Journey to Become a Google Cloud Machine Learning Engineer

Table of Contents

1. Comprehending Google Cloud Services
2. Mastering Python Programming
3. Preparing for ML Development
4. Developing and Deploying ML Models
5. Understanding Neural Networks and Deep Learning
6. Learning BQ/BQML, TensorFlow and Keras
7. Exploring Google Cloud Vertex AI
8. Discovering Google Cloud ML API
9. Using Google Cloud ML Best Practices
10. Achieving the GCP ML Certification
11. Appendix 1 - Practicing with Basic GCP Services
12. Appendix 2 - Practicing with Python Data Library
13. Appendix 3 - Practicing with ScikitLearn
14. Appendix 4 - Practicing with Vertex AI
15. Appendix 5 - Practicing with Google Cloud ML API