Machine Learning on Google Cloud Specialization Course Syllabus

Overview: This specialization is designed for learners aiming to master machine learning on Google Cloud Platform. Through a series of hands-on labs and practical exercises, you'll gain experience building, training, and deploying machine learning models using Vertex AI, BigQuery ML, TensorFlow, and Keras. The course spans approximately 46 hours of content, allowing flexible pacing. Each module combines theory with Qwiklabs-based practice to reinforce real-world skills in enterprise ML workflows.

Module 1: Introduction to AI and Machine Learning on Google Cloud

Estimated time: 9 hours

• Explore Google's AI and ML ecosystem
• Understand the role of Vertex AI in ML workflows
• Build basic ML models using Vertex AI AutoML
• Compare managed ML services on Google Cloud

Module 2: Build, Train, and Deploy ML Models with Keras on Google Cloud

Estimated time: 13 hours

• Design input data pipelines with TensorFlow
• Implement custom ML models using Keras and TensorFlow 2.x
• Train models at scale on Google Cloud
• Deploy trained models using Vertex AI

Module 3: Feature Engineering

Estimated time: 8 hours

• Perform feature engineering with BigQuery ML
• Use Dataflow for scalable data transformation
• Enhance features using Dataprep
• Optimize input data for model performance

Module 4: Machine Learning in the Enterprise

Estimated time: 8 hours

• Apply best practices for enterprise ML
• Implement MLOps principles using core GCP technologies
• Design reliable training and inference workflows
• Monitor and maintain production ML systems

Module 5: Exploratory Data Analysis and Data Quality

Estimated time: 8 hours

• Conduct exploratory data analysis (EDA) on large datasets
• Improve data quality for ML projects
• Use BigQuery for data preprocessing
• Identify and handle missing or biased data

Module 6: Final Project

Estimated time: 10 hours

• Build an end-to-end ML solution using Vertex AI
• Apply feature engineering and model training techniques
• Deploy a scalable model on Google Cloud

Prerequisites

• Familiarity with Python programming
• Basic understanding of machine learning fundamentals
• Experience with data analysis concepts

What You'll Be Able to Do After

• Build and deploy ML models using Vertex AI AutoML and BigQuery ML
• Implement custom models with TensorFlow and Keras
• Apply feature engineering and data preprocessing techniques
• Design and manage ML workflows in enterprise environments
• Earn a certificate to validate your Google Cloud ML skills