Abstract
In this study, the solution of classification, regression, and time series problems used in different fields with Scikit-learn and TensorFlow libraries using Python programming language is discussed. Four projects are carried out. The first project addressed the classification problem. The performance of the models is evaluated by using classical machine learning techniques and deep neural networks to solve the classification problem. The second project considered is the regression problem. The White Wine Quality dataset is used to understand the regression problem. The third project is the image classification problem. Image classification is one of the essential areas of study in recent years. Classification of images is achieved with CNN, one of the deep learning techniques. Keras and TensorFlow libraries are used in CNN. The last project discussed is the estimation of the closing value of the stock market stock.
TopIntroduction
Python programming language has been preferred in many fields in recent years because of its extensive library support (Srinath, 2017). Some of these areas are as follows:
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In the development of web applications (Burch, 2010)
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In the field of natural language processing (Bird et al., 2009)
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In cyber security education and field (Eckroth, 2018)
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In data science, artificial intelligence, machine learning, data mining, and deep learning studies (Raschka et al., 2020)
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In the field of image processing (Van der Walt et al., 2014)
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In embedded system development and applications (Cho et al., 2023)
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In software testing processes (Okken, 2022)
As you can see, Python programming language is used in almost many areas. These examples can be increased further. The fact that it is an open source, high-level language and easy to use/learn makes this language popular (Srinath, 2017). Python programming language and libraries are the most preferred languages in the field of machine learning (Raschka et al., 2020).
Machine learning is one of the most studied areas in computer science in recent years. It is also used by both researchers and companies in many fields. Health, education, information security, and the economy are some examples of areas where machine-learning techniques are used. In this book chapter, the solution of classification, regression, and time series problems used in different fields with Scikit-learn and TensorFlow libraries using Python programming language will be discussed.
Motivation
Machine learning and deep learning are some of the most critical areas studied by researchers in recent years. Python programming language is frequently preferred by researchers due to the libraries it offers. This book chapter will cover different machine learning problems and enable readers to code an application step by step. In addition to coding, questions such as how to interpret a classification result and how to interpret a regression problem are also answered in the book chapter. The projects in the book section will guide undergraduate and graduate students as well as many people who want to improve themselves in this field. The examples discussed here can be easily applied by readers to different problems. In this way, the reader will gain practice in using machine learning and deep learning algorithms.
Contribution
The contributions of the book section can be summarized as follows:
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The use of essential classification algorithms in the Sklearn library is mentioned.
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Interpretation of classification results is interpreted under different metrics.
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The use of the confusion matrix is demonstrated using the Matplotlib library.
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The use of algorithms used in regression problems is demonstrated.
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Step-by-step coding of the image classification problem is included.
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Stocks purchased at random times are predicted using some algorithms.
Key Terms in this Chapter
Deep Learning: It is an advanced version of artificial neural networks from machine learning techniques.
Machine Learning: It is the modeling of systems that make predictions by using mathematical and statistical processes on data.
Data Mining: It is a technique of discovering correlations, patterns, or trends by analyzing large amounts of data stored in repositories such as databases and storage devices.
Pattern Recognition: Pattern recognition is exactly the process of recognizing patterns with the help of a machine learning algorithm. Machine learning is a field based on the recognition and interpretation of patterns in data. With the pattern recognition system, the computer automatically identifies complex data sets or regular systems.
Artificial Intelligence: Artificial intelligence is simply defined as systems that imitate human intelligence to perform certain tasks and can improve themselves by repeating the information they collect.
Expert System: It is computer software used to solve problems in an information field. The logic of these software; when information is stored in databases and then problems are encountered, it is tried to reach results with inferences made on these databases.