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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
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Appendix 2: Practicing Using the Python Data Libraries

In Chapter 2, Mastering Python Programming, we covered the Python data libraries, including NumPy, Pandas, Matpotlib, and Seaborn. In this appendix, we will continue learning these libraries by practicing using them on the Google Colab platform (colab.research.google.com).

With a step-by-step approach, we will show how to use these libraries to manage and visualize data. For the NumPy library, we will discuss how to generate and operate NumPy arrays. For the Pandas library, we cover features including Series, DataFrames, missing data handling, GroupBy, and operations. For the Matpotlib and Seaborn libraries, we will show their features by exploring multiple data visualization examples.

Follow these examples and make sure you understand each of them. Practicing each example on Google Colab will yield the best results.

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Journey to Become a Google Cloud Machine Learning Engineer
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