Machine Learning Theory and Applications: Hands-on Use Cases with Python on Classical and Quantum Machines

Enables readers to understand mathematical concepts behind data engineering and machine learning algorithms and apply them using open-source Python libraries.

Machine Learning Theory and Applications delves into the realm of machine learning and deep learning, exploring their practical applications by comprehending mathematical concepts and implementing them in real-world scenarios using Python and renowned open-source libraries. This comprehensive guide covers a wide range of topics, including data preparation, feature engineering techniques, commonly utilized machine learning algorithms like support vector machines and neural networks, as well as generative AI and foundation models. To facilitate the creation of machine learning pipelines, a dedicated open-source framework named hephAIstos has been developed exclusively for this book.