Overview
- Pedagogically structured to make the knowledge of machine learning, deep learning, data science, and cloud computing easily accessible
- Equips you with skills to build and deploy large-scale learning models on Google Cloud Platform
- Covers the programming skills necessary for machine learning and deep learning modeling using the Python stack
- Includes packages such as Numpy, Pandas, Matplotlib, Scikit-learn, Tensorflow, and Keras
Access this book
Tax calculation will be finalised at checkout
Other ways to access
Table of contents (47 chapters)
-
Front Matter
-
Getting Started with Google Cloud Platform
-
Front Matter
-
-
Programming Foundations for Data Science
-
Front Matter
-
-
Introducing Machine Learning
-
Front Matter
-
Keywords
About this book
Take a systematic approach to understanding the fundamentals of machine learning and deep learning from the ground up and how they are applied in practice. You will use this comprehensive guide for building and deploying learning models to address complex use cases while leveraging the computational resources of Google Cloud Platform.
Author Ekaba Bisong shows you how machine learning tools and techniques are used to predict or classify events based on a set of interactions between variables known as features or attributes in a particular dataset. He teaches you how deep learning extends the machine learning algorithm of neural networks to learn complex tasks that are difficult for computers to perform, such as recognizing faces and understanding languages. And you will know how to leverage cloud computing to accelerate data science and machine learning deployments.
Building Machine Learning and Deep Learning Models on Google Cloud Platform is dividedinto eight parts that cover the fundamentals of machine learning and deep learning, the concept of data science and cloud services, programming for data science using the Python stack, Google Cloud Platform (GCP) infrastructure and products, advanced analytics on GCP, and deploying end-to-end machine learning solution pipelines on GCP.
What You’ll Learn
- Understand the principles and fundamentals of machine learning and deep learning, the algorithms, how to use them, when to use them, and how to interpret your results
- Know the programming concepts relevant to machine and deep learning design and development using the Python stack
- Build and interpret machine and deep learning models
- Use Google Cloud Platform tools and services to develop and deploy large-scale machine learning and deep learning products
- Be aware of the different facets and design choices to consider when modeling a learning problem
- Productionalize machine learning models into software products
Who This Book Is For
Beginners to the practice of data science and applied machine learning, data scientists at all levels, machine learning engineers, Google Cloud Platform data engineers/architects, and software developers
Authors and Affiliations
About the author
Ekaba Bisong is a Data Science Lead at T4G. He previously worked as a data scientist/data engineer at Pythian. In addition, he maintains a relationship with the Intelligent Systems Labs at Carleton University with a research focus on learning systems (encompassing learning automata and reinforcement learning), machine learning, and deep learning. He is a Google Certified Professional Data Engineer and a Google Developer Expert in machine learning.
Bibliographic Information
Book Title: Building Machine Learning and Deep Learning Models on Google Cloud Platform
Book Subtitle: A Comprehensive Guide for Beginners
Authors: Ekaba Bisong
DOI: https://doi.org/10.1007/978-1-4842-4470-8
Publisher: Apress Berkeley, CA
eBook Packages: Professional and Applied Computing, Apress Access Books, Professional and Applied Computing (R0)
Copyright Information: Ekaba Bisong 2019
Softcover ISBN: 978-1-4842-4469-2Published: 28 September 2019
eBook ISBN: 978-1-4842-4470-8Published: 27 September 2019
Edition Number: 1
Number of Pages: XXIX, 709
Number of Illustrations: 4 b/w illustrations, 344 illustrations in colour
Topics: Artificial Intelligence, Big Data