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Journey to Become a Google Cloud Machine Learning Engineer

You're reading from   Journey to Become a Google Cloud Machine Learning Engineer Build the mind and hand of a Google Certified ML professional

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Product type Paperback
Published in Sep 2022
Publisher Packt
ISBN-13 9781803233727
Length 330 pages
Edition 1st Edition
Languages
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Author (1):
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Dr. Logan Song Dr. Logan Song
Author Profile Icon Dr. Logan Song
Dr. Logan Song
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Toc

Table of Contents (23) Chapters Close

Preface 1. Part 1: Starting with GCP and Python
2. Chapter 1: Comprehending Google Cloud Services FREE CHAPTER 3. Chapter 2: Mastering Python Programming 4. Part 2: Introducing Machine Learning
5. Chapter 3: Preparing for ML Development 6. Chapter 4: Developing and Deploying ML Models 7. Chapter 5: Understanding Neural Networks and Deep Learning 8. Part 3: Mastering ML in GCP
9. Chapter 6: Learning BQ/BQML, TensorFlow, and Keras 10. Chapter 7: Exploring Google Cloud Vertex AI 11. Chapter 8: Discovering Google Cloud ML API 12. Chapter 9: Using Google Cloud ML Best Practices 13. Part 4: Accomplishing GCP ML Certification
14. Chapter 10: Achieving the GCP ML Certification 15. Part 5: Appendices
16. Index 17. Other Books You May Enjoy Appendix 1: Practicing with Basic GCP Services 1. Appendix 2: Practicing Using the Python Data Libraries 2. Appendix 3: Practicing with Scikit-Learn 3. Appendix 4: Practicing with Google Vertex AI 4. Appendix 5: Practicing with Google Cloud ML API

What this book covers

Chapter 1, Comprehending Google Cloud Services, provides an overview of the GCP services with the practice examples detailed in Appendix 1.

Chapter 2, Learning Python Programming, delves into the Python basic knowledge and programming skills. The Python data science libraries are explored, with practice examples detailed in Appendix 2.

Chapter 3, Preparing for ML Development, covers preparations for the ML process, including ML problem definition and data preparation.

Chapter 4, Developing and Deploying ML Models, dives into the ML process, including platform preparation, dataset splitting, model training, validation, testing, and deployment with the practice examples detailed in Appendix 3.

Chapter 5, Understanding Neural Networks and Deep Learning, introduces the modern AI methods of deep learning with neural network modeling.

Chapter 6, Learning BQML, TensorFlow, and Keras, discover Google’s BigQuery machine learning for structured data, and Google’s ML framework of TensorFlow and Keras.

Chapter 7, Exploring Google Cloud Vertex AI, examines Google’s end-to-end ML suite of Vertex AI and its ML services with the practice examples detailed in Appendix 4.

Chapter 8, Discovering Google Cloud ML API, looks at how you can leverage Google’s pre-trained model APIs for ML development with the practice examples detailed in Appendix 5.

Chapter 9, Using Google Cloud ML Best Practices, summarizes the best practices in ML development in Google Cloud.

Chapter 10, Achieving the GCP ML Certification, studies GCP ML certification exam questions by integrating the knowledge and skills learned from previous chapters.

Appendix 1, Practicing with Basic GCP Services, provides examples for provisioning basic GCP services.

Appendix 2, Practicing with Python Data Library, provides examples of Python data library practices, including NumPy, Pandas, Matpotlib, and Seaborn.

Appendix 3, Practicing with ScikitLearn, provides examples of scikit-learn library practices.

Appendix 4, Practicing with Google Vertex AI, provides examples for practicing Google Cloud Vertex AI services.

Appendix 5, Practicing with Google Cloud ML API, provides examples for Google Cloud ML API practicing.

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