Building Machine Learning and Deep Learning Models on Google Cloud Platform - by Ekaba Bisong (Paperback)

About the Book

Book Synopsis

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

From the Back Cover

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 divided into 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.

You will:

• 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
  
• Productionalizemachine learning models into software products
  

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.