1.1 Introduction to Data Science
1.2 Artificial Intelligence Foundations
1.3 Generative AI Revolution
2.1 Advanced Python for Data Science
2.2 Data Structures and Algorithms
3.1 NumPy for Numerical Computing
3.2 Pandas for Data Analysis
4.1 Statistical Visualizations
4.2 Power BI for Business Intelligence
4.3 Dashboard Development
5.1 Hardware for AI/ML
5.2 Cloud Platforms
5.3 Development Environments
6.1 Matrix Operations
6.2 Applications in ML
7.1 Differential Calculus
7.2 Optimization Algorithms
8.1 Probability Theory
8.2 Statistical Inference
9.1 Hypothesis Testing
9.2 Advanced Statistical Methods
10.1 EDA Methodology
10.2 Feature Engineering
11.1 ML Pipeline Development
11.2 Model Selection & Validation
12.1 Classification Algorithms
12.2 Regression Analysis
13.1 Clustering Algorithms
13.2 Dimensionality Reduction
14.1 Neural Network Architecture
14.2 Training Deep Networks
15.1 Convolutional Neural Networks
15.2 Recurrent Networks & Transformers
16.1 Text Preprocessing
16.2 Feature Extraction
17.1 Sequence Models
17.2 Pre-trained Language Models
18.1 LLM Fundamentals
18.2 Fine-tuning & Adaptation
19.1 Prompt Engineering Mastery
19.2 LLM Application Development
20.1 RAG Architecture
20.2 Advanced RAG Techniques
21.1 Generative Model Foundations
21.2 Diffusion Models
22.1 Text-to-Image Models
22.2 Advanced Image AI
23.1 Vision-Language Models
23.2 Cross-Modal Applications
24.1 Speech Processing
24.2 Audio Generation
25.1 Code Generation Models
25.2 Domain-Specific Applications
26.1 Agent Architecture
26.2 Agent Frameworks
27.1 Agent Design Patterns
27.2 Multi-Agent Systems
28.1 MLOps Pipeline
28.2 Model Management
29.1 Model Serving
29.2 Cloud Deployment
30.1 Production Monitoring
30.2 System Optimization
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