Module 1: Machine Learning Foundations

• ML problem framing
• Supervised vs unsupervised learning
• Feature engineering concepts
• Model evaluation metrics
• Bias, fairness & ethics in ML

Module 2: Data Engineering for ML

• Data ingestion & preprocessing
• Handling structured & unstructured data
• Data pipelines for ML workloads
• BigQuery for ML data analysis
• Data quality & validation

Module 3: ML Model Development

• Regression, classification & clustering
• Decision trees & ensemble models
• Neural network fundamentals
• Model training & tuning
• Hyperparameter optimization

Module 4: Deep Learning Fundamentals

• Neural networks & backpropagation
• CNNs & RNNs overview
• TensorFlow & Keras basics
• Model optimization techniques

Module 5: Google Cloud ML Services

• Vertex AI overview
• AutoML vs custom training
• Training jobs & notebooks
• Managed datasets & pipelines
• Feature Store usage

Module 6: MLOps on Google Cloud

• ML lifecycle management
• CI/CD for ML pipelines
• Model versioning & tracking
• Model deployment strategies
• Monitoring & retraining models
  

Module 7: Model Deployment & Serving

• Online & batch prediction
• REST APIs for ML models
• Scaling & performance optimization
• Cost optimization strategies

Module 8: Advanced ML & Use Cases

• Recommendation systems
• Time-series forecasting
• NLP & text analytics overview
• Computer vision fundamentals
• Real-world enterprise ML scenarios

Module 9: Security, Governance & Responsible AI

• IAM & access control for ML
• Data privacy & compliance
• Explainable AI concepts
• Responsible AI practices on GCP

Module 10: End-to-End ML Capstone Project

• Problem statement selection
• Data preparation & modeling
• Training & evaluation
• Deployment on Google Cloud
• Project presentation & documentation

Module 11: Google Certification Exam Preparation

• Exam blueprint & objectives
• Scenario-based question practice
• Mock tests & assessments
• Exam strategies & tips