selecting the most appropriate method for the job at hand.

Iris Dataset Exploration:

In 1936, statistician Ronald Fisher published a paper titled "The use of numerous measures in taxonomic issues" that served as the first official introduction to the Iris dataset. There are 150 observations total in the dataset, 50 of which are for each of the 3 species of iris flowers. Sepal height, sepal breadth, petal length, and petal width are the four features that each observation consists of. The dataset's objective is to identify the species of a iris flower depending on these characteristics.

We can read inside the dataset using Python's Pandas package and display some fundamental statistics to investigate the dataset:

import pandas as pd


url = "https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data"
names = ['sepal-length', 'sepal-width', 'petal-length', 'petal-width', 'class']
df = pd.read_csv(url, names=names)


print(df.head())
print(df.describe())

The Iris dataset will be read into the UCI Machine Learning Repository, given column names, and then the first few rows of the dataset, along with some summary statistics, will be displayed. The final result should resemble this:

sepal-length  sepal-width  petal-length  petal-width class
0           5.1          3.5           1.4          0.2  Iris-setosa
1           4.9          3.0           1.4          0.2  Iris-setosa
2           4.7          3.2           1.3          0.2  Iris-setosa
3           4.6          3.1           1.5          0.2  Iris-setosa
4           5.0          3.6           1.4          0.2  Iris-setosa
sepal-length  sepal-width  petal-length  petal-width
count    150.000000   150.000000    150.000000   150.000000
mean       5.843333     3.054000      3.758667     1.198667
std        0.828066     0.433594      1.764420     0.763161
min        4.300000     2.000000      1.000000     0.100000
25%        5.100000     2.800000      1.600000     0.300000
50%        5.800000     3.000000      4.350000     1.300000
75%        6.400000     3.300000      5.100000     1.800000
max        7.900000     4.400000      6.900000     2.500000

The output shows that there are 150 observations in the dataset, each of which has four features as well as a correct class label. The characteristics' median values range from 5 to 6, and their standard deviations are rather small.

Applications of machine learning and computing iris dataset machine learning:

There are several uses for computing and machine learning in numerous industries. Applications for computer science and machine learning include:

Recognition of voice patterns and visual patterns is possible with the use of machine learning algorithms. Image recognition algorithms, for instance, can be used to categorise photographs based on their content while voice recognition algorithms may be employed to transform spoken words into text.

Machine learning algorithms may be employed to examine and comprehend material written in natural language. Sentiment analysis algorithms, for instance, can be used to examine customer reviews and ascertain the general attitude towards a good or service.

Fraud detection: Algorithms for machine learning can be used to spot fraudulent activity in financial systems. These algorithms are capable of analysing transaction data and spotting fraudulent pattern-making.

Healthcare: Patterns that are suggestive of disease can be found by analysing patient data using machine learning algorithms. Machine learning algorithms, for instance, can be used to identify conditions like diabetes and cancer.

Systems that recommend goods and services to consumers based on past behaviour and interests are known as recommender systems. Users may receive movie recommendations through recommender systems, for instance, depending on their prior viewing habits.

Machine learning techniques can be applied to the Iris dataset to identify the species of irises flower by looking at the features of a flower. This may be useful in horticulture and other disciplines where it could be crucial to determine the genus of a given flower.