Description
Introduction of Generative AI Model
Generative AI is transforming the landscape of data analytics and artificial intelligence by enabling the creation of synthetic data, automating insights, and enhancing decision-making processes. This training explores the role of generative AI in data analytics, covering fundamental concepts, model architectures, applications, and best practices. Participants will gain hands-on experience with AI-driven tools and frameworks, including deep learning techniques for generating realistic data and improving AI model performance.
Prerequisites
- Basic understanding of data analytics and machine learning concepts
- Familiarity with Python programming and AI frameworks (e.g., TensorFlow, PyTorch)
- Knowledge of fundamental statistics and data processing techniques
Table of Contents
1. Introduction to Generative AI in Data Analytics
1.1 Understanding Generative AI and Its Role in Data Science
1.2 Comparison of Traditional AI vs. Generative AI Approaches
1.3 Key Technologies: GANs, VAEs, and Transformers
1.4 Ethical Considerations and Bias in Generative AI
2. Generative AI Models and Architectures
2.1 Overview of Generative Adversarial Networks (GANs)
2.2 Variational Autoencoders (VAEs) for Data Synthesis
2.3 Transformer Models: GPT, BERT, and Their Applications
2.4 Diffusion Models: Next-Generation AI for Image and Text Generation
3. Data Augmentation and Synthetic Data Generation
3.1 Enhancing Machine Learning Models with Synthetic Data
3.2 Data Imputation and Anomaly Detection Using AI
3.3 Privacy-Preserving AI and Federated Learning for Data Generation
3.4 Hands-On: Generating Realistic Data for AI Training
4. AI-Powered Data Analytics and Business Insights
4.1 AI-Driven Predictive Analytics and Forecasting
4.2 Automated Insights Generation with Natural Language Processing (NLP)
4.3 AI-Powered Data Cleaning and Transformation Techniques
4.4 Hands-On: Using AI to Automate Data Analysis
5. Natural Language Processing and AI-Generated Content
5.1 Text Summarization and Sentiment Analysis with AI
5.2 Conversational AI and Chatbots in Data-Driven Applications
5.3 Document Generation and AI-Powered Reporting
5.4 Hands-On: Building a Text Analytics Pipeline with AI
6. Deep Learning for Image and Video Analytics
6.1 AI-Generated Image and Video Synthesis
6.2 Computer Vision Applications in Data Analytics
6.3 AI-Powered Object Detection and Recognition
6.4 Hands-On: Implementing AI for Image and Video Analysis
7. Real-World Applications of Generative AI in Data Analytics
7.1 Financial Forecasting and Fraud Detection
7.2 AI in Healthcare: Drug Discovery and Medical Data Generation
7.3 AI-Powered Personalization and Recommendation Systems
7.4 Hands-On: Implementing AI for Real-World Use Cases
8. Future Trends and Challenges in Generative AI
8.1 Advances in AI Model Training and Optimization
8.2 Ethical and Regulatory Challenges in AI-Generated Content
8.3 The Future of AI-Augmented Decision-Making
8.4 Final Project: Developing a Generative AI Model for Data Analytics
Conclusion
Generative AI is revolutionizing data analytics by enabling automation, enhancing data-driven decision-making, and unlocking new possibilities in artificial intelligence. This training provides a strong foundation in generative AI models and techniques, empowering participants to apply AI-driven innovations to their analytics workflows and business strategies.
