Alexis Perrier

Unleash Google's Cloud Platform to build, train and optimize machine learning models About This Book • Get well versed in GCP pre-existing services to build your own smart models • A comprehensive guide covering aspects from data processing, analyzing to building and training ML models • A practical approach to produce your trained ML models and port them to your mobile for easy access Who This Book Is For This book is for data scientists, machine learning developers and AI developers who want to learn Google Cloud Platform services to build machine learning applications. Since the interaction with the Google ML platform is mostly done via the command line, the reader is supposed to have some familiarity with the bash shell and Python scripting. Some understanding of machine learning and data science concepts will be handy What You Will Learn • Use Google Cloud Platform to build data-based applications for dashboards, web, and mobile • Create, train and optimize deep learning models for various data science problems on big data • Learn how to leverage BigQuery to explore big datasets • Use Google's pre-trained TensorFlow models for NLP, image, video and much more • Create models and architectures for Time series, Reinforcement Learning, and generative models • Create, evaluate, and optimize TensorFlow and Keras models for a wide range of applications In Detail Google Cloud Machine Learning Engine combines the services of Google Cloud Platform with the power and flexibility of TensorFlow. With this book, you will not only learn to build and train different complexities of machine learning models at scale but also host them in the cloud to make predictions. This book is focused on making the most of the Google Machine Learning Platform for large datasets and complex problems. You will learn from scratch how to create powerful machine learning based applications for a wide variety of problems by leveraging different data services from the Google Cloud Platform. Applications include NLP, Speech to text, Reinforcement learning, Time series, recommender systems, image classification, video content inference and many other. We will implement a wide variety of deep learning use cases and also make extensive use of data related services comprising the Google Cloud Platform ecosystem such as Firebase, Storage APIs, Datalab and so forth. This will enable you to integrate Machine Learning and data processing features into your web and mobile applications. By the end of this book, you will know the main difficulties that you may encounter and get appropriate strategies to overcome these difficulties and build efficient systems. Style and approach An easy-to-follow step by step guide which will help you get to the grips with real-world applications of Google Cloud Machine Learning.

Learn to leverage Amazon's powerful platform for your predictive analytics needs About This Book • Create great machine learning models that combine the power of algorithms with interactive tools without worrying about the underlying complexity • Learn the What's next? of machine learning—machine learning on the cloud—with this unique guide • Create web services that allow you to perform affordable and fast machine learning on the cloud Who This Book Is For This book is intended for data scientists and managers of predictive analytics projects; it will teach beginner- to advanced-level machine learning practitioners how to leverage Amazon Machine Learning and complement their existing Data Science toolbox. No substantive prior knowledge of Machine Learning, Data Science, statistics, or coding is required. What You Will Learn • Learn how to use the Amazon Machine Learning service from scratch for predictive analytics • Gain hands-on experience of key Data Science concepts • Solve classic regression and classification problems • Run projects programmatically via the command line and the Python SDK • Leverage the Amazon Web Service ecosystem to access extended data sources • Implement streaming and advanced projects In Detail Predictive analytics is a complex domain requiring coding skills, an understanding of the mathematical concepts underpinning machine learning algorithms, and the ability to create compelling data visualizations. Following AWS simplifying Machine learning, this book will help you bring predictive analytics projects to fruition in three easy steps: data preparation, model tuning, and model selection. This book will introduce you to the Amazon Machine Learning platform and will implement core data science concepts such as classification, regression, regularization, overfitting, model selection, and evaluation. Furthermore, you will learn to leverage the Amazon Web Service (AWS) ecosystem for extended access to data sources, implement realtime predictions, and run Amazon Machine Learning projects via the command line and the Python SDK. Towards the end of the book, you will also learn how to apply these services to other problems, such as text mining, and to more complex datasets. Style and approach This book will include use cases you can relate to. In a very practical manner, you will explore the various capabilities of Amazon Machine Learning services, allowing you to implementing them in your environment with consummate ease.