1. Getting Started with Amazon SageMaker Studio
Authored by Michael Hsieh, this book is a comprehensive guide to navigating the complexities of machine learning utilizing Amazon SageMaker Studio. It offers readers invaluable insights on how to build end-to-end machine learning projects through a detailed, hands-on approach. Whether you are a novice looking to break into the world of AI or an experienced developer seeking to enhance your skills, this book is a must-read. Hsieh breaks down concepts and teaches you how to effectively work within the SageMaker IDE, ensuring you gain practical experience alongside foundational knowledge. The publication date is April 11, 2022, making this guide current and relevant in the fast-evolving tech landscape. Don’t miss the chance to explore this essential resource.
2. No-code AI: Concepts And Applications In Machine Learning
This groundbreaking book is co-authored by Minsoo Kang, Park Sung Yul, Myeong-ae Chung, and Dong-hun Han, scheduled for publication on July 22, 2024. ‘No-code AI’ revolutionizes the way we look at machine learning by offering concepts and applications that require no programming expertise. This is perfect for business professionals and data enthusiasts who may lack coding skills but are eager to leverage AI’s potential. The authors delineate clear, actionable insights on utilizing various platforms effectively to visualize and apply machine learning techniques without writing a single line of code. This book is essential for anyone looking to understand and apply AI without the typical barriers of coding knowledge.
3. Python Cloud-Based Machine Learning Model Tricks
For those looking for budget-friendly options, this Japanese edition by an anonymous author is priced at only $2.99 and is set to be published on April 30, 2024. It showcases training and deploying scalable AI models using Google Colab and AWS SageMaker. This book is an excellent resource for beginners, giving readers easy access to powerful machine learning tools without the need for extensive hardware. With practical examples and tutorials, it provides a guided path to learning how to create cloud-based models that can easily be scaled up. This is a perfect starter book that promises rich learning at an unbeatable price!
