Overview
From Scratch, Learn testing types and Strategies involved in all the phases of ML Models (AI) with real time examples
Syllabus
-
- Introduction to Machine Learning Testing
-- Overview of Machine Learning and AI
-- Importance of Testing in Machine Learning
- Types of Machine Learning Models
-- Supervised Learning Models
-- Unsupervised Learning Models
-- Reinforcement Learning Models
- Basics of Model Testing
-- Test Data vs. Training Data
-- Cross-Validation Techniques
-- Evaluation Metrics (Accuracy, Precision, Recall, F1 Score)
- Techniques for Model Validation
-- Understanding Overfitting and Underfitting
-- Bias-Variance Tradeoff
-- K-Fold Cross-Validation
- Testing Frameworks and Tools
-- Introduction to Popular Testing Frameworks (e.g., PyTest, UnitTest for Python)
-- Specific Tools for Machine Learning Testing (e.g., MLflow, TensorFlow Model Analysis)
- Performance Testing
-- Latency and Throughput
-- Scaling Machine Learning Models
- Security and Bias Testing
-- Testing for Model Bias
-- Security Concerns and Adversarial Testing in AI
- Continuous Integration and Deployment in ML
-- Implementing CI/CD Pipelines for Machine Learning
-- Automation in Model Testing and Deployment
- Case Studies and Practical Applications
-- Real-World Examples of Model Testing
-- Hands-On Projects and Exercises
- Future Trends in AI Model Testing
-- Innovations in Testing Methodologies
-- Evolution of AI Testing with New Technologies
- Conclusion and Review
-- Recap of Key Concepts
-- Preparing for Next Steps in Machine Learning Testing
Taught by
Tags
On-Demand
