With Natural Language Processing and Recommender Systems
The updated edition focuses on mastering new features in PySpark 3.1 for developing intelligent, data-driven applications. It covers a range of topics, including building scalable machine learning models, implementing natural language processing, and creating recommender systems, providing readers with the tools to enhance their data processing skills.
With Flask, Streamlit, Docker, and Kubernetes on Google Cloud Platform
Focusing on practical applications, this book guides readers through the process of building and deploying machine learning and deep learning models in real-world scenarios. It offers comprehensive end-to-end examples that illustrate key concepts, making it accessible for practitioners looking to implement these technologies effectively in production environments. The content emphasizes hands-on learning, ensuring readers gain the skills needed to navigate the complexities of machine learning workflows.