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Google Cloud Certified: Professional Machine Learning Engineer Study Guide "Google Cloud Certified: Professional Machine Learning Engineer Study Guide" serves as a comprehensive resource to prepare for the Professional Machine Learning Engineer certification exam. Through twelve chapters covering various aspects of machine learning fundamentals, Google Cloud Platform, data preparation and feature engineering, model building and training, model evaluation and validation, model deployment, distributed training and scalability, ML pipelines and workflow automation, ML operations and monitoring, MLOps and DevOps for ML, and exam preparation strategies, readers will gain the knowledge and skills required to excel in their role as a Machine Learning Engineer. By following the guide and mastering the topics, readers will be well-equipped to pass the certification exam and demonstrate their expertise in machine learning on the Google Cloud Platform. The book covers the following: Chapter 1: Introduction to Google Cloud Certified: Professional Machine Learning Engineer Certification Overview of the Google Cloud Certified: Professional Machine Learning Engineer certification. Understanding the role and responsibilities of a Machine Learning Engineer. Examining the GCP Professional Machine Learning Engineer Certification exam structure, format, and scoring. Navigating the GCP Professional Machine Learning Engineer certification journey. Developing a study plan and effective GCP Professional Machine Learning Engineer Certification exam preparation strategies. Chapter 2: Machine Learning Fundamentals Understanding the basics of machine learning and its applications. Exploring supervised, unsupervised, and reinforcement learning. Evaluating performance metrics and model evaluation techniques. Understanding feature engineering and selection. Handling data preprocessing and cleaning for Machine Learning.<br