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Aws certificate developer associate course
Module 1:Data Engineering
    • 1.1 Collection and storage of data
       
    • 1.2 Data preprocessing and cleaning
       
    • 1.3 Data transformation and feature engineering
       
    • 1.4 Data integration and data quality
       
Module 2 :Exploratory Data Analysis
    • 2.1 Descriptive statistics
       
    • 2.2 Data visualization techniques
       
    • 2.3 Identifying patterns and trends
       
    • 2.4 Data summarization
       
Module 3:Modeling
    • 3.1 Supervised, unsupervised, and reinforcement learning
       
    • 3.2 Model selection and evaluation
       
    • 3.3 Hyperparameter tuning
       
    • 3.4 Training and deploying machine learning models
       
Module 4:Machine Learning Implementatio and Operations
    • 4.1 Building ML pipelines
       
    • 4.2 Model deployment and monitoring
       
    • 4.3 Managing and maintaining ML models
       
    • 4.4 A/B testing and experimentation
       
Module 5:Machine Learning for Security and Compliance
    • 5.1 Data privacy and protection
       
    • 5.2 Security best practices in ML
       
    • 5.3 Compliance with regulations (e.g., GDPR)
       
    • 5.4 Ethical considerations in AI/ML
       
Module 6: End-to-End ML Lifecycle
    • 6.1 Problem formulation and scoping
       
    • 6.2 Data gathering and labeling
       
    • 6.3 Model development and evaluation
       
    • 6.4 Model deployment and maintenance
       
    • 6.5 Model retirement and retraining
       
module 7:AWS Application Integration
    • 7.1 Overview of AWS ML services
       
    • 7.2 Using AWS tools for ML tasks
       
module 8:Specialized ML Topics
    • 8.1 Time series analysis
       
    • 8.2 Natural language processing (NLP)
       
    • 8.3 Computer vision
       
    • 8.4 Reinforcement learning
       
    • 8.5 Anomaly detection
       
module 9:AWS ML Best Practices and Design Patterns
    • 9.1 Architectural best practices
       
    • 9.2 Scalability and reliability considerations
       
    • 9.3 Cost optimization for ML workloads
       
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