Module 1: Advanced Python and Statistics

Learning Objectives

• Master advanced Python programming
• Understand statistical foundations
• Apply statistical concepts

Topics Covered

• Advanced Python programming
• Object-oriented programming
• Statistical distributions
• Probability theory
• Bayesian statistics
• Statistical testing

Module 2: Machine Learning Mastery

Learning Objectives

• Master ML algorithms
• Optimize model performance
• Handle complex datasets

Topics Covered

• Supervised learning algorithms
• Unsupervised learning
• Ensemble methods
• Feature engineering
• Model selection
• Hyperparameter tuning

Module 1: Advanced Python and Statistics

Learning Objectives

• Master advanced Python programming
• Understand statistical foundations
• Apply statistical concepts

Topics Covered

• Advanced Python programming
• Object-oriented programming
• Statistical distributions
• Probability theory
• Bayesian statistics
• Statistical testing

Module 2: Machine Learning Mastery

Learning Objectives

• Master ML algorithms
• Optimize model performance
• Handle complex datasets

Topics Covered

• Supervised learning algorithms
• Unsupervised learning
• Ensemble methods
• Feature engineering
• Model selection
• Hyperparameter tuning

Module 3: Deep Learning and Neural Networks

Learning Objectives

• Build neural networks
• Apply deep learning to real problems
• Deploy AI models

Topics Covered

• Neural network fundamentals
• Deep learning frameworks
• CNN for image processing
• RNN for sequence data
• Transfer learning
• Model deployment

Module 4: MLOps and Production Systems

Learning Objectives

• Deploy production ML systems
• Monitor model performance
• Scale ML solutions

Topics Covered

• Model versioning
• CI/CD for ML
• Model monitoring
• A/B testing for ML
• Cloud deployment
• Scalability considerations

Module 9: Research and Innovation

Learning Objectives

• Conduct data science research
• Publish research papers
• Contribute to open source

Topics Covered

• Research methodologies
• Paper writing and publication
• Open source contribution
• Innovation frameworks
• Technology trends
• Research presentations