This is the code repository for Hands-On Machine Learning on Google Cloud Platform, published by Packt. It contains all the supporting project files necessary to work through the book from start to finish.

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. You will get a practical understanding of deep learning models with their architectures to understand their strengths and weaknesses. Every Deep Learning model is implemented with a relevant dataset and problem to be solved.

All of the code is organized into folders. Each folder starts with a number followed by the application name. For example, Chapter02.

The code will look like the following:

conda install pandas
conda install jupyter
conda install ipython

In this book, machine learning algorithms are implemented on the Google Cloud Platform. To reproduce the many examples in this book, you need to possess a working account on GCP. We have used Python 2.7 and above to build various applications. In that spirit, we have tried to keep all of the code as friendly and readable as possible. We feel that this will enable our readers to easily understand the code and readily use it in different scenarios.

• Hands-on Machine Learning with JavaScript

• Hands-On Machine Learning with C#

• Hands-On Deep Learning with PyTorch