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  • Book Overview & Buying Journey to Become a Google Cloud Machine Learning Engineer
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Journey to Become a Google Cloud Machine Learning Engineer

Journey to Become a Google Cloud Machine Learning Engineer

By : Dr. Logan Song
5 (61)
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Journey to Become a Google Cloud Machine Learning Engineer

Journey to Become a Google Cloud Machine Learning Engineer

5 (61)
By: Dr. Logan Song

Overview of this book

This book aims to provide a study guide to learn and master machine learning in Google Cloud: to build a broad and strong knowledge base, train hands-on skills, and get certified as a Google Cloud Machine Learning Engineer. The book is for someone who has the basic Google Cloud Platform (GCP) knowledge and skills, and basic Python programming skills, and wants to learn machine learning in GCP to take their next step toward becoming a Google Cloud Certified Machine Learning professional. The book starts by laying the foundations of Google Cloud Platform and Python programming, followed the by building blocks of machine learning, then focusing on machine learning in Google Cloud, and finally ends the studying for the Google Cloud Machine Learning certification by integrating all the knowledge and skills together. The book is based on the graduate courses the author has been teaching at the University of Texas at Dallas. When going through the chapters, the reader is expected to study the concepts, complete the exercises, understand and practice the labs in the appendices, and study each exam question thoroughly. Then, at the end of the learning journey, you can expect to harvest the knowledge, skills, and a certificate.
Table of Contents (23 chapters)
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1
Part 1: Starting with GCP and Python
4
Part 2: Introducing Machine Learning
8
Part 3: Mastering ML in GCP
13
Part 4: Accomplishing GCP ML Certification
15
Part 5: Appendices
2
Appendix 2: Practicing Using the Python Data Libraries

Validating the model

After you train your model, you will need to determine whether it will perform well in predicting the target on future new data, and that is the validation process: you must validate the model performance on a labeled dataset that was not used in training – the validation dataset that was built during the dataset splitting phase.

Model validation

Recall what we have discussed in Chapter 3: in the ML problem framing phase, you define the business problem and craft a business metric to measure model success. Now, in this model validation phase, the model validation metric needs to be linked to that business metric as closely as possible.

Earlier in this chapter, we have defined the cost function, which is used to find the optimized model. The cost function is also used for ML model validation. For regression problems, the cost function (the gap between the model value and the actual value) is usually the MSE, which was discussed in the previous...

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