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GIAC GMLE Exam Syllabus Topics:

Section	Objectives
Topic 1: Machine Learning Foundations	- Mathematical and statistical fundamentals 1. Linear algebra and probability basics 2. Statistical inference concepts - Core ML concepts 1. Supervised vs unsupervised learning 2. Bias-variance tradeoff
Topic 2: Machine Learning Engineering and Deployment	- ML pipelines 1. Training and validation workflows 2. Automation of ML workflows - Deployment considerations 1. Model serving 2. Monitoring and maintenance
Topic 3: Data Preparation and Feature Engineering	- Feature engineering 1. Feature selection techniques 2. Encoding categorical variables - Data preprocessing 1. Handling missing data 2. Data cleaning and normalization
Topic 4: Machine Learning Models	- Supervised learning models 1. Classification models 2. Regression models - Unsupervised learning 1. Clustering algorithms 2. Dimensionality reduction
Topic 5: Model Evaluation and Optimization	- Model tuning 1. Hyperparameter optimization 2. Cross-validation techniques - Evaluation metrics 1. ROC-AUC and confusion matrix 2. Accuracy, precision, recall, F1-score
Topic 6: Advanced Topics	- MLOps concepts 1. Model governance and reproducibility 2. CI/CD for ML models - Deep learning basics 1. Common architectures overview 2. Neural network fundamentals