What is supervised and unsupervised machine learning?

Supervised and unsupervised machine learning are two paradigms in the world of machine learning, each of which has its own applications. A brief description of each of these technologies is as follows:

Supervised Machine Learning

In supervised machine learning, models are trained using training data that has a label or expected output. In other words, each training data consists of inputs (features) and corresponding output (labels). The purpose of model training is to find patterns and relationships between inputs and outputs. Then, using the trained model, it is possible to predict the desired output for new unlabeled data. Examples of supervised machine learning algorithms are: Support Vector Machines (SVM), Neural Networks, Decision Trees, Linear Regression and Naive Bayes Classifiers.

Unsupervised Machine Learning

In unsupervised machine learning, data is trained without specific labels and output. The main goal in this type of learning is to discover hidden patterns, structures and relationships in the data. That is, models try to divide data into similar groups or automatically extract important features.

Examples of unsupervised machine learning algorithms are: Clustering, Matrix Factorization, Dimensionality Reduction, and Association Rules.

Supervised learning has more desired labels and outputs than unsupervised learning and is mostly used for classification and prediction problems. But unsupervised learning does not require labels and is mostly used to discover hidden patterns and structures in data. In some cases, both types of machine learning are used in combination to provide better performance for the given problem. According to the explanations we provided, we must say:

In supervised machine learning, the training data consists of input-output pairs. Based on these data, supervised machine learning algorithms learn rules and patterns so that they can predict the desired output for new data.

In unsupervised machine learning, the training data is unlabeled, and the main goal of this type of learning is to discover hidden patterns, structures, and relationships in the data. For example, in clustering, the algorithm tries to divide the data into similar groups without considering a specific label or output.

Also, we should mention the existence of semi-supervised learning. In this type of learning, part of the training data is labeled and the other part is unlabeled. The goal is also to optimize prediction for unlabeled data used in training.

All three types of machine learning (supervised, unsupervised, and pseudo-supervised) are used in the field of machine learning and artificial intelligence, and each has its own uses, advantages, and limitations. Choosing the right type of learning depends on the problem and the data being used.

Python installation and development environment

First, download and install the required version of Python from the official website www.Python.org. Next, install a development environment such as Anaconda or PyCharm. These environments are more complete and provide useful tools for machine learning.

Install the required libraries

After installing Python, you will need to install some popular machine learning libraries such as NumPy, Pandas, and Scikit-learn. To install them, you can use the pip package manager. For example, you can use the following command to install the nampy library:

 pip install numpy pandas scikit-learn

Familiarity with basic concepts

Before starting machine learning, you should learn basic concepts such as the concept of data, matrices, mathematical and statistical operations. These concepts are very important in machine learning.

Training of machine learning models

After familiarizing yourself with the basic concepts, you can start training machine learning models. The Scikit-learn library provides a variety of implementations of machine learning algorithms that you can use. For example, to train a simple classification model such as SVM, you can use this code:

 from sklearn import svm

 from sklearn import datasets

 # Load training data

 iris = datasets.load_iris()

 X = iris.data

 y = iris.target # SVM model training

 model = svm.SVC()

 model. fit(X, y)

Model evaluation

After training the model, you can evaluate it on test data. For this, you can use evaluation criteria such as accuracy, confusion matrix and ROC curve.

Improve and adjust the model

Some models may need to be improved and adjusted. You can use methods like feature selection, parameter optimization, and fault finding to improve model performance.

Using a trained model

After training and improving the model, you can use it to make predictions and analyze data. You can feed the model new data to produce the expected output.

What is regression in machine learning?

Regression is a supervised machine learning method that is used to predict a continuous variable for one or more input variables. The main purpose of regression is to establish a functional relationship between dependent variables (output) and independent variables (input).

In regression, inputs are considered as independent variables and desired output as dependent variable. The regression model tries to understand the patterns and interactions between the dependent and independent variables and creates an estimated function to predict the output for the new data. The type of regression may be different depending on the characteristics of the problem. In the following, I will introduce some common types of regression:

 Linear Regression: In linear regression, a hypothesis is proposed that the relationship between dependent and independent variables is linear. In this method, we try to find the most linear function that best fits the data.

 Polynomial Regression: In polynomial regression, a hypothesis is proposed that the relationship between dependent and independent variables is polynomial. In this method, powers higher than one are used for independent variables in order to have a function that matches the data better.

 Logistic Regression: In logistic regression, the goal is to predict a categorical variable (binary or multicategory). This method is used for classification problems, but it is essentially a regression. Logistic function is used to model the probability of occurrence of each category.

The mentioned cases are only a few examples of regression methods and in practice there are more advanced and more complex methods. Choosing the right regression type depends on the considerations of the problem and the type of data you have. Also, techniques and tools such as feature selection, data rescaling, evaluation methods, and data division into two training and testing parts may be used in regression implementation. Regression is one of the important methods in machine learning and it is used in many areas such as house price prediction, traffic analysis, sales forecasting and other value prediction and estimation problems.

What is classification in machine learning?

Classification is another type of supervised machine learning method that is used to predict and separate data into different categories. The main purpose of classification is to determine the class or category to which an input instance belongs. In classification, inputs are considered as independent variables and categories are considered as dependent variables. The classification model tries to understand the interactions and patterns in the data and create a decision function that makes the correct classification for the new data as input. In classification, we usually seek to determine boundaries or intervals that divide the data into different categories. Some of the popular classification methods are:

 Support Vector Machines: This method tries to find a linear or non-linear boundary between categories. Support vector machine is based on maximizing the distance between boundaries and edge samples (samples that are close to the boundaries).

 Decision Trees: A decision tree is built as a hierarchy of questions based on input features and finally reaches a decision for classification. This method is based on selecting the best feature to split the data and maximize the recovery information.

 Neural Networks: Neural networks are mathematical models that are made by different layers of neurons. This method is able to identify more complex patterns in the data and make classifications based on them.

 Logistic Regression: Although logistic regression was introduced in regression, it is commonly used for classification. Also, in classification, techniques and tools such as feature extraction, data rescaling, classification accuracy assessment methods and data division may be used. Two parts of training and test should be used.

Classification is one of the most important and widely used methods of machine learning. Image segmentation, disease diagnosis, email spam filtering, text analysis, face recognition and many other classification problems are used in various fields.

A practical example of how to build a machine learning model in Python

As a practical example, we can build a simple classification model in Python using the scikit-learn library. In this example, we will use the Support Vector Machine method for classification. For this purpose, we first load the training data and then train the model. In the following, we use the trained model to predict the classification of a new sample.

from sklearn import svm

from sklearn import datasets

from sklearn.model_selection import train_test_split

from sklearn.metrics import accuracy_score

# Load data

iris = datasets.load_iris()

X = iris.data # attributes

y = iris.target # labels

# Divide the data into two parts, training and testing

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)

# Build the model

model = svm.SVC(kernel='linear')

# Training the model with training data

model.fit(X_train, y_train)

# Predict categories for test data

y_pred = model.predict(X_test)

# Calculate the accuracy of the model

accuracy = accuracy_score(y_test, y_pred)

print("Accuracy of the model:", accuracy)

In this example, the Iris dataset is used, which contains features of Iris flowers and their corresponding labels. We have used the train_test_split function to randomly split the data into two parts, training and testing. Then, SVM model with linear kernel is built and trained using training data. Then, using the test data, we have predicted the classification and calculated the accuracy of the model using the accuracy_score function. Note that this is just a simple example, and in real projects, data preprocessing, model parameter tuning, cross-validation, and other more complex steps may be required.