Book Image

Hands-On Machine Learning on Google Cloud Platform

By : Giuseppe Ciaburro, V Kishore Ayyadevara, Alexis Perrier
Book Image

Hands-On Machine Learning on Google Cloud Platform

By: Giuseppe Ciaburro, V Kishore Ayyadevara, Alexis Perrier

Overview of this book

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.
Table of Contents (18 chapters)
Creating ML Applications with Firebase

Supervised and unsupervised machine learning

Supervised machine learning constitutes the set of techniques that work towards building a model that approximate a function. The function takes a set of input variables, which are alternatively called independent variables, and tries to map the input variables to the output variable, alternatively called the dependent variable or the label.

Given that we know the label (or the value) we are trying to predict, for a set of input variables, the technique becomes a supervised learning problem.

In a similar manner, in an unsupervised learning problem, we do not have the output variable that we have to predict. However, in unsupervised learning, we try to group the data points so that they form logical groups.

A distinction between supervised and unsupervised learning at a high level can be obtained as shown in the following diagram: