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Machine Learning with Python
未刊 ご予約承ります。
Title: Machine Learning with Python Subtitle: Principles and Practical Techniques Author: Bhatia, Parteek (Thapar University, India) Publisher: Cambridge University Press ISBN: 9781009170246 Cover: PAPERBACK Date: 2025年08月 DESCRIPTION Pythonで機械学習：原理と実践的テクニック この教科書は、このテーマのあらゆる側面を初心者が学んで実践できるように、Pythonにおける実用的な実装とともに機械学習の理論的基礎を紹介します。データサイエンスと機械学習の入門書を探している学生と専門家どちらにとっても欠かせないリソースとなるでしょう。 Machine learning has become a dominant problem-solving technique in the modern world, with applications ranging from search engines and social media to self-driving cars and artificial intelligence. This lucid textbook presents the theoretical foundations of machine learning algorithms, and then illustrates each concept with its detailed implementation in Python to allow beginners to effectively implement the principles in real-world applications. All major techniques, such as regression, classification, clustering, deep learning, and association mining, have been illustrated using step-by-step coding instructions to help inculcate a 'learning by doing' approach. The book has no prerequisites, and covers the subject from the ground up, including a detailed introductory chapter on the Python language. As such, it is going to be a valuable resource not only for students of computer science, but also for anyone looking for a foundation in the subject, as well as professionals looking for a ready reckoner. * Algorithms are explained in detail with examples with a step-by-step approach to make learning easy and simple, assuming no previously existing knowledge * GitHub resources that provide access to datasets, sample code, and examples have been included in each chapter * Advanced topics like Deep Learning, Convolutional Neural Networks, and Recurrent Neural Networks have been covered extensively * An online supplements package includes a solutions manual and lecture slides for instructors, and further online reading and a chapter-wise list of project ideas for students TABLE OF CONTENTS Chapter 1. Beginning with Machine Learning Chapter 2. Introduction to Python Chapter 3. Data Pre-processing Chapter 4. Implementing Data Pre-processing in Python Chapter 5. Simple Linear Regression Chapter 6. Implementing Simple Linear Regression Chapter 7. Multiple Linear Regression and Polynomial Linear Regression Chapter 8. Implementing Multiple Linear Regression and Polynomial Linear Regression Chapter 9. Classification Chapter 10. Support Vector Machine Classifier Chapter 11. Implementing Classification Chapter 12. Clustering Chapter 13. Implementing Clustering Chapter 14. Association Mining Chapter 15. Implementing Association Mining Chapter 16. Artificial Neural Network Chapter 17. Implementing the Artificial Neural Network Chapter 18. Deep Learning and Convolutional Neural Network Chapter 19. Implementing Convolutional Neural Network Chapter 20. Recurrent Neural Network Chapter 21. Implementing Recurrent Neural Network Chapter 22. Genetic Algorithm for Machine Learning
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