Название: Machine Learning with Python
Автор: Tarkeshwar Barua, Kamal Kant Hiran, Ritesh Kumar Jain, Ruchi Doshi
Издательство: De Gruyter
Год: 2024
Страниц: 486
Язык: английский
Формат: pdf (true)
Размер: 40.4 MB

This book explains how to use the programming language Python to develop Machine Learning and Deep Learning tasks.

Machine Learning (ML) is a discipline within the field of Artificial Intelligence (AI) that concentrates on the creation of algorithms and models, allowing computer systems to acquire knowledge and make forecasts or choices without the need for explicit programming. The primary objective of ML is to empower computers to autonomously learn and enhance their performance based on experience or data.

ML, a branch of AI, enables computers to acquire knowledge and reach conclusions without the need for explicit instructions. This revolutionary discipline encompasses different methodologies, each designed to address specific learning situations. The main forms of ML comprise supervised learning, unsupervised learning, and reinforcement learning, each providing distinct approaches and applications for solving various problems.

Python, a widely used general-purpose interpreted programming language, has gained immense popularity. It boasts a dynamic type system, automatic memory management, and supports multiple programming paradigms such as imperative, functional, and procedural. Python enables the creation of automated solutions for various tasks. Currently, major IT companies including Google, Microsoft, and Apple rely on Python as their primary programming language. Notably, Python stands out as the easiest programming language to learn within a short period of time. It empowers developers to build a wide range of applications, such as desktop, web, and mobile, with minimal coding effort, thanks to the abundance of frameworks and libraries available.

Furthermore, Python is free and open source, with comprehensive documentation accessible online. Python primarily emphasizes business logic over basic programming concepts. Although Python is available in various versions, we will primarily focus on version 3.7, with the upcoming release of Python 3.8 in October 2019, which will introduce new features and address performance-related issues. Python possesses the traits of being dynamically typed, automatically garbage-collected, and having memory management capabilities, while supporting multiple programming paradigms including procedural, object-oriented, and functional. It is an agile language that allows for rapid development of customized solutions in a short span of time. Additionally, Python facilitates modular application development, enabling developers to join modules together seamlessly before the final release.

Classic machine learning algorithms constitute the foundational framework of contemporary data science and serve as indispensable tools for the analysis and prediction of data. These algorithms encompass a broad range of techniques that are fundamental to the comprehension of the principles and applications of machine learning. Linear regression, in spite of its simplicity, serves as a powerful algorithm utilized for the purpose of modeling the correlation between independent and dependent variables. It is extensively employed in the realm of predictive analytics and forecasting tasks, with variations such as Simple Linear Regression, Multiple Linear Regression, and Polynomial Regression, tailored to cater to different scenarios...




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